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CMS Projects (2014, 2016, 2017 & 2018 Selections)
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| Baccini (CMS 2015) (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Time-Series Measurements of Biomass Change from InSAR (TanDEM-X), MODIS, and LiDAR Observations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Alessandro (Ale) Baccini, Boston University
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Deforestation and forest degradation of tropical vegetation account for 6 -17% of global annual CO2 emissions to the atmosphere (Van der Werf et al. 2009). International policy mechanisms designed to address emissions from forest loss such as REDD+ require the ability to monitor not only emissions from deforestation but also from forest degradation as well as the uptake by vegetation. While much progress has been made in monitoring changes in forest area and carbon density, measurements of biomass loss due to deforestation and degradation, and increases due to uptake remain challenging. Here we propose to develop a novel methodology to monitor CO2 fluxes to the atmosphere from losses (due to deforestation and degradation) and gains (from vegetation uptake) for the Amazon Basin. The approach is based on a combination of the InSAR TanDEM-x Spaceborne Interferometer, MODIS, LiDAR and field measurements.
The overall objective of the proposed project is to address the research and development required for a multi-sensor, multi-spatial resolution monitoring system integrated with a carbon bookkeeping model to quantify CO2 fluxes to the atmosphere from land carbon dynamics. The specific objectives are to: 1) Quantify the correspondence between TanDEM-X phase height and biomass and derive biomass changes as a function of phase height variations. Using TanDEM-X data taken monthly and at 50-m resolution over Tapajos forest between 2011 and 2016, we will then determine the accuracy with which multi-temporal TanDEM-X observations can be used to measure biomass changes (losses and gains). To do this we will use existing field data and LiDAR measurements collected in the region; 2) assess the within-pixel sensitivity of MODIS derived biomass changes. We will build on Baccini et al. (2012; In Review) and derive annual biomass change estimates. We will then compare with high resolution change estimates from TanDEM-X and assess the sensitivity of MODIS to sub-pixel changes in biomass; 3) address the research and development required to combine InSAR spaceborne observations with MODIS reflectance. By combining time series of InSAR and MODIS observations we expect to increase the sensitivity in biomass change while expanding our monitoring capability over larger area; 4) assess the impact of differing resolutions and accuracies in biomass change estimates when products from objectives (1) and (3) are used in a bookkeeping model to derive CO2 fluxes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Alessandro (Ale) Baccini, Boston University | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla-Menashe, D., Houghton, R. A. 2017. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science. 358(6360), 230-234. DOI: 10.1126/science.aam5962 Treuhaft, R., Lei, Y., Goncalves, F., Keller, M., Santos, J., Neumann, M., Almeida, A. 2017. Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry. Forests. 8(8), 277. DOI: 10.3390/f8080277 Walker, W. S., Gorelik, S. R., Baccini, A., Aragon-Osejo, J. L., Josse, C., Meyer, C., Macedo, M. N., Augusto, C., Rios, S., Katan, T., de Souza, A. A., Cuellar, S., Llanos, A., Zager, I., Mirabal, G. D., Solvik, K. K., Farina, M. K., Moutinho, P., Schwartzman, S. 2020. The role of forest conversion, degradation, and disturbance in the carbon dynamics of Amazon indigenous territories and protected areas. Proceedings of the National Academy of Sciences. 117(6), 3015-3025. DOI: 10.1073/pnas.1913321117 Wang, J. A., Baccini, A., Farina, M., Randerson, J. T., Friedl, M. A. 2021. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nature Climate Change. 11(5), 435-441. DOI: 10.1038/s41558-021-01027-4 Wigneron, J., Fan, L., Ciais, P., Bastos, A., Brandt, M., Chave, J., Saatchi, S., Baccini, A., Fensholt, R. 2020. Tropical forests did not recover from the strong 2015-2016 El Nino event. Science Advances. 6(6). DOI: 10.1126/sciadv.aay4603 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Baccini, A., W. Walker, L.E. Carvalho, M.K. Farina, K.K. Solvik, and D. Sulla-Menashe. 2021. Aboveground Biomass Change for Amazon Basin, Mexico, and Pantropical Belt, 2003-2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1824
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| Bowman (CMS 2016) (2017) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | A decadal carbon reanalysis from the NASA Carbon Monitoring System Flux (CMS-Flux) project | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Kevin Bowman, JPL
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Bowman (CMS 2014) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Bowman (CMS 2022) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Dramatic increases in atmospheric CO2 from preindustrial to present day are the primary driver of climate change. The spatial origin of the CO2 growth rate and its variability is a complex function of anthropogenic, terrestrial, and oceanic processes. While well constrained at global scales, the tilt of anthropogenic emissions towards developing countries has increased regional uncertainty at levels that rival natural variability. Patterns of climate variability, including the 2010 and 2015 El Ninos, directly affect the airborne fraction through spatially complex land carbon processes such as fires, gross primary productivity (GPP), and respiration while modulating atmosphere-ocean pCO2 exchange across entire ocean basins. Changes in the frequency and intensity of climate anomalies may alter the trajectory of carbon storage and fluxes leading to carbon-climate feedbacks. The combination of regional emissions and natural fluxes complicates the attribution of expected changes in atmospheric CO2 to carbon mitigation strategies such as those proposed by Paris Climate Accord.
In order to improve this attribution, we propose a decadal carbon reanalysis from 2010- 2019 that will build upon, extend, and improve products developed under the NASA CMS-Flux, which was initiated during the first phase of the CMS pilot studies. These products will include observationally-constrained and spatially-explicit bottom-up' estimates of anthropogenic, oceanic, and terrestrial carbon fluxes and uncertainties, which are a continuation of anthropogenic emissions from the Fossil Fuel Assimilation System (FFDAS) and assimilated oceanic pCO2 fluxes from ECCO-Darwin. The terrestrial ecosystem fluxes will be derived from the C data model framework (CARDAMOM) assimilation system, which will ingest satellite-constrained biomass and productivity measures including solar induced fluorescence from GOSAT and OCO-2. Atmospheric observations of CO2 from GOSAT and OCO-2 along with CO from MOPITT will be assimilated into the CMS-Flux framework to produce spatially-resolved 'top-down' estimates of total and fire fluxes, respectively.
To achieve decadal scale fluxes, we will link two state-of-the-art data assimilation approaches: ensemble Kalman filtering and 4D-variational methodologies. In order to provide improvements in the characterization of CMS-Flux products and uncertainties, an innovative optimal reduced flux basis technique will be used to calculate critical diagnostics such as degrees of freedom and posterior uncertainty correlations. Inferred fluxes will be evaluated against atmospheric observations based upon a new method introduced in Liu et al 2016 that attributes model-data concentration differences to regional fluxes. Bottom-up estimates will be evaluated against independent data where available such as FLUXCOM.
Taken together, the proposed extension of CMS-Flux will be one of the most advanced carbon cycle data assimilation systems available covering a decade that includes the 2nd largest El Nino on record, the 5 highest global temperatures since the 19th century, and a global agreement to curb carbon emissions. Products from CMS-Flux will fill an important need of the carbon community to relate changes in atmospheric CO2 growth rate to regional anthropogenic, land, and oceanic drivers. These in turn provide the broad carbon context to understand the efficacy of carbon mitigation strategies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Alexis (Anthony) Bloom, Jet Propulsion Lab, California Institute of Technology | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): |
https://cmsflux.jpl.nasa.gov/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Barkhordarian, A., Bowman, K. W., Cressie, N., Jewell, J., Liu, J. 2021. Emergent constraints on tropical atmospheric aridity--carbon feedbacks and the future of carbon sequestration. Environmental Research Letters. 16(11), 114008. DOI: 10.1088/1748-9326/ac2ce8 Bloom, A. A., Bowman, K. W., Liu, J., Konings, A. G., Worden, J. R., Parazoo, N. C., Meyer, V., Reager, J. T., Worden, H. M., Jiang, Z., Quetin, G. R., Smallman, T. L., Exbrayat, J., Yin, Y., Saatchi, S. S., Williams, M., Schimel, D. S. 2020. Lagged effects regulate the inter-annual variability of the tropical carbon balance. Biogeosciences. 17(24), 6393-6422. DOI: 10.5194/bg-17-6393-2020 Butler, M. P., Lauvaux, T., Feng, S., Liu, J., Bowman, K. W., Davis, K. J. 2020. Atmospheric Simulations of Total Column CO2 Mole Fractions from Global to Mesoscale within the Carbon Monitoring System Flux Inversion Framework. Atmosphere. 11(8), 787. DOI: 10.3390/atmos11080787 Byrne B, Liu J, Lee M, Baker I, Bowman K W, Deutscher N M, Feist D G, Griffith D W T, Iraci L T, Kiel M, Kimball J S, Miller C E, Morino I, Parazoo N C, Petri C, Roehl C M, Sha M K, Strong K, Velazco V A, Wennberg P O, Wunch D. 2020 Improved Constraints on Northern Extratropical CO2 Fluxes Obtained by Combining Surface-Based and Space-Based Atmospheric CO2 Measurements. Journal of Geophysical Research: Atmospheres. 125(15). DOI: 10.1029/2019JD032029 Carroll, D., Menemenlis, D., Adkins, J. F., Bowman, K. W., Brix, H., Dutkiewicz, S., Fenty, I., Gierach, M. M., Hill, C., Jahn, O., Landschutzer, P., Lauderdale, J. M., Liu, J., Manizza, M., Naviaux, J. D., Rodenbeck, C., Schimel, D. S., Van der Stocken, T., Zhang, H. 2020. The ECCO-Darwin Data-Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean p CO 2 and Air-Sea CO 2 Flux. Journal of Advances in Modeling Earth Systems. 12(10). DOI: 10.1029/2019MS001888 Carroll, D., Menemenlis, D., Dutkiewicz, S., Lauderdale, J. M., Adkins, J. F., Bowman, K. W., Brix, H., Fenty, I., Gierach, M. M., Hill, C., Jahn, O., Landschutzer, P., Manizza, M., Mazloff, M. R., Miller, C. E., Schimel, D. S., Verdy, A., Whitt, D. B., Zhang, H. 2022. Attribution of Space-Time Variability in Global-Ocean Dissolved Inorganic Carbon. Global Biogeochemical Cycles. 36(3). DOI: 10.1029/2021GB007162 Crowell, S., Baker, D., Schuh, A., Basu, S., Jacobson, A. R., Chevallier, F., Liu, J., Deng, F., Feng, L., McKain, K., Chatterjee, A., Miller, J. B., Stephens, B. B., Eldering, A., Crisp, D., Schimel, D., Nassar, R., O'Dell, C. W., Oda, T., Sweeney, C., Palmer, P. I., Jones, D. B. A. 2019. The 2015-2016 carbon cycle as seen from OCO-2 and the global in situ network. Atmospheric Chemistry and Physics. 19(15), 9797-9831. DOI: 10.5194/acp-19-9797-2019 Konings, A. G., Bloom, A. A., Liu, J., Parazoo, N. C., Schimel, D. S., Bowman, K. W. 2019. Global satellite-driven estimates of heterotrophic respiration. Biogeosciences. 16(11), 2269-2284. DOI: 10.5194/bg-16-2269-2019 Liao, E., Resplandy, L., Liu, J., Bowman, K. W. 2020. Amplification of the Ocean Carbon Sink During El Ninos: Role of Poleward Ekman Transport and Influence on Atmospheric CO 2. Global Biogeochemical Cycles. 34(9). DOI: 10.1029/2020GB006574 Liu, J., Baskaran, L., Bowman, K., Schimel, D., Bloom, A. A., Parazoo, N. C., Oda, T., Carroll, D., Menemenlis, D., Joiner, J., Commane, R., Daube, B., Gatti, L. V., McKain, K., Miller, J., Stephens, B. B., Sweeney, C., Wofsy, S. 2021. Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020). Earth System Science Data. 13(2), 299-330. DOI: 10.5194/essd-13-299-2021 Liu, J., Bowman, K. W., Schimel, D. S., Parazoo, N. C., Jiang, Z., Lee, M., Bloom, A. A., Wunch, D., Frankenberg, C., Sun, Y., O'Dell, C. W., Gurney, K. R., Menemenlis, D., Gierach, M., Crisp, D., Eldering, A. 2017. Contrasting carbon cycle responses of the tropical continents to the 2015-2016 El Nino. Science. 358(6360). DOI: 10.1126/science.aam5690 Liu, J., Bowman, K., Parazoo, N. C., Bloom, A. A., Wunch, D., Jiang, Z., Gurney, K. R., Schimel, D. 2018. Detecting drought impact on terrestrial biosphere carbon fluxes over contiguous US with satellite observations. Environmental Research Letters. 13(9), 095003. DOI: 10.1088/1748-9326/aad5ef Liu, J., Wennberg, P. O., Parazoo, N. C., Yin, Y., Frankenberg, C. 2020. Observational Constraints on the Response of High-Latitude Northern Forests to Warming. AGU Advances. 1(4). DOI: 10.1029/2020AV000228 Parazoo, N. C., Bowman, K. W., Baier, B. C., Liu, J., Lee, M., Kuai, L., Shiga, Y., Baker, I., Whelan, M. E., Feng, S., Krol, M., Sweeney, C., Runkle, B. R., Tajfar, E., Davis, K. J. 2021. Covariation of Airborne Biogenic Tracers (CO 2 , COS, and CO) Supports Stronger Than Expected Growing Season Photosynthetic Uptake in the Southeastern US. Global Biogeochemical Cycles. 35(10). DOI: 10.1029/2021GB006956 Quetin, G. R., Bloom, A. A., Bowman, K. W., Konings, A. G. 2020. Carbon Flux Variability From a Relatively Simple Ecosystem Model With Assimilated Data Is Consistent With Terrestrial Biosphere Model Estimates. Journal of Advances in Modeling Earth Systems. 12(3). DOI: 10.1029/2019MS001889 Schuh, A. E., Jacobson, A. R., Basu, S., Weir, B., Baker, D., Bowman, K., Chevallier, F., Crowell, S., Davis, K. J., Deng, F., Denning, S., Feng, L., Jones, D., Liu, J., Palmer, P. I. 2019. Quantifying the Impact of Atmospheric Transport Uncertainty on CO 2 Surface Flux Estimates. Global Biogeochemical Cycles. 33(4), 484-500. DOI: 10.1029/2018GB006086 Worden, J., Saatchi, S., Keller, M., Bloom, A. A., Liu, J., Parazoo, N., Fisher, J. B., Bowman, K., Reager, J. T., Fahy, K., Schimel, D., Fu, R., Worden, S., Yin, Y., Gentine, P., Konings, A. G., Quetin, G. R., Williams, M., Worden, H., Shi, M., Barkhordarian, A. 2021. Satellite Observations of the Tropical Terrestrial Carbon Balance and Interactions With the Water Cycle During the 21st Century. Reviews of Geophysics. 59(1). DOI: 10.1029/2020RG000711 Yin, Y., Bowman, K., Bloom, A. A., Worden, J. 2019. Detection of fossil fuel emission trends in the presence of natural carbon cycle variability. Environmental Research Letters. 14(8), 084050. DOI: 10.1088/1748-9326/ab2dd7 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Liu, J., Baskarran, L., Bowman, K., Schimel, D., Bloom, A. A.,
Parazoo, N., Oda, T., Carrol, D., Menemenlis, D., Joiner, J.,
Commane, R., Daube, B., Gatti, L. V., McKain, K., Miller, J.,
Stephens, B. B., Sweeney, C., and Wofsy, S.: CMS-Flux NBE
2020 [Data set], NASA, DOI: 10.25966/4V02-C391
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| Chatterjee (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||
| Project Title: | Synthesis, Reconciliation and Assessment of CMS Prototype Products | |||||||||||||||||||||||||||||||||||||
|
Science Team |
Abhishek Chatterjee, NASA JPL
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||
| Abstract: |
The proposed research will prototype a synthesis and harmonization framework for existing and planned prototype products from the Carbon Monitoring System (CMS) initiative. The decade-long CMS program has generated a diverse suite of carbon monitoring products that are highly inhomogeneous in nature, in terms of their domain and space-/time-scales. We argue that the time is ripe for establishing a comprehensive and multifaceted framework to evaluate and diagnose these prototype products. The overarching goals of this proposal are to: (a) develop and implement a new CMS system capability that will conduct a thorough assessment and consistency check among various prototype products along with their reported uncertainty estimates, (b) develop and apply quantitative and hypothesis-driven approaches for reconciling bottom-up and top-down CMS prototype products, including characterization of uncertainties associated with net land flux estimates, and (c) recommend refinements or design of new CMS products to fill missing links to close the carbon budget or reduce uncertainties to better inform carbon policy and management decisions. Initially, at the prototyping stage the focus will be on large-scale carbon cycle analyses and budget assessments – our proposed objectives will assess the value of CMS prototype products at global and selected regional domains over both retrospective (pre-2015) and a more contemporary (post2015) period. However, our goal is to keep the framework flexible and scalable such that it can be easily adapted to other regional, or national and local scales as opportunities arise or relevant prototype products become available.
In addition, the proposing team will engage with, and contribute to two sets of scientific assessment groups (stakeholders - GCP and WMO IG3IS). Bidirectional communication channels will be established to deliver high-level syntheses information from CMS prototype products that can contribute to ongoing activities and objectives of these stakeholders. The proposed analyses will leverage existing CMS prototype products (several of which are already archived and accessible), bring in past and current developers of these products as well as tap into the growing network of in situ, satellite sensors on orbit and airborne assets. This proposal is timely – it evaluates the state of CMS prototype products right now, how robust are the reported uncertainty estimates on these products and the refinements necessary to meet the demands and requirements of the program's end goal - “… a prototype carbon monitoring system from an Earth’s system perspective” | |||||||||||||||||||||||||||||||||||||
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Participants: |
Sourish Basu, NASA GSFC GMAO / University of Maryland | |||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
2023. Chapter 2 : Climate Trends. Fifth National Climate Assessment DOI: 10.7930/NCA5.2023.CH2 Bruhwiler, L., Basu, S., Butler, J. H., Chatterjee, A., Dlugokencky, E., Kenney, M. A., McComiskey, A., Montzka, S. A., Stanitski, D. 2021. Observations of greenhouse gases as climate indicators. Climatic Change. 165(1-2). DOI: 10.1007/s10584-021-03001-7 Byrne, B., Baker, D. F., Basu, S., Bertolacci, M., Bowman, K. W., Carroll, D., Chatterjee, A., Chevallier, F., Ciais, P., Cressie, N., Crisp, D., Crowell, S., Deng, F., Deng, Z., Deutscher, N. M., Dubey, M., Feng, S., Garcia, O., Griffith, D. W. T., Herkommer, B., Hu, L., Jacobson, A. R., Janardanan, R., Jeong, S., Johnson, M. S., Jones, D. B. A., Kivi, R., Liu, J., Liu, Z., Maksyutov, S., Miller, J. B., Miller, S. M., Morino, I., Notholt, J., Oda, T., O'Dell, C. W., Oh, Y., Ohyama, H., Patra, P. K., Peiro, H., Petri, C., Philip, S., Pollard, D. F., Poulter, B., Remaud, M., Schuh, A., Sha, M. K., Shiomi, K., Strong, K., Sweeney, C., Te, Y., Tian, H., Velazco, V. A., Vrekoussis, M., Warneke, T., Worden, J. R., Wunch, D., Yao, Y., Yun, J., Zammit-Mangion, A., Zeng, N. National CO2 budgets (2015-2020) inferred from atmospheric CO2 observations in support of the Global Stocktake DOI: 10.5194/essd-2022-213 Feldman, A. F., Zhang, Z., Yoshida, Y., Chatterjee, A., Poulter, B. Using OCO-2 column CO2 retrievals to rapidly detect and estimate biospheric surface carbon flux anomalies DOI: 10.5194/acp-23-1545-2023 Feldman, A. F., Zhang, Z., Yoshida, Y., Gentine, P., Chatterjee, A., Entekhabi, D., Joiner, J., Poulter, B. 2023. A multi-satellite framework to rapidly evaluate extreme biosphere cascades: The Western US 2021 drought and heatwave. Global Change Biology. 29(13), 3634-3651. DOI: 10.1111/gcb.16725 Hurtt, G. C., Andrews, A., Bowman, K., Brown, M. E., Chatterjee, A., Escobar, V., Fatoyinbo, L., Griffith, P., Guy, M., Healey, S. P., Jacob, D. J., Kennedy, R., Lohrenz, S., McGroddy, M. E., Morales, V., Nehrkorn, T., Ott, L., Saatchi, S., Sepulveda Carlo, E., Serbin, S. P., Tian, H. 2022. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps. Environmental Research Letters. 17(6), 063010. DOI: 10.1088/1748-9326/ac7407 Laughner, J. L., Neu, J. L., Schimel, D., Wennberg, P. O., Barsanti, K., Bowman, K. W., Chatterjee, A., Croes, B. E., Fitzmaurice, H. L., Henze, D. K., Kim, J., Kort, E. A., Liu, Z., Miyazaki, K., Turner, A. J., Anenberg, S., Avise, J., Cao, H., Crisp, D., de Gouw, J., Eldering, A., Fyfe, J. C., Goldberg, D. L., Gurney, K. R., Hasheminassab, S., Hopkins, F., Ivey, C. E., Jones, D. B. A., Liu, J., Lovenduski, N. S., Martin, R. V., McKinley, G. A., Ott, L., Poulter, B., Ru, M., Sander, S. P., Swart, N., Yung, Y. L., Zeng, Z. 2021. Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change. Proceedings of the National Academy of Sciences. 118(46). DOI: 10.1073/pnas.2109481118 Lovenduski, N. S., Chatterjee, A., Swart, N. C., Fyfe, J. C., Keeling, R. F., Schimel, D. 2021. On the Detection of COVID-Driven Changes in Atmospheric Carbon Dioxide. Geophysical Research Letters. 48(22). DOI: 10.1029/2021GL095396 Ma, L., Hurtt, G., Ott, L., Sahajpal, R., Fisk, J., Lamb, R., Tang, H., Flanagan, S., Chini, L., Chatterjee, A., Sullivan, J. Global Evaluation of the Ecosystem Demography Model (ED v3.0) DOI: 10.5194/gmd-2021-292 Murray-Tortarolo, G., Perea, K., Mendoza-Ponce, A., Martinez-Arroyo, A., Murguia-Flores, F., Jaramillo, V. J., Serrano-Medrano, M., Garcia-Garcia, M., Vargas, R., Chatterjee, A., Michalak, A., Zhang, Z., Wang, J. A., Poulter, B. 2024. A Greenhouse Gas Budget for Mexico During 2000-2019. Journal of Geophysical Research: Biogeosciences. 129(1). DOI: 10.1029/2023JG007667 Murray-Tortarolo, G., Poulter, B., Vargas, R., Hayes, D., Michalak, A. M., Williams, C., Windham-Myers, L., Wang, J. A., Wickland, K. P., Butman, D., Tian, H., Sitch, S., Friedlingstein, P., O'Sullivan, M., Briggs, P., Arora, V., Lombardozzi, D., Jain, A. K., Yuan, W., Seferian, R., Nabel, J., Wiltshire, A., Arneth, A., Lienert, S., Zaehle, S., Bastrikov, V., Goll, D., Vuichard, N., Walker, A., Kato, E., Yue, X., Zhang, Z., Chaterjee, A., Kurz, W. 2022. A Process-Model Perspective on Recent Changes in the Carbon Cycle of North America. Journal of Geophysical Research: Biogeosciences. 127(9). DOI: 10.1029/2022JG006904 Ramonet, M., Chatterjee, A., Ciais, P., Levin, I., Sha, M. K., Steinbacher, M., Sweeney, C. 2023. CO2 in the Atmosphere: Growth and Trends Since 1850. Oxford Research Encyclopedia of Climate Science. DOI: 10.1093/acrefore/9780190228620.013.863 Weir, B., Crisp, D., O'Dell, C. W., Basu, S., Chatterjee, A., Kolassa, J., Oda, T., Pawson, S., Poulter, B., Zhang, Z., Ciais, P., Davis, S. J., Liu, Z., Ott, L. E. 2021. Regional impacts of COVID-19 on carbon dioxide detected worldwide from space. Science Advances. 7(45). DOI: 10.1126/sciadv.abf9415 Weir, B., Ott, L. E., Collatz, G. J., Kawa, S. R., Poulter, B., Chatterjee, A., Oda, T., Pawson, S. 2021. Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems. Atmospheric Chemistry and Physics. 21(12), 9609-9628. DOI: 10.5194/acp-21-9609-2021 Zhang, Z., Poulter, B., Knox, S., Stavert, A., McNicol, G., Fluet-Chouinard, E., Feinberg, A., Zhao, Y., Bousquet, P., Canadell, J. G., Ganesan, A., Hugelius, G., Hurtt, G., Jackson, R. B., Patra, P. K., Saunois, M., Hoglund-Isaksson, L., Huang, C., Chatterjee, A., Li, X. 2021. Anthropogenic emission is the main contributor to the rise of atmospheric methane during 1993-2017. National Science Review. 9(5). DOI: 10.1093/nsr/nwab200 | |||||||||||||||||||||||||||||||||||||
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| Project Title: | Continuation and expansion to a national-scale of the filling a critical gap in Indonesia's national carbon monitoring, reporting, and verification capabilities for supporting REDD+ activities: Incorporating, quantifying and locating fire emissions from within tropical peat-swamp forests project | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Mark Cochrane, University of Maryland
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Cochrane (CMS 2013) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Cochrane (CMS 2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Indonesia ranks as the 3rd largest CO2eq emitting nation, largely due to episodic uncontrolled fires within drained peat-swamp forests. The original project (NNX13AP46G) set out to 1) provide extensive field investigation of land cover, hydrologic, fuel and fire dynamics in a 120,000 ha REDD+ project in Central Kalimantan; 2) Collect a new LIDAR dataset to complement our existing 2007 and 2011 coverages; 3) Conduct groundbreaking detailed emissions field sampling of smoldering in-situ peat fires; and 4) Generate a fully parameterized and validated annual emissions model for the study region in support of its REDD+ project. Despite extensive bureaucratic and logistical challenges and delays inherent in working in Indonesia, objectives 1-3 have now been completed and the modeling efforts are ongoing with all necessary data now in hand as we complete the original project time period. However, our recent unprecedented emission findings (Stockwell et al. 2016), gained in situ during the height of the 2015 El Niño, have documented substantial differences between the actual regional peat fire emissions and existing emission factors, indicating regional Indonesian carbon equivalent emissions (100 year) may have been 19% less than current IPCC-based emission factor estimates. The IPCC emission factors are derived from one lab study burning peat from Sumatra (Christian et al. 2003) and considerable variation in emissions may exist between peat fires of Indonesia’s three major peat formations highlighting the need for the additional field emissions measurements we intend to carry out in the continuation of the project proposed here.
We propose expanding to a national level, our successful regional (Kalimantan) CMS project (NNX13AP46G), to better advance Indonesia’s Monitoring, Reporting and Verification (MRV) capabilities for peatland carbon emissions and support nationwide Reducing Emissions from Deforestation and Forest Degradation (REDD) efforts. We will implement our standardized field-based analyses of fuels, hydrology, peat burning characteristics and fire emissions, developed from our ongoing work in a 120,000 ha REDD+ project, to regionally parameterize our peatland emissions model for all of Indonesia’s major peatland areas by including three new locations, Riau and Jambi (Sumatra) and Western Papua (Papua), for inclusion within the Indonesian National Carbon Accounting System (INCAS). We will conduct on-site whole air sampling of natural peat smoke plumes in situ for precise measurement of non-reactive greenhouse gases, collect peat samples just in front of these active peat fires, and burn the samples in the US while measuring aerosol mass and optical properties and reactive gases. This will create comprehensive and pertinent emissions factors (EFs) for each study region that will be critically important for assessing health impacts and total global warming potential (GWP) of these emissions. Remotely sensed land cover/change (Landsat) and surface fire ignition timing and locations (MODIS) provide spatial and temporal drivers for the modeled emissions that will now be validated/constrained at a national level using biomass burning emissions estimations derived from Visible/Infrared Imager and Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite and the new Japanese Geostationary Meteorological Satellite (Himawari-8). Multiple LIDAR datasets (2014, 2011, 2007) for Kalimantan are being used to quantify model accuracy, and new work will be undertaken to quantify uncertainty in our most
recent LIDAR-based digital terrain model (DTM), further improving assessments of modeling errors. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Israr Albar, Indonesia Ministry of Environment and Forestry | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Applegate, Grahame, Laura L. B. Graham, Andri Thomas, Ahmad Yunan, Didie, Agus, Ato, Bambang H. Saharjo and Mark A. Cochrane. 2017.Fire Scene Evaluation Field Manual/ Petunjuk laPang evaluasi kejadian kebakaran Penerbit. IPB Press IPB Science Techno Park, Kota Bogor - Indonesia ISBN: 978-602-440-173-3 Goldstein, J. E., Graham, L., Ansori, S., Vetrita, Y., Thomas, A., Applegate, G., Vayda, A. P., Saharjo, B. H., Cochrane, M. A. 2020. Beyond slash-and-burn: The roles of human activities, altered hydrology and fuels in peat fires in Central Kalimantan, Indonesia. Singapore Journal of Tropical Geography. 41(2), 190-208. DOI: 10.1111/sjtg.12319 Jayarathne, T., Stockwell, C. E., Gilbert, A. A., Daugherty, K., Cochrane, M. A., Ryan, K. C., Putra, E. I., Saharjo, B. H., Nurhayati, A. D., Albar, I., Yokelson, R. J., Stone, E. A. 2018. Chemical characterization of fine particulate matter emitted by peat fires in Central Kalimantan, Indonesia, during the 2015 El Nino. Atmospheric Chemistry and Physics. 18(4), 2585-2600. DOI: 10.5194/acp-18-2585-2018 Kemal Putra, I., Hero Saharjo, B., Wasis, B. 2019. Institutional Challenge on Forest and Land Fire Management at the Site Level. Jurnal Ilmu Pertanian Indonesia. 24(2), 151-159. DOI: 10.18343/jipi.24.2.151 Li, F., Zhang, X., Kondragunta, S., Lu, X. 2020. An evaluation of advanced baseline imager fire radiative power based wildfire emissions using carbon monoxide observed by the Tropospheric Monitoring Instrument across the conterminous United States. Environmental Research Letters. 15(9), 094049. DOI: 10.1088/1748-9326/ab9d3a Lu, X., Zhang, X., Li, F., Cochrane, M. A. 2019. Investigating Smoke Aerosol Emission Coefficients Using MODIS Active Fire and Aerosol Products: A Case Study in the CONUS and Indonesia. Journal of Geophysical Research: Biogeosciences. 124(6), 1413-1429. DOI: 10.1029/2018JG004974 Putra, E. I., Cochrane, M. A., Vetrita, Y., Graham, L., Saharjo, B. H. 2018. Determining critical groundwater level to prevent degraded peatland from severe peat fire. IOP Conference Series: Earth and Environmental Science. 149, 012027. DOI: 10.1088/1755-1315/149/1/012027 Sinclair, A. L., Graham, L. L., Putra, E. I., Saharjo, B. H., Applegate, G., Grover, S. P., Cochrane, M. A. 2020. Effects of distance from canal and degradation history on peat bulk density in a degraded tropical peatland. Science of The Total Environment. 699, 134199. DOI: 10.1016/j.scitotenv.2019.134199 Vetrita, Y., Cochrane, M. A. 2019. Fire Frequency and Related Land-Use and Land-Cover Changes in Indonesia's Peatlands. Remote Sensing. 12(1), 5. DOI: 10.3390/rs12010005 Yokelson, R. J., Saharjo, B. H., Stockwell, C. E., Putra, E. I., Jayarathne, T., Akbar, A., Albar, I., Blake, D. R., Graham, L. L. B., Kurniawan, A., Meinardi, S., Ningrum, D., Nurhayati, A. D., Saad, A., Sakuntaladewi, N., Setianto, E., Simpson, I. J., Stone, E. A., Sutikno, S., Thomas, A., Ryan, K. C., Cochrane, M. A. 2022. Tropical peat fire emissions: 2019 field measurements in Sumatra and Borneo and synthesis with previous studies. Atmospheric Chemistry and Physics. 22(15), 10173-10194. DOI: 10.5194/acp-22-10173-2022 Zarzana, K. J., Selimovic, V., Koss, A. R., Sekimoto, K., Coggon, M. M., Yuan, B., Dube, W. P., Yokelson, R. J., Warneke, C., de Gouw, J. A., Roberts, J. M., Brown, S. S. Primary emissions of glyoxal and methylglyoxal from laboratory measurements of open biomass burning DOI: 10.5194/acp-2018-521 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Vetrita, Y., and M.A. Cochrane. 2019. Annual Burned Area from Landsat, Mawas, Central Kalimantan, Indonesia, 1997-2015. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1708
Vetrita, Y., and M.A. Cochrane. 2021. Land Use and Cover Maps from Landsat, Mawas, Central Kalimantan, Indonesia, 1994-2019. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1838 Lu, X., X. Zhang, F. Li, and M.A. Cochrane. 2023. Fire Particulate Emissions from Combined VIIRS and AHI Data for Indonesia, 2015-2020. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2118 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Cochrane (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Effectiveness and monitoring of large-scale carbon-loss mitigation activities in Indonesia’s peatlands | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Mark Cochrane, University of Maryland
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Cochrane (CMS 2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Indonesia is engaged in, arguably, the planet’s largest carbon-flux mitigation project. Through its Peatland Restoration Agency (BRG) they are blocking drainage canals with the objective of peatland restoration (“rewetting, revegetation and revitalization”) for 2.5 million ha of drained and degraded peatlands by 2020. Furthermore, the Ministry of Environment and Forestry has plans for restoring another ~5 million ha in the coming years. Reducing carbon emissions from Indonesia’s tropical converted peatlands, where frequent wildfires have become a globally-significant side-effect (84% of Southeast Asia’s carbon emissions), is essential for stabilizing global climate. Indonesia has unilaterally committed to reduceing its GHG emissions by 29%. However, Indonesia has yet to develop a methodology for monitoring and evaluating the effectiveness at reducing GHG emissions from these peatland restoration projects. We propose to work with our stakeholders, BRG and FAO, to assess the effects of ongoing mitigation projects upon landscape hydrology, fire occurrence and behavior, and vegetation regrowth, which all impact carbon fluxes. We will accomplish this by building upon our established research infrastructure, with hundreds of established dipwells (hydrology), long term vegetation plots, and years of applying common methods for assessing fire behavior and carbon emissions at large research sites we maintain in Central Kalimantan (Indonesian Borneo), Riau and Jambi Provinces (Sumatra). We will work with FAO to use our extensive field data (space and time), to calibrate the PRIMS Soil Moisture product. By relating PRIMS responses to water table depths, vegetation cover, distance to canals and frequency and time since disturbance, we will define the uncertainties of the PRIMS soil moisture estimates (Sentinel 1) as functions of of these landscape attributes. We will subsequently validate the PRIMS products by verifying its accuracy when applied in Riau and Jambi, our other research sites that also contain ongoing BRG mitigation activities. We will also build a hydrologic model of the Mawas site (Kalimantan) to ascertain if canal blocking is only raising water levels, or if it is also changing drainage properties of the underlying peat. By detecting fires (MODIS/VIIRS), monitoring their behavior and peat consumption in the field, mapping their extent (Landsat/Sentinel 2), and evaluating changes in the particle emissions in smoke plumes (VIIRS), we will quantify changes in fire-related emissions from the mitigation activities. Similarly, by monitoring any changes in growth or composition within our vegetation plots, we will assess if the mitigation efforts are resulting in apparent restoration of degraded forests. We will also test the planned FAO PRIMS Subsidence product against our long term data series of well distributed subsidence plots to calibrate its outputs, compare it with GEDI lidar points (if available) to assess if discernible changes in peat height loss rates exist within and outside mitigation areas. These combined activities enable us to produce a comprehensive evaluation of if, where and how BRG mitigation activities are effecting carbon fluxes, while developing tools that will enhance our prototype CMS for tropical peatlands.
The proposed work uses many satellite products to produce enhance CMS capabilities that are critical to assessing near-to-mid term carbon fluxes and changes to those fluxes caused by peatland restoration efforts. We are also advancing our stakeholder’s interests by helping BRG to assess effectiveness of mitigation projects, and helping FAO calibrate an important peatland monitoring tool that provides early warning capabilities for illegal conversion. By providing the CMS tools for evaluating carbon fluxes in these globally important ecosystems we are advancing carbon products but more importantly providing societally relevant information on the largest carbon mitigation activities being undertaken. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Israr Albar, Indonesia Ministry of Environment and Forestry | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Cochrane, M. A., Bowman, D. M. J. S. 2021. Manage fire regimes, not fires. Nature Geoscience. 14(7), 455-457. DOI: 10.1038/s41561-021-00791-4 Graham, L. L. B., Applegate, G. B., Thomas, A., Ryan, K. C., Saharjo, B. H., Cochrane, M. A. 2022. A Field Study of Tropical Peat Fire Behaviour and Associated Carbon Emissions. Fire. 5(3), 62. DOI: 10.3390/fire5030062 Hafni, D. A. F., Putra, E. I., Harahap, A. A. N., Saharjo, B. H., Graham, L., Nurhayati, A. D., Cochrane, M. A. 2022. Peat fire risk assessment in Central Kalimantan, Indonesia using the Standardized Precipitation Index (SPI). IOP Conference Series: Earth and Environmental Science. 959(1), 012058. DOI: 10.1088/1755-1315/959/1/012058 Jessup, T. C., Vayda, A. P., Cochrane, M. A., Applegate, G. B., Ryan, K. C., Saharjo, B. H. 2021. Why estimates of the peat burned in fires in Sumatra and Kalimantan are unreliable and why it matters. Singapore Journal of Tropical Geography. 43(1), 7-25. DOI: 10.1111/sjtg.12406 Lu, X., Zhang, X., Li, F., Cochrane, M. A., Ciren, P. 2021. Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions. Remote Sensing. 13(2), 196. DOI: 10.3390/rs13020196 Lu, X., Zhang, X., Li, F., Gao, L., Graham, L., Vetrita, Y., Saharjo, B. H., Cochrane, M. A. 2021. Drainage canal impacts on smoke aerosol emissions for Indonesian peatland and non-peatland fires. Environmental Research Letters. 16(9), 095008. DOI: 10.1088/1748-9326/ac2011 Nurhayati, A. D., Hero Saharjo, B., Sundawati, L., Syartinilia, S., A. Cochrane, M. 2021. Forest and Peatland Fire Dynamics in South Sumatra Province. Forest and Society. 591-603. DOI: 10.24259/fs.v5i2.14435 Putra, E. I., Ramadhi, A., Shadiqin, M. F., Saad, A., Setianto, E., Nurhayati, A. D., Saharjo, B. H., Cochrane, M. A. 2022. Assessing the severity of forest fire in Sungai Buluh Protected Peat Forest, Jambi. IOP Conference Series: Earth and Environmental Science. 959(1), 012059. DOI: 10.1088/1755-1315/959/1/012059 Vetrita, Y., Cochrane, M. A., Suwarsono, S., Priyatna, M., Sukowati, K. A. D., Khomarudin, M. R. 2021. Evaluating accuracy of four MODIS-derived burned area products for tropical peatland and non-peatland fires. Environmental Research Letters. 16(3), 035015. DOI: 10.1088/1748-9326/abd3d1 Yokelson, R. J., Saharjo, B. H., Stockwell, C. E., Putra, E. I., Jayarathne, T., Akbar, A., Albar, I., Blake, D. R., Graham, L. L. B., Kurniawan, A., Meinardi, S., Ningrum, D., Nurhayati, A. D., Saad, A., Sakuntaladewi, N., Setianto, E., Simpson, I. J., Stone, E. A., Sutikno, S., Thomas, A., Ryan, K. C., Cochrane, M. A. 2022. Tropical peat fire emissions: 2019 field measurements in Sumatra and Borneo and synthesis with previous studies. Atmospheric Chemistry and Physics. 22(15), 10173-10194. DOI: 10.5194/acp-22-10173-2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Vetrita, Y., and M.A. Cochrane. 2019. Annual Burned Area from Landsat, Mawas, Central Kalimantan, Indonesia, 1997-2015. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1708
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| Cook (CMS 2015) (2016) | |||||||||||||||||||||||||||||||||||||||
| Project Title: | Remote Sensing as a Bridge to Operational Forest Carbon Monitoring in Interior Alaska | ||||||||||||||||||||||||||||||||||||||
|
Science Team |
Bruce Cook, NASA GSFC
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | ||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Cook (TE 2014) Morton (CMS 2013) | ||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Cook (CMS 2018) | ||||||||||||||||||||||||||||||||||||||
| Abstract: |
Monitoring U.S. forest carbon stocks is critical for natural resource management and national greenhouse gas reporting activities. The USFS Forest Inventory and Analysis (FIA) program—the largest network of permanent forest inventory plots in the world covers most U.S. forestlands. However, more than 450,000 km2 of forests in Interior Alaska (15% of US forestland) are not included in the FIA program, as these remote regions are difficult and expensive to monitor with standard field methods. Recent warming and projected future impacts from climate change on forest carbon stocks, composition, and extent have elevated the need to develop new approaches for forest monitoring in Alaska. The broader policy focus on land carbon sinks also encourages monitoring and accounting of the complete US land carbon sink, including Interior Alaska. Article 4 of the Paris Agreement recognizes the importance of “removals by sinks of greenhouse gases,” and specifically requests that national inventories include information on removals.
Here, we propose to expand the joint NASA-USFS Pilot Project in the Tanana Inventory Unit, funded in part by ROSES-2013 CMS, to inventory a second USFS region in Interior Alaska, the Susitna-Copper River (SCR) Inventory Unit. Based on the success of the pilot project, the USFS has initiated a 10-year, $25M inventory plan for Interior Alaska using remote sensing data from Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager (http://gliht.gsfc.nasa.gov). The proposed research leverages USFS funding for G-LiHT data collection. However, the USFS inventory activity does not support research collaboration between NASA and USFS scientists, data analysis, or methods development. The proposed CMS project supports the transition of lidar- assisted forest inventory activities from research to operations, targeting specific objectives for NASA’s CMS program to use “remote sensing data products to produce and evaluate prototype MRV system approaches” and “studies that address research needs to advance remote sensing-based approaches to MRV” identified in Section 2.1 of the ROSES-2015 CMS solicitation (A.7).
The proposed project has five components. The first three activities represent a continuation of research themes and data products outlined in the NASA-USFS Pilot Project, including specific requests for core inventory products by the USFS Forest Inventory & Analysis (FIA) Program, a key stakeholder for this effort. Core project components include 1) collaboration between USFS and NASA scientists on experimental design for optimal integration of field and lidar data for forest carbon monitoring, 2) estimation of forest carbon stocks for the SCR Inventory Unit using established methods to combine plot and lidar data, and 3) development of new, spatially explicit estimates of carbon stocks and uncertainties using hierarchical Bayesian
statistical methods. In addition to these core inventory activities, we will use the combination of field inventory plots and G-LiHT data to 4) develop estimates of woody shrub biomass (e.g., alder and willow), a dominant feature of boreal forest landscapes that are not included in FIA inventory estimates, and 5) collaborate with USFS Forest Health experts to identify mortality and carbon losses from insects and disease (e.g., spruce bark beetle, aspen and birch leaf miners, birch leaf roller, alder dieback and canker disease). These additional project components target two specific needs identified by USFS scientists and stakeholders.
The main outcomes from this work will be estimates of total (live + dead) forest carbon stocks, including woody shrubs, and associated uncertainties for the SCR Inventory Unit of Interior Alaska. These estimates provide critical and timely information for carbon monitoring and resource management, and baseline conditions for the spatial distribution of vegetation carbon stocks in a region undergoing rapid climate change. | ||||||||||||||||||||||||||||||||||||||
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Participants: |
Michael Alonzo, American University | ||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Alonzo, M., Andersen, H., Morton, D., Cook, B. 2018. Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion. Forests. 9(3), 119. DOI: 10.3390/f9030119 Alonzo, M., Dial, R. J., Schulz, B. K., Andersen, H., Lewis-Clark, E., Cook, B. D., Morton, D. C. 2020. Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar. Remote Sensing of Environment. 245, 111841. DOI: 10.1016/j.rse.2020.111841 Alonzo, M., Morton, D. C., Cook, B. D., Andersen, H., Babcock, C., Pattison, R. 2017. Patterns of canopy and surface layer consumption in a boreal forest fire from repeat airborne lidar. Environmental Research Letters. 12(6), 065004. DOI: 10.1088/1748-9326/aa6ade Andersen, H. -E., C. Babcock, B. Cook, D. Morton, A. Finley and M. Alonzo. Using remote sensing to support forest inventory in interior Alaska – demonstration of a generalized regression estimator in a two-phase, model-assisted sampling design using two-sources of auxiliary data. Forests (submitted). Babcock, C., Finley, A. O., Andersen, H., Pattison, R., Cook, B. D., Morton, D. C., Alonzo, M., Nelson, R., Gregoire, T., Ene, L., Gobakken, T., Naesset, E. 2018. Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations. Remote Sensing of Environment. 212, 212-230. DOI: 10.1016/j.rse.2018.04.044 Babcock, C., Finley, A. O., Cook, B. D., Weiskittel, A., Woodall, C. W. 2016. Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data. Remote Sensing of Environment. 182, 1-12. DOI: 10.1016/j.rse.2016.04.014 Ene, L. T., Gobakken, T., Andersen, H., Naesset, E., Cook, B. D., Morton, D. C., Babcock, C., Nelson, R. 2018. Large-area hybrid estimation of aboveground biomass in interior Alaska using airborne laser scanning data. Remote Sensing of Environment. 204, 741-755. DOI: 10.1016/j.rse.2017.09.027 Finley, A. O., Datta, A., Cook, B. D., Morton, D. C., Andersen, H. E., Banerjee, S. 2019. Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes. Journal of Computational and Graphical Statistics. 28(2), 401-414. DOI: 10.1080/10618600.2018.1537924 Finley, A. O., S. Banerjee, Y. Zhou and B. D. Cook. 2016. Process-based hierarchical models for coupling high-dimensional LiDAR and forest variables over large geographic domains. Journal of the American Statistical Association, arXiv: 1603.07409 Montesano, P. M., Neigh, C. S., Wagner, W., Wooten, M., Cook, B. D. 2019. Boreal canopy surfaces from spaceborne stereogrammetry. Remote Sensing of Environment. 225, 148-159. DOI: 10.1016/j.rse.2019.02.012 Pattison, R., Andersen, H., Gray, A., Schulz, B., Smith, R. J., Jovan, S. 2018. Forests of the Tanana Valley State Forest and Tetlin National Wildlife Refuge, Alaska: results of the 2014 pilot inventory DOI: 10.2737/pnw-gtr-967 Shirota, S., A. O. Finley, B. D. Cook and S. Banerjee. Conjugate nearest neighbor Gaussian process models for efficient statistical interpolation of large spatial data. IEEE Transactions on Geoscience and Remote Sensing (submitted). Shoot, C., H. -E. Andersen, Monika Moskal, C. Babcock, B. Cook and D. Morton. Classifying Forest Type in the National Forest Inventory Context from a Fusion of Hyperspectral and Lidar Data. Remote Sensing of Environment (submitted). Taylor-Rodriguez, D., Finley, A. O., Datta, A., Babcock, C., Andersen, H., Cook, B. D., Morton, D. C., Banerjee, S. 2019. Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping. Statistica Sinica. DOI: 10.5705/ss.202018.0005 Finley, A. O., Banerjee, S., Zhou, Y., Cook, B. D., Babcock, C. 2017. Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables. Remote Sensing of Environment. 190, 149-161. DOI: 10.1016/j.rse.2016.12.004 Datta, A., Banerjee, S., Finley, A. O., Gelfand, A. E. 2016. On nearest-neighbor Gaussian process models for massive spatial data. WIREs Computational Statistics. 8(5), 162-171. DOI: 10.1002/wics.1383 Salazar, E., Hammerling, D., Wang, X., Sanso, B., Finley, A. O., Mearns, L. O. 2016. Observation-based blended projections from ensembles of regional climate models. Climatic Change. 138(1-2), 55-69. DOI: 10.1007/s10584-016-1722-1 | ||||||||||||||||||||||||||||||||||||||
| Cook (CMS 2018) (2019) | |||
| Project Title: | NASA-USFS Partnership to Advance Operational Forest Carbon Monitoring in Interior Alaska | ||
|
Science Team |
Bruce Cook, NASA GSFC
(Project Lead)
| ||
| Solicitation: | NASA: Carbon Monitoring System (2018) | ||
| Precursor Projects: | Cook (CMS 2015) | ||
| Abstract: |
The USDA-Forest Service (USFS) has partnered with NASA to undertake the first, systematic inventory of forests in interior Alaska. Following a successful pilot study in the Tanana Inventory Unit (CMS 2013), the USFS initiated a 10-year plan to combine a 1/5 intensity grid of forest inventory plots with data from NASA Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager. The second inventory unit, the Copper River/Susitna, implemented the pilot strategy and targeted specific science questions regarding shrub biomass and the increase in pest and pathogen events under a warming climate (CMS 2016). G-LiHT data products supported USFS operations, including pre-field planning to confirm forested conditions and helicopter access, and GLiHT transects reduced uncertainty in estimated aboveground biomass by sampling between plot locations across the vast expanse of interior Alaska. For NASA, the partnership has generated both science and methods developments. For example, tree cores from remote field plots capture forest growth in response to climate change, GLiHT data reveal the fine-scale heterogeneity in vegetation structure and surface topography, and new hierarchical Bayesian modeling frameworks provide robust, scalable solutions that link sparse field data with sampled and wall-to-wall remote sensing information.
Here, we propose a continuation of the NASA-USFS collaboration on inventory activities, expanding work into the Southwest Inventory Unit that extends from the western boundaries of Denali National Park to the southern Aleutian Islands. Collaboration on the Southwest Inventory Unit will require greater reliance on remotely sensed data, including the use of G-LiHT-only plots where helicopter and field crew access is not possible. The proposed work has three primary science objectives. First, we will derive more inventory variables directly from G LiHT data, including estimates of biomass by species and standing dead biomass, to avoid sampling bias from remote or wilderness areas that cannot be accessed by FIA crews. Second, we will estimate shrub biomass; the FIA inventory does not include shrub species (e.g., willow, alder), but benchmark data on shrub cover, height, and total biomass are critical for complete carbon monitoring in this rapidly-warming landscape. Third, we will map the distribution of soil organic carbon based on soil cores from FIA plots and covariates derived from G-LiHT and remote sensing data.
The proposed effort also includes two objectives for methods development. The first methods task is to expand the hierarchical modeling framework for spatially-explicit biomass estimation to generate a model that delivers joint pixel-level prediction, with associated uncertainty, for forest biomass by species and other inventory attributes. Second, given the extremely high costs for helicopter access to remote field locations, we will develop an optimization routine to quantify and target regions with highest uncertainty and quantify reductions in uncertainty from the addition of new field, GLiHT, and other remote sensing data. Development of both methods directly address the emphasis within CMS to quantify and reduce uncertainties.
The proposed work directly responds to the requests in the CMS solicitation for continued development of prototype CMS products, including the use of remote sensing data for carbon monitoring efforts for decision support. Although G-LiHT Flights are funded separately by FIA, science support from the NASA CMS Program has been essential for the NASA-USFS partnership on inventory and science activities. | ||
| |||
|
Participants: |
Michael Alonzo, American University | ||
| Project URL(s): | None provided. | ||
| Data Products: | None provided. | ||
| Publications: |
Heaton, M. J., Datta, A., Finley, A. O., Furrer, R., Guinness, J., Guhaniyogi, R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M., Lindgren, F., Nychka, D. W., Sun, F., Zammit-Mangion, A. 2018. A Case Study Competition Among Methods for Analyzing Large Spatial Data. Journal of Agricultural, Biological and Environmental Statistics. 24(3), 398-425. DOI: 10.1007/s13253-018-00348-w | ||
| Dietze (CMS 2016) (2017) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | A prototype data assimilation system for the terrestrial carbon cycle to support Monitoring, Reporting, and Verification | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Michael Dietze, Boston University
(Project Lead)
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Solicitation: | NASA: Carbon Monitoring System (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Dietze (CMS 2020) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
NASA in general, and CMS in particular, have devoted considerable resources to developing remote sensing data products aimed at quantifying and understanding the terrestrial carbon (C) cycle. Similar efforts have been taken throughout the research
community, generating bottom-up estimates based on inventory data, eddy covariance, process-based models, etc. While these efforts collectively span a wide range of observations (optical, lidar, radar, field-measurements) and response variables (cover, pools, fluxes, disturbances), each data product typically only leverages one or two data sources. However, what is fundamentally needed to improve monitoring, reporting and verification (MRV) isn’t numerous alternative C estimates but a synthetic view of the whole. Furthermore, any approach to synthesis needs to be flexible and extensible, so that it can deal with different data sources with different spatial and temporal resolutions, extents, and uncertainties, as well as new sensors and products as they are brought online. Finally, it needs to inform top-down atmospheric inversions, which currently cannot ingest these bottom-up C estimates an a constraint.
We propose to develop a prototype synthesis, focused initially on the continental US (CONUS), by employing a formal Bayesian model-data assimilation between process- based ecosystem models and multiple data sources to estimate key C pools and fluxes. Models are at the center of our novel system, but rather than providing a prognostic forward-simulation they serve as a scaffold in a fundamentally data-driven process by allowing different data sources to be merged together. Essentially, while data on different scales and processes are difficult to merge directly, all of these data can be used to inform the state variables (i.e. pools not parameters) in the models. In addition to a ‘best estimate’ of the terrestrial C cycle, a key outcome of such a synthesis would be a robust and transparent accounting of uncertainties. This approach is also exceedingly extensible to new data products, or to changes in the availability of data in space and time, as assimilation only requires the construction of simple data models (e.g. Likelihoods) that link model states to observations. The proposed bottom-up model-data assimilation will also provide informative prior means and uncertainties for the CarbonTracker-Lagrange (CT-L) inverse modeling framework. This assimilation of a robust, data-driven bottom- up prior will provide, for the first time, a formal synthesis between top-down and bottom- up C estimates.
While new to the CMS team, PIs Dietze and Serbin have extensive experience with remote sensing, field measurements, process-based modeling, and model-data fusion. The proposed work explicitly builds upon their PEcAn model-data informatics system and directly leverages numerous data products CMS has already invested in over the CONUS region. The prototype system will build on existing PEcAn data assimilation case studies focused on inventory data, phenology, and hyperspectral remote sensing. The proposed project leverages three parallel and interlocking lines of research. First, we will extend our existing system to iteratively ingest a range of CMS data products (airborne lidar, GLAS satellite lidar, radar, hyperspatial forest cover, disturbance products, etc.). Second, to address the challenges in assimilating disturbance and land use, we will incorporate the well-established Ecosystem Demography scaling approach into the data assimilation system itself. Third, we will coordinate with Co-PI Andrews' CMS inversion team to prototype informative land priors for use in top-down inversions as a proof-of-concept on top-down/bottom-up integration. Finally, our proposed prototype project has an obvious extension to global-scale bottom-up international MRV and REDD activities as well as a
range of top-down inversions. Overall, this proposal has the potential to strengthen the entire CMS portfolio. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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|
Participants: |
Arlyn Andrews, NOAA Earth System Research Laboratory | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Dokoohaki, H., Morrison, B. D., Raiho, A., Serbin, S. P., Dietze, M. A novel model-data fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2 DOI: 10.5194/gmd-2021-236 Fer, I., Kelly, R., Moorcroft, P. R., Richardson, A. D., Cowdery, E. M., Dietze, M. C. 2018. Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation. Biogeosciences. 15(19), 5801-5830. DOI: 10.5194/bg-15-5801-2018 Hurtt, G. C., Andrews, A., Bowman, K., Brown, M. E., Chatterjee, A., Escobar, V., Fatoyinbo, L., Griffith, P., Guy, M., Healey, S. P., Jacob, D. J., Kennedy, R., Lohrenz, S., McGroddy, M. E., Morales, V., Nehrkorn, T., Ott, L., Saatchi, S., Sepulveda Carlo, E., Serbin, S. P., Tian, H. 2022. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps. Environmental Research Letters. 17(6), 063010. DOI: 10.1088/1748-9326/ac7407 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Dubayah (CMS 2018) (2019) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Pantropical structure and biomass mapping using the fusion of GEDI and TanDEM-X data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Ralph Dubayah, University of Maryland
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Dubayah (CMS 2022) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
One of the key goals of the Carbon Monitoring System (CMS) is to provide accurate maps of the carbon status of the Earth’s forests at regional to global scales based on remote sensing data. While existing methods have produced pantropical and global forest biomass estimates, these have had exceptionally large errors that reduces their useful spatial resolution to coarse resolutions (arguably > 10 km). The major reason behind this status has been the lack of suitable satellite data on ecosystem structure that can be used to drive models that predict biomass. The Global Ecosystem Dynamics Investigation (GEDI) was selected in late 2014 as an Earth Ventures mission to provide the lidar data required to vastly improve our ability to map biomass. GEDI was successfully launched in late 2018 and is now acquiring science data. Over its two-year mission GEDI will provide about 10 billion estimates of canopy structure and biomass, a vast improvement over existing products. As part of GEDI, the mission has a collaboration with the German Aerospace Center(DLR) to explore the fusion of GEDI lidar data with the Xband, interferometric data of TanDEM-X (TDX) towards producing finer resolution maps of structure and biomass (e.g. at 100 m resolution) compared to GEDI’s 1 km gridded products, and whether such data may also be used to fill in GEDI gaps in coverage caused by clouds and variation in orbital sampling. This collaboration is ongoing but only towards the production of demonstration products for a few limited areas to demonstrate proof of concept. Over the past four years, the joint GEDI/DLR work has definitively affirmed the capability of this fusion to provide improved structure and biomass products at much finer resolution and higher accuracies than can be achieved by either mission by itself. Consequently, DLR has agreed to partner with us in this proposal towards the production of a global height and biomass map from fusion of GEDI and TDX data (at no cost to NASA). The overall goal of our project is to create pantropical products of canopy structure and biomass at fine resolution, jointly with DLR. The work has two main thrusts. The first, is the continued testing and application of GEDI/TDX fusion algorithms, based on established radiative transfer algorithms parameterized with GEDI data, that enable improved height estimates from TDX. The second is to create maps of biomass by relating these heights at 25 m resolution to GEDI footprint estimates of biomass, producing wall-to-wall biomass maps for the GEDI epoch. These biomass maps are then aggregated to coarser resolutions (from 1 ha to 1 km sq. to regional and countryscale) and errors are estimated using generalized hierarchical model-based inference (GHMB) as well as other methods of uncertainty estimation. The processing of pantropical TDX data will be led by DLR and the creation of height and biomass from these data will be jointly led by US and DLR scientists. The project accesses and responds to stakeholder needs through its partnering with Ecometrica and its management of the UK Space Agency’s Forests 2020 project. The research effort will initially focus on the 6 partner countries in Forests 2020, and the AfriSAR area in Gabon, with eventual expansion to the entire pantropics. The project provides an unprecedented opportunity to produce the most accurate and detailed map of forest biomass yet and will serve as a solid baseline for MRV efforts and other CMS projects. It further exploits NASA’s Earth Ventures investment in GEDI towards meeting the goals of the Carbon Monitoring System. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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|
Participants: |
John Armston, University of Maryland | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Duren (CMS 2015) (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Prototype methane monitoring system for California | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Riley Duren, Carbon Mapper/U. Arizona
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Duren (CMS 2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
We propose to leverage a planned project with California stakeholder agencies to develop a prototype methane monitoring system for the state. The California Air Resources Board (CARB) and the California Energy Commission (CEC) are funding JPL to conduct a baseline survey of methane super-emitters across the state in late 2016 using proven airborne imaging spectrometers. The California Baseline Methane Survey will produce a data set of geolocated methane plume images for super-emitter sources. We propose to build on and dramatically improve the relevance of that data set by developing and validating point source flux estimates, uncertainty estimates, linking that information with multi-scale attribution data and regional flux estimates derived from other CMS and NACP projects (that employ satellite and surface observations), and coordinating with California stakeholder agencies to infuse those products into their decision-making frameworks. We will also work with a broader set of stakeholders to evaluate the potential future application of this Prototype Methane Monitoring System in other key regions in the US and internationally.
Our proposed development of a Prototype Methane Monitoring System for California is of immediate societal relevance and significance given growing priorities to account for and mitigate methane emissions. The recently approved California law AB1496 states that “there is an urgent need to improve the monitoring and measurement of methane emissions from the major sources in California” and directs the California Air Resources Board to “undertake, in consultation with districts that monitor methane, monitoring and measurements of high-emission methane hot spots in the state using the best available and cost-effective scientific and technical methods”. Hence this project is directly responsive to that policy by addressing methane hot spots (super-emitters), by establishing a close collaboration between local, state, and US national stakeholders and by applying the best available scientific methods (including remote sensing derived point
flux estimates and integration with other data sets across multiple spatial scales and emission sectors).
The planned use for this data set by stakeholders spans multiple governance levels, emission sectors and programs – ranging from EPA Region 9’s interest in livestock emissions under the EPA AgStar program (the largest methane emission sector in California) to SC-AQMD’s focus on landfills, oil and gas (both for methane and potential co-emitted criteria pollutants). Similarly, the proposed end-to-end, multi-scale approach will also help explore and path-find the potential future extensibility of these methods to other regions in the US and internationally – addressing key US national priorities (US- Canada Joint Statement, 2016; President’s Climate Action Plan, 2013). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Bart Croes, California Energy Commission / California Air Resources Board (retired) / CIRES at University of Colorado-Boulder | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Carranza, V., Rafiq, T., Frausto-Vicencio, I., Hopkins, F. M., Verhulst, K. R., Rao, P., Duren, R. M., Miller, C. E. 2018. Vista-LA: Mapping methane-emitting infrastructure in the Los Angeles megacity. Earth System Science Data. 10(1), 653-676. DOI: 10.5194/essd-10-653-2018 Cusworth, D. H., Duren, R. M., Thorpe, A. K., Tseng, E., Thompson, D., Guha, A., Newman, S., Foster, K. T., Miller, C. E. 2020. Using remote sensing to detect, validate, and quantify methane emissions from California solid waste operations. Environmental Research Letters. 15(5), 054012. DOI: 10.1088/1748-9326/ab7b99 Cusworth, D. H., Duren, R. M., Yadav, V., Thorpe, A. K., Verhulst, K., Sander, S., Hopkins, F., Rafiq, T., Miller, C. E. 2020. Synthesis of Methane Observations Across Scales: Strategies for Deploying a Multitiered Observing Network. Geophysical Research Letters. 47(7). DOI: 10.1029/2020GL087869 Cusworth, D. H., Thorpe, A. K., Ayasse, A. K., Stepp, D., Heckler, J., Asner, G. P., Miller, C. E., Yadav, V., Chapman, J. W., Eastwood, M. L., Green, R. O., Hmiel, B., Lyon, D. R., Duren, R. M. 2022. Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States. Proceedings of the National Academy of Sciences. 119(38). DOI: 10.1073/pnas.2202338119 Duren, R. M., Thorpe, A. K., Foster, K. T., Rafiq, T., Hopkins, F. M., Yadav, V., Bue, B. D., Thompson, D. R., Conley, S., Colombi, N. K., Frankenberg, C., McCubbin, I. B., Eastwood, M. L., Falk, M., Herner, J. D., Croes, B. E., Green, R. O., Miller, C. E. 2019. California's methane super-emitters. Nature. 575(7781), 180-184. DOI: 10.1038/s41586-019-1720-3 Thorpe, A. K., Duren, R. M., Conley, S., Prasad, K. R., Bue, B. D., Yadav, V., Foster, K. T., Rafiq, T., Hopkins, F. M., Smith, M. L., Fischer, M. L., Thompson, D. R., Frankenberg, C., McCubbin, I. B., Eastwood, M. L., Green, R. O., Miller, C. E. 2020. Methane emissions from underground gas storage in California. Environmental Research Letters. 15(4), 045005. DOI: 10.1088/1748-9326/ab751d Thorpe, A. K., O'Handley, C., Emmitt, G. D., DeCola, P. L., Hopkins, F. M., Yadav, V., Guha, A., Newman, S., Herner, J. D., Falk, M., Duren, R. M. 2021. Improved methane emission estimates using AVIRIS-NG and an Airborne Doppler Wind Lidar. Remote Sensing of Environment. 266, 112681. DOI: 10.1016/j.rse.2021.112681 Yadav, V., Duren, R., Mueller, K., Verhulst, K. R., Nehrkorn, T., Kim, J., Weiss, R. F., Keeling, R., Sander, S., Fischer, M. L., Newman, S., Falk, M., Kuwayama, T., Hopkins, F., Rafiq, T., Whetstone, J., Miller, C. 2019. Spatio-temporally Resolved Methane Fluxes From the Los Angeles Megacity. Journal of Geophysical Research: Atmospheres. 124(9), 5131-5148. DOI: 10.1029/2018JD030062 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Carranza, V., T. Rafiq, I. Frausto-Vicencio, F. Hopkins, K.R. Verhulst, P. Rao, R.M. Duren, and C.E. Miller. 2018. Sources of Methane Emissions (Vista-LA), South Coast Air Basin, California, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1525
Hopkins, F.M., T. Rafiq, and R.M. Duren. 2019. Sources of Methane Emissions (Vista-CA), State of California, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1726 Thorpe, A.K., B.D. Bue, D.R. Thompson, and R.M. Duren. 2019. Methane Plumes Derived from AVIRIS-NG over Point Sources across California, 2016-2017. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1727 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Duren (CMS 2018) (2019) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Multi-tiered Carbon Monitoring System | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Riley Duren, Carbon Mapper/U. Arizona
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Duren (CMS 2015) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Cusworth (CMS 2022) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Reducing CH4 and fossil fuel CO2 emissions remains a top climate mitigation priority for stakeholders around the world. States such as California have committed to ambitious GHG stabilization targets. State agencies such as the California Air Resources Board (CARB) are strongly motivated to verify emissions and inform policy formulation at the scale of major emitting regions, air basins and cities. Additionally, private companies such as Chevron have expressed interest in better facility-scale emissions data to reduce their greenhouse gas footprints and product loss. Meanwhile several foundations such as the Rocky Mountain Institute (RMI) are working to establish trusted climate data initiatives through public-private partnerships. A common feature of these interests is a focus on facility-scale point source emitters and their contribution to local emission budgets to prioritize mitigation efforts. A common challenge is that CH4 and CO2 emissions data at those spatial scales is currently sparse, inaccurate or non-existent. There is an urgent need to provide CH4 and CO2 data and analytics that are trusted, timely and at spatial scales relevant to decision making. A tiered observational strategy and integrated data analysis framework have the potential to leverage emerging and planned airborne and satellite remote sensing capabilities to address these challenges and
stakeholder needs (ultimately in key regions globally).
We propose to build on the success of our Prototype Methane Monitoring System for California (CMS-2015-Duren) and Megacities Carbon Project to develop and test a Multi-tiered CH4 (and as a secondary goal: CO2) monitoring system for a broader set of high emitting regions and priority emission sectors in the US. In year 1 of the project we plan to conduct CH4 and CO2 point source surveys of key regions and sectors in California, the Permian basin in Texas and New Mexico, and major oil and gas infrastructure centers along the Gulf Coast with NASA’s Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-ng) together with coordinated snap-shot mode CO2 observations from the Orbiting Carbon Observatory-3 (OCO-3) and routine CH4 observations from the Sentinel-5 Precursor/TROPOMI satellite. In years 2 and 3 we will generate CH4 (goal: CO2) regional and point source emission estimates in those regions that leverage and extend multi-scale estimation techniques previously prototyped in California. This will allow stakeholders to place facility scale emissions into context with regional emissions. If selected, this project will benefit from additional funding from RMI to support more airborne surveys and data product development. It also benefits from in-kind contributions from other collaborators. Our stakeholders including RMI, CARB and Chevron have indicated an interest in evaluating and potentially adopting the methods, tools and data products developed by this project for infusion into their decision frameworks. Finally, we also plan to leverage existing surface measurements from our own Megacities Carbon Project and collaborators in the southern San Joaquin Valley and potentially the Permian basin, Salt Lake City and the Uintah Basin to help validate emission estimates. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Sebastien Biraud, Lawrence Berkeley National Laboratory | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | Carbon Mapper Data Portal | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Ayasse, A. K., Thorpe, A. K., Cusworth, D. H., Kort, E. A., Negron, A. G., Heckler, J., Asner, G., Duren, R. M. 2022. Methane remote sensing and emission quantification of offshore shallow water oil and gas platforms in the Gulf of Mexico. Environmental Research Letters. 17(8), 084039. DOI: 10.1088/1748-9326/ac8566 Cusworth, D. H., Duren, R. M., Thorpe, A. K., Olson-Duvall, W., Heckler, J., Chapman, J. W., Eastwood, M. L., Helmlinger, M. C., Green, R. O., Asner, G. P., Dennison, P. E., Miller, C. E. 2021. Intermittency of Large Methane Emitters in the Permian Basin. Environmental Science & Technology Letters. 8(7), 567-573. DOI: 10.1021/acs.estlett.1c00173 Cusworth, D. H., Duren, R. M., Thorpe, A. K., Pandey, S., Maasakkers, J. D., Aben, I., Jervis, D., Varon, D. J., Jacob, D. J., Randles, C. A., Gautam, R., Omara, M., Schade, G. W., Dennison, P. E., Frankenberg, C., Gordon, D., Lopinto, E., Miller, C. E. 2021. Multisatellite Imaging of a Gas Well Blowout Enables Quantification of Total Methane Emissions. Geophysical Research Letters. 48(2). DOI: 10.1029/2020GL090864 Cusworth, D. H., Thorpe, A. K., Ayasse, A. K., Stepp, D., Heckler, J., Asner, G. P., Miller, C. E., Yadav, V., Chapman, J. W., Eastwood, M. L., Green, R. O., Hmiel, B., Lyon, D. R., Duren, R. M. 2022. Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States. Proceedings of the National Academy of Sciences. 119(38). DOI: 10.1073/pnas.2202338119 Hmiel, B., Lyon, D. R., Warren, J. D., Yu, J., Cusworth, D. H., Duren, R. M., Hamburg, S. P. 2023. Empirical quantification of methane emission intensity from oil and gas producers in the Permian basin. Environmental Research Letters. 18(2), 024029. DOI: 10.1088/1748-9326/acb27e Lauvaux, T., Giron, C., Mazzolini, M., d'Aspremont, A., Duren, R., Cusworth, D., Shindell, D., Ciais, P. 2022. Global assessment of oil and gas methane ultra-emitters. Science. 375(6580), 557-561. DOI: 10.1126/science.abj4351 Sherwin, E. D., Rutherford, J. S., Zhang, Z., Chen, Y., Wetherley, E. B., Yakovlev, P. V., Berman, E. S. F., Jones, B. B., Cusworth, D. H., Thorpe, A. K., Ayasse, A. K., Duren, R. M., Brandt, A. R. 2024. US oil and gas system emissions from nearly one million aerial site measurements. Nature. 627(8003), 328-334. DOI: 10.1038/s41586-024-07117-5 Thorpe, A. K., O'Handley, C., Emmitt, G. D., DeCola, P. L., Hopkins, F. M., Yadav, V., Guha, A., Newman, S., Herner, J. D., Falk, M., Duren, R. M. 2021. Improved methane emission estimates using AVIRIS-NG and an Airborne Doppler Wind Lidar. Remote Sensing of Environment. 266, 112681. DOI: 10.1016/j.rse.2021.112681 Yadav, V., Verhulst, K., Duren, R. M., Thorpe, A. K., Kim, J., Keeling, R., Weiss, R., Cusworth, D. H., Mountain, M., Miller, C. E., Whetstone, J. 2023. A declining trend of methane emissions in the Los Angeles Basin from 2015 to 2020. Environmental Research Letters. DOI: 10.1088/1748-9326/acb6a9 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Elvidge (CMS 2015) (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Global monitoring, reporting, and verification (MRV) system for carbon emissions from natural gas flaring | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Christopher (Chris) Elvidge, Colorado School of Mines
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
NOAA has developed a prototype MRV (monitoring, reporting and verification) system for global gas flaring. The purpose of this project is to reduce the uncertainties in the carbon emission estimates and produce a consistent time series of annual CO2 emission estimates for individual flare sites spanning 2012 through 2018. The monitoring system is based near-infrared and short-wave infrared nighttime data collected by the Visible Infrared Imaging Radiometer Suite (VIIRS). Peak radiant emissions from gas flares occur near 1.62 um - center of the VIIRS M11 spectral band. Using detections in multiple spectral bands, the algorithm calculates temperature, source size and radiant heat. Flares are separated from biomass burning and industrial sites based on temperature and persistence. More than 7000 flares were found each year in 2012-2014. Fire each flaring site, annual average radiant heat is calculated from the cloud-free observation set. The current calibration is based on national level flaring data reported by Cedigaz. The uncertainty in the current estimates exceeds the year-to-year differences in flared gas volumes from individual countries, calling into question the estimates. It is believed that the large uncertainties arise from country level errors in the Cedigaz estimates.
Methods: Nighttime VIIRS data will be collected on a series of test flares burning a precisely controlled natural gas flow rates. Measurements will be made over a range of view angles and three flow rates (low, medium and high). Additional test flare events will explore the effects of multiple flares inside a VIIRS pixel and the effects of black carbon. From this test set, a new calibration will be developed for estimating flared gas volumes. The calibration will then be applied to VIIRS data spanning 2012-2018 resulting in both site specific and national estimates of CO2 emissions from natural gas flaring.
Significance: The project meets on of the primary calls in the announcement – for proposals to develop MRV systems using remotely sensed data. There are three primary applications for the gas flaring MRV:

A. Emission reductions to meet Intended Nationally Determined Contributions (INDC): Countries need to have historical records and annual updates of their CO2 emissions from gas flaring. The data will be used to gauge the level of effort to be placed on gas flaring reduction. For countries with large flaring emissions, reductions in flaring may be enough to meet their INDC. Other countries with small flaring volumes may decide to focus their efforts on achieving their INDC targets in other sectors. Accurate gas flaring emission data are key to these decisions. The MRV data will also be used to document the INDC emission reductions from gas flaring.
B. Zero Routine Flaring by 2013: The gas flaring MRV data are crucial this initiative. The MRV data will be used to identify the routine flares. This will likely be done based on duty cycle. Certainly flares detected 50-100% of the time are routine. As the duty cycle declines, at some point the flare will be deemed to be œnon-routine. The VIIRS data can be used to distinguish routine versus non- routine flaring once a decision has been made on the duty cycle threshold. For the routine flares, these can be tracked over time to document changes indicating the flare has been extinguished or converted to non-routine status.
C. Low Carbon Fuel Standards (LCFS): Site specific MRV data can be assigned to specific production fields as one of the data sources used to calculate the carbon intensity of fuels. This approach can be used to establish flaring baseline for specific production fields and tracking of changes in flaring that count towards carbon emission reductions. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Kimberly Baugh, University of Colorado | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Elvidge, C. D., Bazilian, M. D., Zhizhin, M., Ghosh, T., Baugh, K., Hsu, F. 2018. The potential role of natural gas flaring in meeting greenhouse gas mitigation targets. Energy Strategy Reviews. 20, 156-162. DOI: 10.1016/j.esr.2017.12.012 Elvidge, C., Zhizhin, M., Baugh, K., Hsu, F., Ghosh, T. 2015. Methods for Global Survey of Natural Gas Flaring from Visible Infrared Imaging Radiometer Suite Data. Energies. 9(1), 14. DOI: 10.3390/en9010014 Elvidge, C., Zhizhin, M., Baugh, K., Hsu, F., Ghosh, T. 2019. Extending Nighttime Combustion Source Detection Limits with Short Wavelength VIIRS Data. Remote Sensing. 11(4), 395. DOI: 10.3390/rs11040395 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Elvidge, C.D., and M. Zhizhin. 2021. Global Gas Flare Survey by Infrared Imaging, VIIRS Nightfire, 2012-2019. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1874
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| Escobar (CMS 2015) (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | CMS Applications: Stakeholder Engagement and Analysis of CMS Data Products in Decision Making and Policy Frameworks | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Vanessa Escobar, NASA GSFC / SSAI
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Escobar (CMS 2013) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Poulter (CMS 2018) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Our team seeks to build upon and expand the current Carbon Monitoring System (CMS) Applications project (Escobar-2013) that assesses, identifies, and appropriately links existing decision support processes and policies to CMS carbon science. This CMS Applications effort aims to serve as a vehicle for facilitating and translating critical NASA science into decision support systems, establishing the science maturity and application readiness for NASA Headquarters and clearly stating the impact of CMS science to society for Congress. The partnerships fostered though this effort will lead to better utilization of NASA CMS data products, in turn leading to positive financial and societal outcomes.
Our proposed work is highly relevant to the following activities listed as priorities for this call:
1) understanding and engaging the user community for carbon monitoring products;
2) evaluating current and planned NASA CMS products with regard to their value for decision making by identified users and to assist in having existing products
used for stakeholder activities;
3) conducting MRV-related work in support of international REDD or REDD+
projects, as well as studies of stakeholder interests;
4) assessing the NASA CMS applications program though a 'lessons learned'
document which evaluates the number of potential and actual CMS data users.

During the next phase of funding our team will focus efforts on several fronts. First, we will continue to develop the translation tools created during the Escobar-2013 project, and relate CMS product capabilities to stakeholder needs through the use tutorials, short science articles, white papers, and policy briefs. that identify thematic opportunities, identify data gaps and sync the CMS science research to the beneficiary of the data (stakeholder). Workshops events and the CMS Policy Speaker Series will continue to serve as tools for highlighting carbon relevant policies and identifying the science needs of operational organizations. Furthermore, we will develop a systematic evaluation of these workshops and policy series with follow-up surveys and reports in an effort to assess the societal relevance of our activities.
Our team will conduct science policy bridging with organizations such as (but not limited to) RGGI, EPA, USGS, 3DEP, USGCRP, Chesapeake Bay Restoration Program, USDA Environmental Markets and the Department of Natural Resources for Maryland, Delaware, Pennsylvania and Sonoma County, CA and USGCRP. These partners were identified in the Escobar 2013 Applications work and a clear understanding of their needs and objectives will be expanded on for the 2016 efforts. We will also collaborate closely with the Carbon Cycle Interagency Working Group of the U.S Global Change Research Program, and contribute to carbon-related reports, such as the SOCCR-2 and the Fourth National Climate Assessment.
Of equal importance is merging the diversity of the CMS Initiative with ongoing and future NASA missions and programs. These cross-mission collaborations are essential for broadening the reach and relevance of CMS science. The proposed CMS Applications effort will leverage opportunities with newer missions like SMAP and OCO-2 while also planning for future synergy with ICESat-2, NISAR and ASCENDS.
Finally, research to assess the impact and value of the CMS data in specific case studies will be conducted in collaboration with the Joint Global Change Research Institute (JGCRI), collaboration between the U.S DoE Pacific Northwest National Laboratory (PNNL) and the University of Maryland at College Park. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Phillip Abbott, Purdue University | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Brown, M. E., Cooper, M. W., Griffith, P. C. 2020. NASA's carbon monitoring system (CMS) and arctic-boreal vulnerability experiment (ABoVE) social network and community of practice. Environmental Research Letters. 15(11), 115014. DOI: 10.1088/1748-9326/aba300 Brown, M. E., Ihli, M., Hendrick, O., Delgado-Arias, S., Escobar, V. M., Griffith, P. 2016. Social network and content analysis of the North American Carbon Program as a scientific community of practice. Social Networks. 44, 226-237. DOI: 10.1016/j.socnet.2015.10.002 Kaushik, A., Graham, J., Dorheim, K., Kramer, R., Wang, J., Byrne, B. 2020. The Future of the Carbon Cycle in a Changing Climate. Eos. 101. DOI: 10.1029/2020EO140276 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Fatoyinbo (CMS 2015) (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Future Mission Fusion for High Biomass Forest Carbon Accounting | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Temilola (Lola) Fatoyinbo, NASA GSFC
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Objectives
The primary objectives of our research are: (1) To independently quantify the relationship between biomass density and expected error from GEDI, NISAR and ICESat-2 in high AGB forests in Sonoma County, Costa Rica, and Gabon; (2) To identify the sources of error in high biomass forests for each mission, including from field estimates (GPS error, allometry), from errors in the airborne/spaceborne data (penetration to the ground), and from errors in empirical modeling; (3) To assess data fusion techniques in order to increase the accuracy of AGB estimation through the integration of the airborne simulators for the three missions; (4) To provide AGB stock and error maps to local stakeholders through a user-friendly web portal, enabling the estimation of total AGB and expected error specifically within areas of local interest.
Methods/Techniques
The proposed research focuses on establishing the relationship between AGB density and estimation error for each of three future active remote sensing NASA missions using three study areas with high AGB forests. We propose to use existing airborne datasets that have been collected over forests in Gabon, Costa Rica, and Sonoma County, and to process these datasets to simulate NISAR, ICESAT-2, and GEDI.

Field data have already been collected in all three study sites. New field data will also be collected in particularly high biomass areas of Sonoma County. Finally, Terrestrial Laser Scanning (TLS) data will be collected in Sonoma County, as well as provided to the research team from existing collections in Gabon. This TLS data will quantify existing or expected errors in field estimates of AGB.
LVIS and discrete return Airborne Laser Scanning (ALS) data are the data sources used to simulate GEDI, through a GEDI waveform simulator already under development at the University of Maryland. LVIS data has already been collected in Costa Rica and Gabon, and ALS has been collected in Sonoma County. ALS data will also be used to simulate ICESAT-2’s ATLAS dataset, through a photon counting simulation already tested using ALS data in Gabon. This simulation will be expanded to Sonoma County. Finally, UAVSAR will be used to simulate NISAR. Metrics gleaned from each simulation product will be used to build mission-independent AGB stock and error models for each of the three datasets.
Finally, a prototype design for future mission fusion will be developed to capitalize on the three independent sets of structural observations from GEDI, ICESAT-2 and NISAR. All AGB and error maps will be provided to local stakeholders via a cloud-based GIS software package, Ecometrica, which will enable the manipulation of maps to perform carbon accounting for locally relevant land management activities.
Perceived Significance
Through comparing future mission utility on a shared set of field observations, the proposed research will provide a precise and comparable quantification of expected errors from GEDI, ICESAT-2, and NISAR in high AGB forests. Additionally, methods will be tested to fuse these three future datasets with the intention of developing best practices for AGB and error MRV. By working with scientists from each of the three missions’science teams, this research will provide an unbiased analysis of the strengths and weaknesses of the future missions and inform the development of the next generation of NASA active RS instruments. Additionally, by working with local stakeholders both in the US and abroad, the proposed research will facilitate knowledge and data transfer from data developers to data users in the hopes that best practices can be developed to optimize the utility of future missions products for carbon monitoring initiatives, such as REDD+. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Mathias Disney, University College London | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Disney, M., Burt, A., Wilkes, P., Armston, J., Duncanson, L. 2020. New 3D measurements of large redwood trees for biomass and structure. Scientific Reports. 10(1). DOI: 10.1038/s41598-020-73733-6 Duncanson, L., Armston, J., Disney, M., Avitabile, V., Barbier, N., Calders, K., Carter, S., Chave, J., Herold, M., Crowther, T. W., Falkowski, M., Kellner, J. R., Labriere, N., Lucas, R., MacBean, N., McRoberts, R. E., Meyer, V., Naesset, E., Nickeson, J. E., Paul, K. I., Phillips, O. L., Rejou-Mechain, M., Roman, M., Roxburgh, S., Saatchi, S., Schepaschenko, D., Scipal, K., Siqueira, P. R., Whitehurst, A., Williams, M. 2019. The Importance of Consistent Global Forest Aboveground Biomass Product Validation. Surveys in Geophysics. 40(4), 979-999. DOI: 10.1007/s10712-019-09538-8 Duncanson, L., Neuenschwander, A., Hancock, S., Thomas, N., Fatoyinbo, T., Simard, M., Silva, C. A., Armston, J., Luthcke, S. B., Hofton, M., Kellner, J. R., Dubayah, R. 2020. Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California. Remote Sensing of Environment. 242, 111779. DOI: 10.1016/j.rse.2020.111779 Silva, C. A., Duncanson, L., Hancock, S., Neuenschwander, A., Thomas, N., Hofton, M., Fatoyinbo, L., Simard, M., Marshak, C. Z., Armston, J., Lutchke, S., Dubayah, R. 2021. Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping. Remote Sensing of Environment. 253, 112234. DOI: 10.1016/j.rse.2020.112234 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Duncanson, L., R.O. Dubayah, J. Armston, M. Liang, A. Arthur, and D. Minor. 2020. CMS: LiDAR Biomass Improved for High Biomass Forests, Sonoma County, CA, USA, 2013. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1764
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| Fatoyinbo (CMS 2016) (2017) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Estimating Total Ecosystem Carbon in Blue Carbon and Tropical Peatland Ecosystems | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Temilola (Lola) Fatoyinbo, NASA GSFC
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Fatoyinbo (CMS 2014) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
The proposed research focuses on the application and further development of C Stock mapping and estimation from multiple satellite and airborne remote sensing platforms, with a focus on mangrove and tropical peatland forests. One main goal of this proposed project is to develop a standardized MRV (monitoring, reporting and verification) methodology that incorporates canopy height measurements from multiple Remote Sensing sources (TanDEM-X, High Resoluiton stereo, Lidar where available) to estimate extent, stocks and both spatial and vertical changes that can be incorporated and approved not only for scientific applications, but also for MRV (monitoring, reporting, verification) in voluntary Carbon markets. Here we propose to help advance the field of carbon standards and MRV methodologies, by incorporating the new set of methods using active InSAR, Polarimetric InSAR, Lidar and optical stereo data. We will also propose to begin the integration of remote sensing observations of forest canopy height and biomass within a mangrove ecosystem model to advance the Tier-3 certification process for tropical forest wetlands.
Mangrove and Peatland forests are experiencing rapid decline, either through land conversion for commodity production (aquaculture, rice, oil palm), unsustainable harvesting for timber and charcoal, or poor management. To counter this trend, a large focus is in restoration and reforestation and the determining what types of observations are required to monitor the successful regeneration of forests. Thus, we also propose to further our ongoing work in estimating mangrove forest rates of change from TanDEM-X and Very High Resolution Stereo data 1) to monitor and evaluate the efficacy of existing mangrove and peatland restoration projects and 2) provide quantitative historical data on mangrove and adjoining peatland forest extent that will aid in development phase of planned restoration projects.
One main goal of this proposed project is to develop a standardized MRV (monitoring, reporting and verification) methodology that incorporates canopy height measurements from multiple RS sources (TanDEM-X, VHRS, Lidar or other data) to estimate extent, stocks and both spatial and vertical changes that can be incorporated and approved not only for scientific applications, but also for MRV in voluntary Carbon markets.
The objectives for this proposed project are:
1. Reduce the uncertainty and increase the Application Readiness Level (ARL) of mangrove and peatland forest extent, vertical structure and change (gain, loss, growth rates) maps in Africa and South-East Asia using multi-sensor data
2. Improve total carbon stock estimates and emissions for mangroves and peatland forests using forest vertical structure and relationships of soil C with geophysical factors, with propagated sources and estimates of error.
3. Prototype the development of MRV systems for mangrove forests that are compliant with IPCC Tier 3 emissions through the integration of remote sensing observations of forest canopy height into a NPP model that allocates carbon increment to specific C pools.
4. Develop a MRV Certification Prototypes for Mangrove and Peatlands that advances more traditional MRV methods to include forest structure from multiple remotely sensed datasets.
This project is an extension of a current CMS project ending in 2017 (CMS I) focused on Total C estimation in Blue Carbon ecosystems (specifically mangroves) in three countries of Africa Gabon, Tanzania, Mozambique. We will expand the current geographical focus of the project to coastal areas in West Africa and South-East Asia. In addition, we are also expanding our focus from mangroves, to adjoining tropical freshwater peat forests (primarily in Indonesia, but also in Ghana). Our collaborators and stakeholders are existing REDD project developers in Asia and Africa, sustainable logging companies, Universities and International Biodiversity and conservation projects. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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|
Participants: |
Anthony Campbell, NASA GSFC / UMBC | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Campbell, A. D., Fatoyinbo, L., Goldberg, L., Lagomasino, D. 2022. Global hotspots of salt marsh change and carbon emissions. Nature. DOI: 10.1038/s41586-022-05355-z Campbell, A. D., Fatoyinbo, T., Charles, S. P., Bourgeau-Chavez, L. L., Goes, J., Gomes, H., Halabisky, M., Holmquist, J., Lohrenz, S., Mitchell, C., Moskal, L. M., Poulter, B., Qiu, H., Resende De Sousa, C. H., Sayers, M., Simard, M., Stewart, A. J., Singh, D., Trettin, C., Wu, J., Zhang, X., Lagomasino, D. 2022. A review of carbon monitoring in wet carbon systems using remote sensing. Environmental Research Letters. 17(2), 025009. DOI: 10.1088/1748-9326/ac4d4d Fatoyinbo, T., Feliciano, E. A., Lagomasino, D., Lee, S. K., Trettin, C. 2018. Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta. Environmental Research Letters. 13(2), 025012. DOI: 10.1088/1748-9326/aa9f03 Lagomasino, D., Fatoyinbo, T., Lee, S., Feliciano, E., Trettin, C., Shapiro, A., Mangora, M. M. 2019. Measuring mangrove carbon loss and gain in deltas. Environmental Research Letters. 14(2), 025002. DOI: 10.1088/1748-9326/aaf0de Lee, S., Fatoyinbo, T. E., Lagomasino, D., Feliciano, E., Trettin, C. 2018. Multibaseline TanDEM-X Mangrove Height Estimation: The Selection of the Vertical Wavenumber. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11(10), 3434-3442. DOI: 10.1109/JSTARS.2018.2835647 Mondal, P., Liu, X., Fatoyinbo, T. E., Lagomasino, D. 2019. Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa. Remote Sensing. 11(24), 2928. DOI: 10.3390/rs11242928 Simard, M., Fatoyinbo, L., Smetanka, C., Rivera-Monroy, V. H., Castaneda-Moya, E., Thomas, N., Van der Stocken, T. 2018. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience. 12(1), 40-45. DOI: 10.1038/s41561-018-0279-1 Thomas, N., Bunting, P., Lucas, R., Hardy, A., Rosenqvist, A., Fatoyinbo, T. 2018. Mapping Mangrove Extent and Change: A Globally Applicable Approach. Remote Sensing. 10(9), 1466. DOI: 10.3390/rs10091466 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Simard, M., T. Fatoyinbo, C. Smetanka, V.H. Rivera-monroy, E. Castaneda, N. Thomas, and T. Van der stocken. 2019. Global Mangrove Distribution, Aboveground Biomass, and Canopy Height. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1665
Goldberg, L., D. Lagomasino, N. Thomas, and T. Fatoyinbo. 2022. Global Mangrove Loss Extent, Land Cover Change, and Loss Drivers, 2000-2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1768 Campbell, A., T. Fatoyinbo, and L. Goldberg. 2022. Global Salt Marsh Change, 2000-2019. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2122 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Goes (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||
| Project Title: | Bio-Optical Monitoring and Evaluation System (BIOMES) for improving satellite estimates of Ocean Net Primary Production for Carbon Cycling and Climate Change studies | |||||||||||||||||||||||||||||||||||||
|
Science Team |
Joaquim Goés, Lamont-Doherty Earth Observatory
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||
| Abstract: |
One of the overarching goals of NASA’s CMS program is to promote and enhance the utility of its presently available and planned space based assets for better understanding of our planet’s carbon cycle and interactions among its atmospheric, terrestrial and aquatic carbon components. An essential requirement of this program is that sensors aboard these missions provide well calibrated, long-time series products of the highest quality and accuracy. For ocean ecosystems, accurate and well-characterized basin and global scale measurements of oceanic net primary production (NPP) are central to understanding the ocean carbon cycle, and the role and response of ocean ecosystems to rising CO2 levels. Despite considerable progress, current satellite oceanic NPP products continue to be beleaguered by large uncertainties, in large part, because most NPP models rely on inputs that are based on ‘one-size-fits-all’ algorithms. Additionally, most biogeochemical province-based approaches that are used for scaling-up of limited shipboard measurements from local to basin and global scales are coarse in their resolution and are incapable of capturing sub-mesoscale oceanographic features visible in higher-resolution satellite imagery.
In this study, we propose a way forward to improve satellite based NPP measurements with assessments of uncertainties, through development of a novel Biooptical Monitoring and Evaluation System (BIOMES) that offers a more sophisticated and pragmatic approach for extending local shipboard measurements of NPP to regional and basin scales. BIOMES relies on an Optical-Biogeochemical Classification (O-BGC) scheme developed by us. It uses multi-platform, multi-sensor satellite data, model outputs and in-situ data to partition the oceans into dynamic provinces that capture sub-mesoscale features that are often overlooked as separate biogeochemical provinces in currently used approaches.
Our proposed study will leverage off: 1) several in-situ optical and bio-optical datasets including NPP data collected by our team, and by others, as part of programs previously supported by NASA, NOAA and other agencies, 2) in-situ bio-optical, phytoplankton photo-physiology and NPP data planned for collection during our upcoming NOAA-VIIRS Cal/Val cruises in 2019 and 2020, as well as data from the EXPORTS-Phase 1 cruises that have more intensive NPP measurement plans. Our goal is to develop BIOMES as a template for future ocean carbon cycle study cruises, ensuring that each oceanic biogeochemical province discriminated by BIOMES is adequately sampled. This is an essential step moving forward for improving NPP estimates from space. Satellite-based NPP and other products achieved by this study with associated uncertainty assessments will allow for more accurate assessments of the oceans’ role in the global carbon cycle.
Additional contributions to NASA CMS program that would result from this study are as follows: a) a comprehensive compilation of measurements of inherent optical properties [IOPs, including phytoplankton absorption and particle scattering], b) a compilation of biogeochemical stocks [phytoplankton functional types (PFTs), phytoplankton size classes (PSCs), phytoplankton pigments], c) NPP and photophysiological rate parameters, across a variety of ecosystem states, that would be valuable for augmenting NASA’s SeaWiFS Bio-Optical Archive Storage System (SeaBASS) and for fine-tuning of algorithms for NPP and other ocean color standard products at relevant measurement scales applicable to current (MODIS-Aqua, SNPPVIIRS) and future Plankton Aerosols Cloud and Ecosystems (PACE) NASA missions. Our planned study is responsive to NASA ROSES NNH18ZDA001N-CMS. Our eventual plan is to ensure that the framework for O-BGC and BIOMES are easily transferable to other ocean color missions, as an effective means to generate NPP. PIs Goes and Wei are requesting membership in the CMS Science Team. | |||||||||||||||||||||||||||||||||||||
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Participants: |
Jason Bordoff, Columbia University Centre on Global Energy Policy | |||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Campbell, A. D., Fatoyinbo, T., Charles, S. P., Bourgeau-Chavez, L. L., Goes, J., Gomes, H., Halabisky, M., Holmquist, J., Lohrenz, S., Mitchell, C., Moskal, L. M., Poulter, B., Qiu, H., Resende De Sousa, C. H., Sayers, M., Simard, M., Stewart, A. J., Singh, D., Trettin, C., Wu, J., Zhang, X., Lagomasino, D. 2022. A review of carbon monitoring in wet carbon systems using remote sensing. Environmental Research Letters. 17(2), 025009. DOI: 10.1088/1748-9326/ac4d4d Goes, J. I., Tian, H., Gomes, H. D. R., Anderson, O. R., Al-Hashmi, K., deRada, S., Luo, H., Al-Kharusi, L., Al-Azri, A., Martinson, D. G. 2020. Ecosystem state change in the Arabian Sea fuelled by the recent loss of snow over the Himalayan-Tibetan Plateau region. Scientific Reports. 10(1). DOI: 10.1038/s41598-020-64360-2 Wu, J., Lee, Z., Xie, Y., Goes, J., Shang, S., Marra, J. F., Lin, G., Yang, L., Huang, B. 2021. Reconciling Between Optical and Biological Determinants of the Euphotic Zone Depth. Journal of Geophysical Research: Oceans. 126(5). DOI: 10.1029/2020JC016874 | |||||||||||||||||||||||||||||||||||||
| Greenberg (CMS 2016) (2017) | |||
| Project Title: | Three dimensional change detection of aboveground biomass | ||
|
Science Team |
Jonathan Greenberg, University of Nevada
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | ||
| Precursor Projects: | Greenberg (CMS 2014) | ||
| Abstract: |
We propose to create a Carbon Monitoring Systems (CMS) with the goal of estimating three- dimensional changes in forest biomass over a variety of different disturbance regimes using a suite of different remotely sensed data including airborne and terrestrial LiDAR, and UAV and ground-based multi-angle digital imagery processed using structure-from-motion techniques. In collaboration with the US Forest Service, we will collect these data before and after a disturbance, to determine the sensitivity of these technologies to accumulation and loss of biomass in the overstory and understory. We believe this work will lead to decreasing uncertainties in estimating changes in biomass, as well as providing important information on disturbance risk and successional dynamics that can impact long-term ecosystem processes. | ||
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|
Participants: |
John Armston, University of Maryland | ||
| Project URL(s): | None provided. | ||
| Data Products: | None provided. | ||
| Publications: | None provided. | ||
| Outreach Activities: |
Wired Magazine did an article about wildfires and used data/graphics we developed from our recent CMS project:
https://www.wired.com/story/how-supercomputers-can-help-fix-our-wildfire-problem/
(the TLS scan at the bottom was funded by CMS). | ||
| Guan (CMS 2016) (2017) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Improving the monitoring capability of carbon budget for the US Corn Belt - integrating multi-source satellite data with improved land surface modeling and atmospheric inversion | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Kaiyu Guan, University of Illinois
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
With rising demands of food and fiber from a growing global population, agricultural landscape plays an increasingly important role in the global carbon cycle. Cropland also represents one of the biggest opportunities for carbon sequestration. Accurate quantification of regional scale cropland carbon cycling is critical for designing effective policies and management practices that can contribute to stabilizing atmospheric CO2 concentrations. A comprehensive carbon monitoring system should include the
integration of bottom-up and top-down estimates of carbon flux. However, the current cropland-based carbon monitoring systems face the following challenges: (1) they primarily focus on bottom-up approaches, with lack of integration and cross-verification between bottom-up and top-down approaches; (2) they are lack of spatially explicit characterization in either bottom-up process-based models or top-down atmospheric inversions. Novel satellite data (including Solar Induced Chlorophyll Fluorescence and atmospheric column-average CO2) and other existing NASA satellite data provide unique opportunities in addressing these challenges and improving both bottom-up and top-down approaches.
Here we propose one of the first Carbon Monitoring Systems (CMS) that will integrate both bottom-up and top-down approaches to jointly quantify the carbon budget for the US Corn Belt. The proposal plans to achieve three major improvements for bottom-up and top-down approaches (Task 1-3), with Task 4 to integrate and synthesize results from the two approaches to generate a consistent US Corn Belt carbon flux product including a thorough uncertainty assessment, covering the period of 2007 to 2017. Specifically, the four tasks are:
● Task 1 (Bottom-up approach - inventory/satellite): Combine USDA crop statistics- based and satellite-based solar-induced fluorescence (GOME-2 and OCO-2) to generate an improved 10 km carbon budget inventory (NPP, GPP, and Ra) for the US Corn Belt. ● Task 2 (Bottom-up approach - modeling/satellite): Assimilate multi-sources of satellite data (MODIS LAI, SMAP soil moisture) and newly derived crop inventory data (from Task 1) into the CLM-APSIM framework, to explicitly constrain the crop parameters in space and improve carbon budget simulation.
● Task 3 (Top-down approach - satellite/in-situ): Use satellite and in situ data together to solve for CO2 fluxes at high-resolution in a regional inversion over the US Corn Belt.
● Task 4 (Bottom-up/top-down integration): Integrate bottom-up and top-down approaches to jointly constrain the carbon budget, cross-verify estimates and provide robust uncertainty characterization.
This current proposal targets at the 2nd Research Topic that is listed in the NASA CMS solicitation, i.e. “Studies that address research needs to advance remote sensing-based approaches to monitoring, reporting, and verifications.” The proposed project directly addresses NASA’s strategic goal for the Earth Science to “study planet Earth from space to advance scientific understanding and meet societal needs”. The project will fully utilize the SIF and XCO2 retrievals from the new NASA satellite OCO-2 as well as the data from other existing NASA satellite products (e.g. from SMAP, MODIS, CERES and Landsat-based Crop Data Layer) to develop improved carbon flux estimations from bottom up approaches (inventory-satellite based and process-model based) and top-down approaches (jointly using satellite and in situ data in the atmospheric inversion). Public and private sectors can use this product to inform agricultural productivity and managements, which would further realize the value of NASA data. This effort thus carries a great promise to further constrain the regional and global carbon cycle, and also to directly address one of NASA’s key scientific questions for Earth System Science: “How will carbon cycle dynamics and ecosystem change in the future?” | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Caroline Alden, University of Colorado | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Cai, Y., Guan, K., Peng, J., Wang, S., Seifert, C., Wardlow, B., Li, Z. 2018. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sensing of Environment. 210, 35-47. DOI: 10.1016/j.rse.2018.02.045 DeLucia, E. H., Chen, S., Guan, K., Peng, B., Li, Y., Gomez-Casanovas, N., Kantola, I. B., Bernacchi, C. J., Huang, Y., Long, S. P., Ort, D. R. 2019. Are we approaching a water ceiling to maize yields in the United States? Ecosphere. 10(6). DOI: 10.1002/ecs2.2773 Jiang, C., Guan, K., Pan, M., Ryu, Y., Peng, B., Wang, S. 2020. BESS-STAIR: a framework to estimate daily, 30 m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt. Hydrology and Earth System Sciences. 24(3), 1251-1273. DOI: 10.5194/hess-24-1251-2020 Jiang, C., Guan, K., Wu, G., Peng, B., Wang, S. 2021. A daily, 250 m and real-time gross primary productivity product (2000-present) covering the contiguous United States. Earth System Science Data. 13(2), 281-298. DOI: 10.5194/essd-13-281-2021 Kimm, H., Guan, K., Gentine, P., Wu, J., Bernacchi, C. J., Sulman, B. N., Griffis, T. J., Lin, C. 2020. Redefining droughts for the U.S. Corn Belt: The dominant role of atmospheric vapor pressure deficit over soil moisture in regulating stomatal behavior of Maize and Soybean. Agricultural and Forest Meteorology. 287, 107930. DOI: 10.1016/j.agrformet.2020.107930 Kimm, H., Guan, K., Jiang, C., Peng, B., Gentry, L. F., Wilkin, S. C., Wang, S., Cai, Y., Bernacchi, C. J., Peng, J., Luo, Y. 2020. Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data. Remote Sensing of Environment. 239, 111615. DOI: 10.1016/j.rse.2019.111615 Luo, Y., Guan, K., Peng, J., Wang, S., Huang, Y. 2020. STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product. Remote Sensing. 12(19), 3209. DOI: 10.3390/rs12193209 Peng, B., Guan, K., Pan, M., Li, Y. 2018. Benefits of Seasonal Climate Prediction and Satellite Data for Forecasting U.S. Maize Yield. Geophysical Research Letters. 45(18), 9662-9671. DOI: 10.1029/2018GL079291 Peng, B., Guan, K., Tang, J., Ainsworth, E. A., Asseng, S., Bernacchi, C. J., Cooper, M., Delucia, E. H., Elliott, J. W., Ewert, F., Grant, R. F., Gustafson, D. I., Hammer, G. L., Jin, Z., Jones, J. W., Kimm, H., Lawrence, D. M., Li, Y., Lombardozzi, D. L., Marshall-Colon, A., Messina, C. D., Ort, D. R., Schnable, J. C., Vallejos, C. E., Wu, A., Yin, X., Zhou, W. 2020. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants. 6(4), 338-348. DOI: 10.1038/s41477-020-0625-3 Peng, B., Guan, K., Zhou, W., Jiang, C., Frankenberg, C., Sun, Y., He, L., Kohler, P. 2020. Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction. International Journal of Applied Earth Observation and Geoinformation. 90, 102126. DOI: 10.1016/j.jag.2020.102126 Rastogi, B., Miller, J. B., Trudeau, M., Andrews, A. E., Hu, L., Mountain, M., Nehrkorn, T., Mund, J., Guan, K., Alden, C. B. Evaluating consistency between total column CO<sub>2</sub> retrievals from OCO-2 and the in-situ network over North America: Implications for carbon flux estimation DOI: 10.5194/acp-2021-299 Urban, D., Guan, K., Jain, M. 2018. Estimating sowing dates from satellite data over the U.S. Midwest: A comparison of multiple sensors and metrics. Remote Sensing of Environment. 211, 400-412. DOI: 10.1016/j.rse.2018.03.039 Wang, C., Guan, K., Peng, B., Chen, M., Jiang, C., Zeng, Y., Wu, G., Wang, S., Wu, J., Yang, X., Frankenberg, C., Kohler, P., Berry, J., Bernacchi, C., Zhu, K., Alden, C., Miao, G. 2020. Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the U.S. Midwest. Remote Sensing of Environment. 241, 111728. DOI: 10.1016/j.rse.2020.111728 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Wu, G., K. Guan, H. Kimm, G. Miao, and C. Jiang. 2023. SIF and Vegetation Indices in the US Midwestern Agroecosystems, 2016-2021. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2136
Wang, C., K. Guan, B. Peng, C. Jiang, J. Peng, G. Wu, C. Frankenberg, P. Koehler, X. Yang, Y. Cai, and Y. Huang. 2021. High Resolution Land Cover-Specific Solar-Induced Fluorescence, Midwestern USA, 2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1813 Jiang, C., and K. Guan. 2020. MODIS-based GPP, PAR, fC4, and SANIRv estimates from SLOPE for CONUS, 2000-2019. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1786 Zhou, W., K. Guan, and B. Peng. 2023. Ecosys Model-Estimated Cropland Carbon Fluxes, Illinois, Indiana, and Iowa, 2001-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2125 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Healey (CMS 2016) (2017) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Piloting a GEDI-based Forest Carbon Monitoring, Reporting, and Verification Tool | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Sean Healey, USDA Forest Service
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
NASA's GEDI (Global Ecosystem Dynamics Investigation) mission will mount an innovative lidar instrument on the International Space Station; the mission will provide unprecedented detail about the structure of Earth’s forests. The number, quality, and international consistency of GEDI’s tree height measurements represent a matchless global tool for describing how much carbon our forests store and how that storage is affected by ecological change. However, the only biomass product GEDI is required (and currently funded) to produce is a 1km grid of estimated mean biomass (with standard errors). While there are important science applications for this grid, many scientists, landowners, and government agencies would benefit from easy access to GEDI-based biomass estimates over more flexible spatial domains. The GEDI Science Team (led by the PI of this proposal) has developed an approach to making 1km biomass estimates using sample theory applied to modeled observations of biomass made at each GEDI footprint (GEDI is not a wall-to-wall instrument). This approach accounts for both sampling uncertainty and biomass model error. There is no theoretical obstacle preventing this approach from being applied across areas defined by customized political, ownership, or ecological boundaries. This proposal, first, will pilot a web app that will support monitoring, reporting, and verification of local carbon storage (with uncertainty) over any spatial domain of interest, using exactly the same lidar data and sampling theory as the GEDI gridded product. This pilot application will be constructed in collaboration with the Forest Service FIA (Forest Inventory and Analysis) unit, which already maintains a national-to-local carbon monitoring system and has a legal mandate to improve the spatial detail at which forest characteristics can be reported. In addition to providing a potential long-term home for GEDI’s contribution to practical carbon monitoring, FIA will provide data the project will use to build validation case studies as well as to hone the community’s ability to use a single point-in-time lidar sample to study how changing forests affect carbon storage. Like GEDI itself, this proposal benefits from earlier CMS investments in strategic collection of lidar and field data (PI: Cohen, 2013- 2016) and development of statistical methods that apply sampling theory to estimating biomass from lidar (PI: Healey, 2012-2014). The proposed activities are needed to fully realize GED’s potential in how we plan and compensate forest management that results in augmented carbon storage. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Hans Andersen, U.S. Forest Service Pacific Northwest Research Station | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Healey, S. P., Yang, Z., Gorelick, N., Ilyushchenko, S. 2020. Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation. Remote Sensing. 12(17), 2840. DOI: 10.3390/rs12172840 Healey, S., Menlove, J. 2019. The Stability of Mean Wood Specific Gravity across Stand Age in US Forests Despite Species Turnover. Forests. 10(2), 114. DOI: 10.3390/f10020114 Hurtt, G. C., Andrews, A., Bowman, K., Brown, M. E., Chatterjee, A., Escobar, V., Fatoyinbo, L., Griffith, P., Guy, M., Healey, S. P., Jacob, D. J., Kennedy, R., Lohrenz, S., McGroddy, M. E., Morales, V., Nehrkorn, T., Ott, L., Saatchi, S., Sepulveda Carlo, E., Serbin, S. P., Tian, H. 2022. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps. Environmental Research Letters. 17(6), 063010. DOI: 10.1088/1748-9326/ac7407 Menlove, J., Healey, S. P. 2020. A Comprehensive Forest Biomass Dataset for the USA Allows Customized Validation of Remotely Sensed Biomass Estimates. Remote Sensing. 12(24), 4141. DOI: 10.3390/rs12244141 Patterson, P. L., Healey, S. P., Stahl, G., Saarela, S., Holm, S., Andersen, H., Dubayah, R. O., Duncanson, L., Hancock, S., Armston, J., Kellner, J. R., Cohen, W. B., Yang, Z. 2019. Statistical properties of hybrid estimators proposed for GEDI--NASA's global ecosystem dynamics investigation. Environmental Research Letters. 14(6), 065007. DOI: 10.1088/1748-9326/ab18df Saarela, S., Holm, S., Healey, S., Andersen, H., Petersson, H., Prentius, W., Patterson, P., Naesset, E., Gregoire, T., Stahl, G. 2018. Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data. Remote Sensing. 10(11), 1832. DOI: 10.3390/rs10111832 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Menlove, J., and S.P. Healey. 2021. CMS: Forest Aboveground Biomass from FIA Plots across the Conterminous USA, 2009-2019. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1873
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| Holmquist (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Data-Model Integration for Monitoring and Forecasting Coastal Wetland Carbon Exchanges: Serving Local to National Greenhouse Gas Inventories | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
James Holmquist, Smithsonian Environmental Research Center
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Windham-Myers (CMS 2014) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Tidal wetlands are a substantial carbon (C) sink due to the dynamic response between inundation and soil formation. Yet, their net-warming effect can differ regionally because CH4 emissions vary with salinity, degradation and management practices. These dynamics make wetland preservation and restoration vital strategies for mitigating climate change. The continental U.S. is an ideal scale for analysis of C sources and sinks; it was the first country to adopt IPCC reporting guidance in its national greenhouse gas inventory (NGGI) and represents all intertidal vegetation types across a wide range of inundation patterns.
This proposal will leverage remote sensing technology, data/model availability, and process knowledge improvements to support the continued iterative development of tidal wetlands in the U.S. NGGI. Coordinating vegetation, salinity, and greenhouse gas (GHG) flux sampling among Smithsonian’s Global Change Research Wetland, the Louisiana Universities Marine Consortium, and the National Estuarine Research Reserves (NERRs), we will develop new C-relevant wetland maps with which to apply processinformed models of CO2 and CH4 flux. We will integrate ECOSTRESS, OCO-2, MODIS, Landsat and Sentinel-2 imagery, tidal elevation maps, and ground data from multiple sites to classify plant functional types, salinity gradients, and ecologically relevant inundation properties.
We will leverage efforts by the Coastal Carbon Research Coordination Network’s soils and CH4 working groups, who are merging process-based models and open source data using Bayesian hierarchical frameworks, partitioning uncertainty among initial conditions, model structure, and data. We will fuse these efforts into one state-space model that outputs daily CH4 and CO2 flux, to be upscaled at annual time steps, and fit with multiple data sources.
We propose that for coastal carbon monitoring to be an actionable part of decision making by on-the-ground land managers and also scalable for national governments, the initial conditions and drivers of these models need to be remotely sensed, and determined with as little specialized site knowledge as possible. At NERR sites, we will make nearterm forecasts of GHG fluxes by approximating net primary productivity using phenology curves and plant trait data, and constraining decay using water levels from digital elevation models and NOAA tide gauges. Forecasts will inform the design of CO2 and CH4 chamber flux measurements to validate and characterize model performance. Throughout the year, we will monitor fluxes, porewater salinity, and vegetation cover at two focal sites to quantify temporal variation missed in the more geographically extensive calibration and validation effort.
We will scale up mapped covariates and process models using the Predictive Ecosystem Analyser Framework (PEcAn), and performance against the current practice of applying regional average fluxes to areas of mapped land cover class and change events. Finally, we will host an annual model data-comparison summit to provide NERR-sponsored training to graduate students and iteratively improve near term forecasting and validation data collection.
This proposal actively advances workflows from a previous NASA CMS project and will refine sources, sinks, and fluxes for coastal systems, an understudied terrestrial-aquatic interface. It supports multiple CMS goals, such as characterizing, understanding, and predicting fluxes; exploitation of remote sensing resources, computational capacities, and scientific knowledge; developing regional to national carbon monitoring products; and improving statistical precision and accuracy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Stefano Castruccio, University Of Notre Dame | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Publications: |
Campbell, A. D., Fatoyinbo, T., Charles, S. P., Bourgeau-Chavez, L. L., Goes, J., Gomes, H., Halabisky, M., Holmquist, J., Lohrenz, S., Mitchell, C., Moskal, L. M., Poulter, B., Qiu, H., Resende De Sousa, C. H., Sayers, M., Simard, M., Stewart, A. J., Singh, D., Trettin, C., Wu, J., Zhang, X., Lagomasino, D. 2022. A review of carbon monitoring in wet carbon systems using remote sensing. Environmental Research Letters. 17(2), 025009. DOI: 10.1088/1748-9326/ac4d4d Holmquist, J. R., Windham-Myers, L. 2022. A Conterminous USA-Scale Map of Relative Tidal Marsh Elevation. Estuaries and Coasts. DOI: 10.1007/s12237-021-01027-9 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Holmquist, J.R., L.N. Brown, and G.M. Macdonald. 2021. Resilience of Coastal Wetlands to Sea Level Rise, CONUS, 1996-2100. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1839
Holmquist, J.R., M. Eagle, R.L. Molinari, S. Nick, L.C. Stachowicz, and K. Kroeger. 2022. Blue Carbon-based Natural Climate Solutions, Priority Maps for the U.S., 2006-2011. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2091 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Hudak (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | A bottom-up, stakeholder-driven CMS for regional biomass carbon dynamics: Phase II | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Andrew (Andy) Hudak, USDA Forest Service
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Hudak (CMS 2014) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Hudak (CMS 2022) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Project-level field plot measures of aboveground biomass (AGB) in association with commercial off-the-shelf (COTS) lidar and digital aerial photography (DAP) point cloud data provide excellent information on vegetation structure across the western USA. However these reference datasets highly valued by managers represent a biased sample. Meanwhile, Forest Inventory and Analysis (FIA) plot data provide an unbiased sample of forest conditions but lack spatial coverage, diminishing their value to land managers. Our Phase 1 prototype Carbon Monitoring System (CMS) produced landscape-level AGB maps wherever lidar collections were available in the northwestern USA; these spatially and temporally disjunct areas provided a stratified random sample of AGB training pixels from which to predict AGB annually across the region from Landsat time series (LandTrendr) and climate variables. At both mapping scales, we used the RandomForest algorithm for prediction. We then compared 30 m mapped AGB estimates from our CMS to AGB estimates from FIA at the plot and county levels. The ratio of FIA:CMS AGB (consistently ~0.73 calculated annually from 2001-2016) was used to define annual bias corrections in a simple model-assisted approach to produce regionally unbiased annual AGB estimates and associated pixel-level uncertainty mapped at 30m resolution for Monitoring, Reporting, and Verification (MRV).
In Phase 2, we again will use unbiased FIA plot estimates for purposes of bias correction and MRV, but propose a spatiotemporal assisting model to recalibrate mapped AGB. Moreover, the spatiotemporal assisting model will be applied to ICESat-2 and GEDI lidar variables. Using RandomForest and the LandTrendr data record, we will fill in the spatial and temporal gaps between ICESat-2 and GEDI lidar observations to produce wall-towall space-based lidar data products. We can then use a design-unbiased model-assisted estimator to generate annual mean/total estimates of forest C at the county and state levels, leveraging the spatiotemporal wall-to-wall predictions of COTS AGB C, ICEsat-2 and GEDI lidar.
By our Phase 2 model-assisted approach, we will provide spatially and temporally unbiased estimates of annual AGB C pools and fluxes across the western USA from 1984 to 2020. Disturbance patches as identified by LandTrendr will be attributed by harvest, fire, or insects/stress. AGB C fluxes due to growth and disturbance will be independently validated from revisited FIA plots for MRV. For C monitoring at higher temporal resolution, we also propose to test the utility of DAP point cloud data derived from highresolution National Agriculture Imagery Program (NAIP) imagery, collected across Washington State in 2015, 2017, and 2019.
Our assembled team of scientists and stakeholders share the desire to make effective use of huge investments into valuable project level field and COTS remote sensing data. Throughout Phase 1, we relied on stakeholder contributions; therefore, our last Phase 2 objective is ‘give back’ to our contributing stakeholders that make our CMS possible. Having already assembled many project datasets into a ‘living’ reference database (which continues to grow), we will generate maps of other forest structure and fuel attributes that are critically needed by land managers. We also propose two stakeholder workshops to engage managers with products that more directly meet their needs, such that they ‘buy in’; this will elevate the Application Readiness Level (ARL) of our Phase 2 CMS data products above ARL 5 reached in Phase 1. We assert that our prototype CMS is consistent, objective, transparent, verifiable, and applicable for mapping, monitoring, and managing the diverse forest types of the western USA. Moreover, our CMS contributes substantively to national CMS and MRV goals, and is relevant to international programs such as SilvaCarbon and REDD+ that operate globally, by making explicit use of NASA’s new GEDI and ICESat-2 datasets | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Chad Babcock, University of Minnesota | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Corrao, M. V., Hudak, A. T., Desautel, C., Bright, B. C., Carlo, E. S. 2022. Carbon monitoring and above ground biomass trends: Anchor forest opportunities for tribal, private and federal relationships. Trees, Forests and People. 9, 100302. DOI: 10.1016/j.tfp.2022.100302 Duncanson, L., Kellner, J. R., Armston, J., Dubayah, R., Minor, D. M., Hancock, S., Healey, S. P., Patterson, P. L., Saarela, S., Marselis, S., Silva, C. E., Bruening, J., Goetz, S. J., Tang, H., Hofton, M., Blair, B., Luthcke, S., Fatoyinbo, L., Abernethy, K., Alonso, A., Andersen, H., Aplin, P., Baker, T. R., Barbier, N., Bastin, J. F., Biber, P., Boeckx, P., Bogaert, J., Boschetti, L., Boucher, P. B., Boyd, D. S., Burslem, D. F., Calvo-Rodriguez, S., Chave, J., Chazdon, R. L., Clark, D. B., Clark, D. A., Cohen, W. B., Coomes, D. A., Corona, P., Cushman, K. C., Cutler, M. E., Dalling, J. W., Dalponte, M., Dash, J., de-Miguel, S., Deng, S., Ellis, P. W., Erasmus, B., Fekety, P. A., Fernandez-Landa, A., Ferraz, A., Fischer, R., Fisher, A. G., Garcia-Abril, A., Gobakken, T., Hacker, J. M., Heurich, M., Hill, R. A., Hopkinson, C., Huang, H., Hubbell, S. P., Hudak, A. T., Huth, A., Imbach, B., Jeffery, K. J., Katoh, M., Kearsley, E., Kenfack, D., Kljun, N., Knapp, N., Kral, K., Krucek, M., Labriere, N., Lewis, S. L., Longo, M., Lucas, R. M., Main, R., Manzanera, J. A., Martinez, R. V., Mathieu, R., Memiaghe, H., Meyer, V., Mendoza, A. M., Monerris, A., Montesano, P., Morsdorf, F., Naesset, E., Naidoo, L., Nilus, R., O'Brien, M., Orwig, D. A., Papathanassiou, K., Parker, G., Philipson, C., Phillips, O. L., Pisek, J., Poulsen, J. R., Pretzsch, H., Rudiger, C., Saatchi, S., Sanchez-Azofeifa, A., Sanchez-Lopez, N., Scholes, R., Silva, C. A., Simard, M., Skidmore, A., Sterenczak, K., Tanase, M., Torresan, C., Valbuena, R., Verbeeck, H., Vrska, T., Wessels, K., White, J. C., White, L. J., Zahabu, E., Zgraggen, C. 2022. Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sensing of Environment. 270, 112845. DOI: 10.1016/j.rse.2021.112845 Emick, E., Babcock, C., White, G. W., Hudak, A. T., Domke, G. M., Finley, A. O. 2023. An approach to estimating forest biomass while quantifying estimate uncertainty and correcting bias in machine learning maps. Remote Sensing of Environment. 295, 113678. DOI: 10.1016/j.rse.2023.113678 Jensen, P. O., Meddens, A. J., Fisher, S., Wirsing, A. J., Murray, D. L., Thornton, D. H. 2021. Broaden your horizon: The use of remotely sensed data for modeling populations of forest species at landscape scales. Forest Ecology and Management. 500, 119640. DOI: 10.1016/j.foreco.2021.119640 Mauro, F., Hudak, A. T., Fekety, P. A., Frank, B., Temesgen, H., Bell, D. M., Gregory, M. J., McCarley, T. R. 2021. Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon. Remote Sensing. 13(2), 261. DOI: 10.3390/rs13020261 Mauro, F., Monleon, V. J., Gray, A. N., Kuegler, O., Temesgen, H., Hudak, A. T., Fekety, P. A., Yang, Z. 2022. Comparison of Model-Assisted Endogenous Poststratification Methods for Estimation of Above-Ground Biomass Change in Oregon, USA. Remote Sensing. 14(23), 6024. DOI: 10.3390/rs14236024 Meddens, A. J. H., Steen-Adams, M. M., Hudak, A. T., Mauro, F., Byassee, P. M., Strunk, J. 2022. Specifying geospatial data product characteristics for forest and fuel management applications. Environmental Research Letters. 17(4), 045025. DOI: 10.1088/1748-9326/ac5ee0 Sanchez-Lopez, N., Boschetti, L., Hudak, A. T., Hancock, S., Duncanson, L. I. 2020. Estimating Time Since the Last Stand-Replacing Disturbance (TSD) from Spaceborne Simulated GEDI Data: A Feasibility Study. Remote Sensing. 12(21), 3506. DOI: 10.3390/rs12213506 Stahl, A. T., Andrus, R., Hicke, J. A., Hudak, A. T., Bright, B. C., Meddens, A. J. 2023. Automated attribution of forest disturbance types from remote sensing data: A synthesis. Remote Sensing of Environment. 285, 113416. DOI: 10.1016/j.rse.2022.113416 Temesgen, H., Mauro, F., Hudak, A. T., Frank, B., Monleon, V., Fekety, P., Palmer, M., Bryant, T. 2021. Using Fay-Herriot Models and Variable Radius Plot Data to Develop a Stand-Level Inventory and Update a Prior Inventory in the Western Cascades, OR, United States. Frontiers in Forests and Global Change. 4. DOI: 10.3389/ffgc.2021.745916 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Hurtt (CMS 2016) (2017) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | High-Resolution Carbon Monitoring and Modeling: Continued Prototype Development and Deployment to Regional and National Scales | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
George Hurtt, University of Maryland
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Hurtt (CMS 2014) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Hurtt (CMS 2020) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
The overall goal of our project is the continuing development of a framework for estimating high-resolution forest carbon stocks and dynamics and future carbon sequestration potential using remote sensing and ecosystem modeling linked with
existing field observation systems such as the USFS Forest Inventory and Analysis (FIA) network. In particular, we seek to demonstrate an approach that provides the basis for the rapid expansion from previous prototypes at the county/state-scale to cover a multi-state region encompassing the Regional Greenhouse Gas Initiative (RGGI) domain, and ultimately the coterminous U.S. Additionally, we prepare for national scale prognostic ecosystem modeling using data from the Global Ecosystem Dynamics Investigation (GEDI). Our intent is to drive the model at 1 km resolution over the lower 48 states using the first year of canopy height observations from GEDI. Specifically, we will address the following objectives: (1) Build upon, extend, and improve our existing methodology for carbon stock estimation and uncertainty based on lessons learned from our Phase 2 studies. (2) Provide wall-to-wall, high-resolution, estimates of carbon stocks, carbon sequestration potential, and their uncertainties for multi-state state RGGI+. (3) Validate and enhance national biomass maps using Forest Inventory and Analysis (FIA) data and high- resolution biomass maps over an expanded domain. (4) Demonstrate MRV efficacy to meet stakeholder needs at regional scale, and a vision for future national-scale deployment. (5) Prototype national scale forest carbon products for CONUS using GEDI data. Our proposed research directly responds to the research topics identified for this phase of CMS. Additionally, data from airborne lidar, airborne optical, and spaceborne platforms are essential to this project as is societal relevance, with active stakeholder engagement planned at state, regional, and national scales. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
TeeJay Boudreau, Rhode Island Department of Environmental Management | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Basu, S., Mukhopadhyay, S., Karki, M., DiBiano, R., Ganguly, S., Nemani, R., Gayaka, S. 2018. Deep neural networks for texture classification--A theoretical analysis. Neural Networks. 97, 173-182. DOI: 10.1016/j.neunet.2017.10.001 Chini, L., Hurtt, G., Sahajpal, R., Frolking, S., Klein Goldewijk, K., Sitch, S., Ganzenmuller, R., Ma, L., Ott, L., Pongratz, J., Poulter, B. Land-Use Harmonization Datasets for Annual Global Carbon Budgets DOI: 10.5194/essd-2020-388 Dolan, K. A., Hurtt, G. C., Flanagan, S. A., Fisk, J. P., Sahajpal, R., Huang, C., Page, Y. L., Dubayah, R., Masek, J. G. 2017. Disturbance Distance: quantifying forests' vulnerability to disturbance under current and future conditions. Environmental Research Letters. 12(11), 114015. DOI: 10.1088/1748-9326/aa8ea9 Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O., Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D., Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K., Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu, X., Zhang, T., Moorcroft, P. R. 2017. Vegetation demographics in Earth System Models: A review of progress and priorities. Global Change Biology. 24(1), 35-54. DOI: 10.1111/gcb.13910 Flanagan, S. A., Hurtt, G. C., Fisk, J. P., Sahajpal, R., Zhao, M., Dubayah, R., Hansen, M. C., Sullivan, J. H., Collatz, G. J. 2019. Potential Transient Response of Terrestrial Vegetation and Carbon in Northern North America from Climate Change. Climate. 7(9), 113. DOI: 10.3390/cli7090113 Huang, W., Dolan, K., Swatantran, A., Johnson, K., Tang, H., O'Neil-Dunne, J., Dubayah, R., Hurtt, G. 2019. High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA. Environmental Research Letters. 14(9), 095002. DOI: 10.1088/1748-9326/ab2917 Huang, W., Swatantran, A., Duncanson, L., Johnson, K., Watkinson, D., Dolan, K., O'Neil-Dunne, J., Hurtt, G., Dubayah, R. 2017. County-scale biomass map comparison: a case study for Sonoma, California. Carbon Management. 8(5-6), 417-434. DOI: 10.1080/17583004.2017.1396840 Hurtt, G. C., Andrews, A., Bowman, K., Brown, M. E., Chatterjee, A., Escobar, V., Fatoyinbo, L., Griffith, P., Guy, M., Healey, S. P., Jacob, D. J., Kennedy, R., Lohrenz, S., McGroddy, M. E., Morales, V., Nehrkorn, T., Ott, L., Saatchi, S., Sepulveda Carlo, E., Serbin, S. P., Tian, H. 2022. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps. Environmental Research Letters. 17(6), 063010. DOI: 10.1088/1748-9326/ac7407 Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenoder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., Zhang, X. 2020. Harmonization of global land use change and management for the period 850-2100 (LUH2) for CMIP6. Geoscientific Model Development. 13(11), 5425-5464. DOI: 10.5194/gmd-13-5425-2020 Hurtt, G., Zhao, M., Sahajpal, R., Armstrong, A., Birdsey, R., Campbell, E., Dolan, K., Dubayah, R., Fisk, J. P., Flanagan, S., Huang, C., Huang, W., Johnson, K., Lamb, R., Ma, L., Marks, R., O'Leary, D., O'Neil-Dunne, J., Swatantran, A., Tang, H. 2019. Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA. Environmental Research Letters. 14(4), 045013. DOI: 10.1088/1748-9326/ab0bbe Kumar, U., Ganguly, S., Nemani, R. R., Raja, K. S., Milesi, C., Sinha, R., Michaelis, A., Votava, P., Hashimoto, H., Li, S., Wang, W., Kalia, S., Gayaka, S. 2017. Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing. Remote Sensing. 9(11), 1105. DOI: 10.3390/rs9111105 Lamb, R. L., Hurtt, G. C., Boudreau, T. J., Campbell, E., Sepulveda Carlo, E. A., Chu, H., de Mooy, J., Dubayah, R. O., Gonsalves, D., Guy, M., Hultman, N. E., Lehman, S., Leon, B., Lister, A. J., Lynch, C., Ma, L., Martin, C., Robbins, N., Rudee, A., Silva, C. E., Skoglund, C., Tang, H. 2021. Context and future directions for integrating forest carbon into sub-national climate mitigation planning in the RGGI region of the U.S. Environmental Research Letters. 16(6), 063001. DOI: 10.1088/1748-9326/abe6c2 Lamb, R. L., Ma, L., Sahajpal, R., Edmonds, J., Hultman, N. E., Dubayah, R. O., Kennedy, J., Hurtt, G. C. 2021. Geospatial assessment of the economic opportunity for reforestation in Maryland, USA. Environmental Research Letters. 16(8), 084012. DOI: 10.1088/1748-9326/ac109a Ma, L., Hurtt, G. C., Chini, L. P., Sahajpal, R., Pongratz, J., Frolking, S., Stehfest, E., Klein Goldewijk, K., O'Leary, D., Doelman, J. C. 2020. Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2. Geoscientific Model Development. 13(7), 3203-3220. DOI: 10.5194/gmd-13-3203-2020 Ma, L., Hurtt, G., Ott, L., Sahajpal, R., Fisk, J., Lamb, R., Tang, H., Flanagan, S., Chini, L., Chatterjee, A., Sullivan, J. Global Evaluation of the Ecosystem Demography Model (ED v3.0) DOI: 10.5194/gmd-2021-292 Ma, L., Hurtt, G., Tang, H., Lamb, R., Campbell, E., Dubayah, R., Guy, M., Huang, W., Lister, A., Lu, J., O'Neil-Dunne, J., Rudee, A., Shen, Q., Silva, C. 2021. High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA. Environmental Research Letters. 16(4), 045014. DOI: 10.1088/1748-9326/abe4f4 McDowell, N. G., Allen, C. D., Anderson-Teixeira, K., Aukema, B. H., Bond-Lamberty, B., Chini, L., Clark, J. S., Dietze, M., Grossiord, C., Hanbury-Brown, A., Hurtt, G. C., Jackson, R. B., Johnson, D. J., Kueppers, L., Lichstein, J. W., Ogle, K., Poulter, B., Pugh, T. A. M., Seidl, R., Turner, M. G., Uriarte, M., Walker, A. P., Xu, C. 2020. Pervasive shifts in forest dynamics in a changing world. Science. 368(6494). DOI: 10.1126/science.aaz9463 Tang, H., Ma, L., Lister, A., O'Neill-Dunne, J., Lu, J., Lamb, R. L., Dubayah, R., Hurtt, G. 2021. High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA. Environmental Research Letters. 16(3), 035011. DOI: 10.1088/1748-9326/abd2ef | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Hurtt, G.C., M. Zhao, R. Sahajpal, A. Armstrong, R. Birdsey, E. Campbell, K. Dolan, R.O. Dubayah, J.P. Fisk, S. Flanagan, C. Huang, W. Huang, K. Johnson, R. Lamb, L. Ma, R. Marks, D. O'Leary III, J. O'Neil-Dunne, A. Swatantran, and H. Tang. 2019. Forest Aboveground Biomass and Carbon Sequestration Potential for Maryland, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1660
Tang, H., L. Ma, A.J. Lister, J. O'Neil-Dunne, J. Lu, R. Lamb, R.O. Dubayah, and G.C. Hurtt. 2021. LiDAR Derived Biomass, Canopy Height, and Cover for New England Region, USA, 2015. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1854 Ma, L., G.C. Hurtt, H. Tang, R. Lamb, E. Campbell, R.O. Dubayah, M. Guy, W. Huang, J. Lu, A. Rudee, Q. Shen, C.E. Silva, and A.J. Lister. 2022. Forest Aboveground Biomass and Carbon Sequestration Potential, Northeastern USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1922 O'Neil-Dunne, J., E. Buford, S. Macfaden, and A. Royar. 2022. CMS: Tree Canopy Cover at 0.5-meter resolution, Vermont, 2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2072 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Izaurralde (CMS 2015) (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Cropland Carbon Monitoring System (CCMS): A satellite-based system to estimate carbon fluxes on U.S. Croplands | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Roberto (Cesar) Izaurralde, University of Maryland
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Bandaru (CMS 2020) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Croplands are considered to have large CO2 offset capacity. However, it is highly uncertain how much CO2 stabilization can be achieved through land management strategies as croplands are expected to meet increasing demands for food and bioenergy production. The impacts of land use and land management practices on carbon (C) cycling should be anticipated when developing recommended strategies and policies; otherwise, they may induce unintended loss of CO2 to the atmosphere and render croplands as C sources. Lack of a cropland C monitoring system that captures the complexity of cropland C cycling and provides fine-scale and accurate C flux estimates
hinders the development of effective joint policies and integrated sustainable carbon management strategies targeting CO2 offset potentials.
Current methods for cropland C monitoring yield unreasonable regional flux estimates as they lack spatially resolved crop parameters and management practices. Satellite remote sensing is a strong tool for estimating spatially distributed vegetative characteristics (e.g. crop phenology and LAI) and crop parameters (e.g. land cover and land use change, crop species, crop rotations) used in agroecosystem models. As part of the Global Agricultural Monitoring (GEO-GLAM) program, which is jointly funded by NASA and USDA, we have developed a remote-sensing version of the mechanistic agroecosystem model EPIC, herein referred to as RS-EPIC, which utilizes satellite remote sensing data to improve crop characterization and simulation of crop productivity, soil C storage and C fluxes. The overall scientific goal of this proposal is to develop a Cropland C Monitoring System (CCMS) prototype that improves upon cropland C storage and flux estimates developed under previous NASA CMS activities in terms of spatial and temporal scale and completeness. As a first objective of this goal, we will integrate satellite-derived crop specific characterization of vegetation and management, off-shelf ancillary spatial databases and the RS-EPIC model to estimate seasonal and annual C cycle components including net primary production (NPP), net ecosystem productivity (NEP), harvested C, lateral soil C fluxes and net ecosystem C balance (NECB). These estimates will be produced for corn, soybean, wheat, sorghum, cotton, alfalfa, barley, rice and peas crops grown in the conterminous US at a spatial resolution of 500 m for 2015-2016. Together, the nine major crops grown cover approximately 96% of US cropland area. Three additional objectives are: 1) estimate uncertainty of C storage and fluxes estimated by the CCMS prototype; 2) engage with national agencies to evaluate the CCMS consistency with existing C inventories; 3) conduct a scoping study to evaluate remote sensing methods for mapping soil tillage at large scales.
Ultimately, the CCMS products developed under this project will provide the knowledge base at relevant spatial and temporal scales for understanding complex C cycling outcomes under various land use and land management practices and developing joint policies to meet multiple objectives (e.g. food and energy security) while contributing to stabilize atmospheric CO2. Other potential uses of the CCMS include: 1) use in economic models to determine incentive levels for C management options; 2) integration into hydrological models to assess impacts on aquatic ecosystems; 3) incorporation into regional integrated assessment models to understand contributions of regional management practices to global climate change; 4) use of NPP estimates to interpret the top-bottom CO2 estimates 5) enhancement of EPA reporting of CO2 offset potentials on croplands. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Varaprasad (Prasad) Bandaru, USDA ARS | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Bandaru, V., Yaramasu, R., PNVR, K., He, J., Fernando, S., Sahajpal, R., Wardlow, B. D., Suyker, A., Justice, C. 2020. PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans. International Journal of Applied Earth Observation and Geoinformation. 92, 102188. DOI: 10.1016/j.jag.2020.102188 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Jacob (CMS 2016) (2017) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Improved understanding of methane emissions and trends in North America and globally through a unified top-down and bottom-up approach exploiting GOSAT and TROPOMI satellite data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Daniel Jacob, Harvard University
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Jacob (CMS 2014) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Jacob (CMS 2020) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
We propose to continue our work on the NASA CMS Science Team to improve knowledge of methane emissions in North America and globally through the exploitation of satellite data and in service to stakeholders. There is considerable need to assess gaps in national methane emission inventories, including contributions from different sectors. The resumed growth in global atmospheric methane over the past decade has attracted much attention but its cause is still being debated. Our work will advance understanding by bridging the gap between top-down information from atmospheric methane observations (satellite and suborbital) and bottom-up information from process-based inventories. We will use state-of-science, policy-relevant national emission inventories, including error estimates, to serve as prior information in inversions of satellite data from GOSAT (2009-present) and TROPOMI (2017 launch). From there we will be able to evaluate these inventories and provide guidance for improvements. The long, high- quality record from GOSAT will provide strong constraints on regional sources and unique insight into the factors driving the methane trend. TROPOMI with its global daily coverage is expected to considerably increase our ability to quantify methane emissions from space including seasonal variations.
Our work will build on a strong collaboration with EPA already developed through CMS. This collaboration has produced a spatially resolved version of the national Greenhouse
Gas Inventory (GHGI) including scale-dependent error estimates. We will apply this inventory as prior estimate for inversions of satellite data, and work with EPA in the interpretation of results to evaluate and improve the GHGI. We have also developed an ensemble-based global wetland emission inventory (WetCHARTs) that we will use in our inversions to narrow uncertainty in biogeochemical process controls. We will develop new collaborations with Environment and Climate Change Canada (ECCC) and the Mexican Instituto Nacional de Ecología y Cambio Climático (INECC) to produce spatially resolved versions of their national inventories, enabling evaluation of these inventories with satellite data through our inversion framework. We will apply innovative inverse methods to achieve high-resolution constraints on methane emissions and trends, for North America and globally, with full error characterization. Suborbital data (NOAA, TCCON sites; ATom, SONGNEX, CARVE, SEAC4RS, ACT-America aircraft campaigns) will be used at all stages of the analysis. Specific tasks for the project will involve:
(1) Interpret the GOSAT satellite record (2009-present) using advanced inverse methods, and together with suborbital data, to constrain methane emissions and their trends with full error characterization, globally and for North America at high resolution;
(2) Apply inversion results to evaluate national methane inventories for the US, Canada, and Mexico, working in collaboration with EPA, ECCC, and INECC;
(3) Narrow uncertainties in wetland emissions and the underlying process controls by applying inversion error reductions to a large ensemble of bottom-up inventories;
(4) Start interpreting TROPOMI observations as soon as they become available (expected mid-2018) to improve top-down constraints on methane emissions including seasonal information. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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|
Participants: |
Ilse Aben, SRON Netherlands Institute for Space Research | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Alvarez, R. A., Zavala-Araiza, D., Lyon, D. R., Allen, D. T., Barkley, Z. R., Brandt, A. R., Davis, K. J., Herndon, S. C., Jacob, D. J., Karion, A., Kort, E. A., Lamb, B. K., Lauvaux, T., Maasakkers, J. D., Marchese, A. J., Omara, M., Pacala, S. W., Peischl, J., Robinson, A. L., Shepson, P. B., Sweeney, C., Townsend-Small, A., Wofsy, S. C., Hamburg, S. P. 2018. Assessment of methane emissions from the U.S. oil and gas supply chain. Science. eaar7204. DOI: 10.1126/science.aar7204 Cusworth, D. H., Jacob, D. J., Sheng, J., Benmergui, J., Turner, A. J., Brandman, J., White, L., Randles, C. A. 2018. Detecting high-emitting methane sources in oil/gas fields using satellite observations. Atmospheric Chemistry and Physics. 18(23), 16885-16896. DOI: 10.5194/acp-18-16885-2018 Cusworth, D. H., Jacob, D. J., Varon, D. J., Chan Miller, C., Liu, X., Chance, K., Thorpe, A. K., Duren, R. M., Miller, C. E., Thompson, D. R., Frankenberg, C., Guanter, L., Randles, C. A. 2019. Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space. Atmospheric Measurement Techniques. 12(10), 5655-5668. DOI: 10.5194/amt-12-5655-2019 Lu, X., Jacob, D. J., Zhang, Y., Maasakkers, J. D., Sulprizio, M. P., Shen, L., Qu, Z., Scarpelli, T. R., Nesser, H., Yantosca, R. M., Sheng, J., Andrews, A., Parker, R. J., Boesch, H., Bloom, A. A., Ma, S. 2021. Global methane budget and trend, 2010-2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH<sub>4</sub> ObsPack) and satellite (GOSAT) observations. Atmospheric Chemistry and Physics. 21(6), 4637-4657. DOI: 10.5194/acp-21-4637-2021 Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J., Zhang, Y., Hersher, M., Bloom, A. A., Bowman, K. W., Worden, J. R., Janssens-Maenhout, G., Parker, R. J. 2019. Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010-2015. Atmospheric Chemistry and Physics. 19(11), 7859-7881. DOI: 10.5194/acp-19-7859-2019 Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J., Zhang, Y., Lu, X., Bloom, A. A., Bowman, K. W., Worden, J. R., Parker, R. J. 2021. 2010-2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane. Atmospheric Chemistry and Physics. 21(6), 4339-4356. DOI: 10.5194/acp-21-4339-2021 Parker, R. J., Boesch, H., McNorton, J., Comyn-Platt, E., Gloor, M., Wilson, C., Chipperfield, M. P., Hayman, G. D., Bloom, A. A. 2018. Evaluating year-to-year anomalies in tropical wetland methane emissions using satellite CH4 observations. Remote Sensing of Environment. 211, 261-275. DOI: 10.1016/j.rse.2018.02.011 Parker, R. J., Wilson, C., Bloom, A. A., Comyn-Platt, E., Hayman, G., McNorton, J., Boesch, H., Chipperfield, M. P. 2020. Exploring constraints on a wetland methane emission ensemble (WetCHARTs) using GOSAT observations. Biogeosciences. 17(22), 5669-5691. DOI: 10.5194/bg-17-5669-2020 Scarpelli, T. R., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P., Sheng, J., Rose, K., Romeo, L., Worden, J. R., Janssens-Maenhout, G. 2020. A global gridded (0.1deg x 0.1deg) inventory of methane emissions from oil, gas, and coal exploitation based on national reports to the United Nations Framework Convention on Climate Change. Earth System Science Data. 12(1), 563-575. DOI: 10.5194/essd-12-563-2020 Scarpelli, T. R., Jacob, D. J., Octaviano Villasana, C. A., Ramirez Hernandez, I. F., Cardenas Moreno, P. R., Cortes Alfaro, E. A., Garcia Garcia, M. A., Zavala-Araiza, D. 2020. A gridded inventory of anthropogenic methane emissions from Mexico based on Mexico's national inventory of greenhouse gases and compounds. Environmental Research Letters. 15(10), 105015. DOI: 10.1088/1748-9326/abb42b Shen, L., Zavala-Araiza, D., Gautam, R., Omara, M., Scarpelli, T., Sheng, J., Sulprizio, M. P., Zhuang, J., Zhang, Y., Qu, Z., Lu, X., Hamburg, S. P., Jacob, D. J. 2021. Unravelling a large methane emission discrepancy in Mexico using satellite observations. Remote Sensing of Environment. 260, 112461. DOI: 10.1016/j.rse.2021.112461 Sheng, J., Jacob, D. J., Maasakkers, J. D., Zhang, Y., Sulprizio, M. P. 2018. Comparative analysis of low-Earth orbit (TROPOMI) and geostationary (GeoCARB, GEO-CAPE) satellite instruments for constraining methane emissions on fine regional scales: application to the Southeast US. Atmospheric Measurement Techniques. 11(12), 6379-6388. DOI: 10.5194/amt-11-6379-2018 Sheng, J., Jacob, D. J., Turner, A. J., Maasakkers, J. D., Benmergui, J., Bloom, A. A., Arndt, C., Gautam, R., Zavala-Araiza, D., Boesch, H., Parker, R. J. 2018. 2010-2016 methane trends over Canada, the United States, and Mexico observed by the GOSAT satellite: contributions from different source sectors. Atmospheric Chemistry and Physics. 18(16), 12257-12267. DOI: 10.5194/acp-18-12257-2018 Sheng, J., Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sulprizio, M. P., Bloom, A. A., Andrews, A. E., Wunch, D. 2018. High-resolution inversion of methane emissions in the Southeast US using SEAC<sup>4</sup>RS aircraft observations of atmospheric methane: anthropogenic and wetland sources. Atmospheric Chemistry and Physics. 18(9), 6483-6491. DOI: 10.5194/acp-18-6483-2018 Treat, C. C., Bloom, A. A., Marushchak, M. E. 2018. Nongrowing season methane emissions-a significant component of annual emissions across northern ecosystems. Global Change Biology. 24(8), 3331-3343. DOI: 10.1111/gcb.14137 Turner, A. J., Jacob, D. J., Benmergui, J., Brandman, J., White, L., Randles, C. A. 2018. Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales. Atmospheric Chemistry and Physics. 18(11), 8265-8278. DOI: 10.5194/acp-18-8265-2018 Varon, D. J., Jacob, D. J., Jervis, D., McKeever, J. 2020. Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations. Environmental Science & Technology. 54(16), 10246-10253. DOI: 10.1021/acs.est.0c01213 Varon, D. J., Jacob, D. J., McKeever, J., Jervis, D., Durak, B. O. A., Xia, Y., Huang, Y. 2018. Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes. Atmospheric Measurement Techniques. 11(10), 5673-5686. DOI: 10.5194/amt-11-5673-2018 Varon, D. J., Jervis, D., McKeever, J., Spence, I., Gains, D., Jacob, D. J. 2021. High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations. Atmospheric Measurement Techniques. 14(4), 2771-2785. DOI: 10.5194/amt-14-2771-2021 Varon, D. J., McKeever, J., Jervis, D., Maasakkers, J. D., Pandey, S., Houweling, S., Aben, I., Scarpelli, T., Jacob, D. J. 2019. Satellite Discovery of Anomalously Large Methane Point Sources From Oil/Gas Production. Geophysical Research Letters. 46(22), 13507-13516. DOI: 10.1029/2019GL083798 Zhang, Y., Gautam, R., Zavala-Araiza, D., Jacob, D. J., Zhang, R., Zhu, L., Sheng, J., Scarpelli, T. 2019. Satellite-Observed Changes in Mexico's Offshore Gas Flaring Activity Linked to Oil/Gas Regulations. Geophysical Research Letters. 46(3), 1879-1888. DOI: 10.1029/2018GL081145 Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S., Worden, J., Parker, R. J., Boesch, H. 2021. Attribution of the accelerating increase in atmospheric methane during 2010-2018 by inverse analysis of GOSAT observations. Atmospheric Chemistry and Physics. 21(5), 3643-3666. DOI: 10.5194/acp-21-3643-2021 Zhang, Y., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P., Sheng, J., Gautam, R., Worden, J. 2018. Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane. Atmospheric Chemistry and Physics. 18(21), 15959-15973. DOI: 10.5194/acp-18-15959-2018 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Yuzhong Zhang & Daniel Jacob (2021), Global methane fluxes optimized with GOSAT data for 2010-2018, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/FPKC6Q6SGWE0
Scarpelli, Tia R.; Jacob, Daniel J.; Maasakkers, Joannes D.; Sulprizio, Melissa P.; Sheng, Jian-Xiong; Rose, Kelly; Romeo, Lucy; Worden, John R.; Janssens-Maenhout, Greet, 2021, 'Global Inventory of Methane Emissions from Fuel Exploitation', Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), DOI: 10.5067/Q28GFYJYFZ7H Joannes D. Maasakkers & Daniel J. Jacob (2021), High-resolution mean North American methane fluxes for 2010-2015 optimized with GOSAT satellite data, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/HD8VRAZN65CL Scarpelli, Tia R; Jacob, Daniel J.; Octaviano Villasana, Claudia A.; Ramírez Hernández, Irma F.; Cárdenas Moreno, Paulina R.; Cortés Alfaro, Eunice A.; García García, Miguel Á.; Zavala-Araiza, Daniel, 2020, 'Gridded inventory of Mexico's anthropogenic methane emissions', DOI: 10.7910/DVN/5FUTWM, Harvard Dataverse, V1 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kawa (CMS 2015) (2016) | ||||||||||||||||||||||||||||||||||||||
| Project Title: | Airborne Eddy Flux Measurements for Validation/Evaluation of High-Resolution MRV Systems | |||||||||||||||||||||||||||||||||||||
|
Science Team |
Stephan (Randy) Kawa, NASA GSFC
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | |||||||||||||||||||||||||||||||||||||
| Abstract: |
Progress in the Carbon Monitoring System (CMS) demands rigorous evaluation and quantitative uncertainty characterization in all products and analyses. A range of validation approaches is used, but comprehensive evaluation is challenging, often limited
in coverage, representativeness, and precision. The guiding science question for this proposal is: how best to validate CMS regional-scale products and how well can this be done?
We aim to expand the current scope of validation methods for CMS through acquisition and analysis of airborne eddy covariance carbon flux observations. Specifically, we will address the question: How can real-time flux measurements over regional length scales contribute to validation of the products and processes inherent in designing a high- resolution Monitoring, Reporting, and Verification (MRV) system? We will do this within the framework of a prototype system for monitoring carbon stocks and fluxes under development for CMS at the University of Maryland (UMD).
Airborne eddy covariance is a powerful observational tool capable of providing near- direct measurements of surface-atmosphere exchange at ecosystem and policy relevant scales of 1 – 100 km. Our group at GSFC has assembled a system for measurement of CO2, CH4, H2O, and heat fluxes based on the NASA Sherpa aircraft. The Sherpa provides a versatile, economical platform for measuring greenhouse gas (GHG) fluxes to be used in evaluating top-down and bottom-up source/sink estimates for a wide range of applications, including evaluation of biophysical process models as well as validation of top-level satellite flux products from OCO-2 and other carbon space missions. The system is supported and scheduled for installation, flight-testing, and science demonstration over the Maryland Eastern Shore during July-Sept 2016.
To address uncertainties in the high-resolution MRV system we will focus on measuring and evaluating the ecosystem model processes used to connect vegetation metabolism to biomass change and, hence, integrated carbon flux. The analysis will compare flux data and modeling across gradients of forest height and type as well as soil and climate regime within the US Mid-Atlantic region. We will also use the airborne flux data to assess uncertainties in scaling up from local to regional and larger domains. This will include leveraging of the flux data acquired in 2016 under separate funding as well as acquisition of additional airborne flux data. The latter will be guided by sensitivities identified in the carbon stock and modeling surveys of the UMD prototype system. We will also assess the measurement requirements for airborne flux observations to quantify net carbon emissions and storage.
The impact of this project will be to advance the primary CMS goal of evaluation of errors and uncertainties by demonstrating a potentially powerful tool for flux quantification applicable to CMS. We will produce a data set of regional GHG flux estimates and their statistical errors for use in other CMS and community analyses, and we will provide a more comprehensive validation/evaluation of uncertainties in the UMD prototype MRV products. The measurement technique is also potentially applicable to validation for CMS Integrated Emission/Uptake (‘Flux’) products. This research directly addresses the CMS solicitation request to advance remote sensing-based approaches to MRV through use of airborne flux observations as an alternative method for quantifying net carbon emissions, and the need to improve the characterization and quantification of errors and uncertainties in existing NASA CMS products. The work is timely both for maturation of the MRV prototype system to include a better description of uncertainties as well as to make use of a new experimental capability for the corresponding domain.
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Participants: |
George (Jim) Collatz, NASA GSFC - retired | |||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Hannun, R. A., Wolfe, G. M., Kawa, S. R., Hanisco, T. F., Newman, P. A., Alfieri, J. G., Barrick, J., Clark, K. L., DiGangi, J. P., Diskin, G. S., King, J., Kustas, W. P., Mitra, B., Noormets, A., Nowak, J. B., Thornhill, K. L., Vargas, R. 2020. Spatial heterogeneity in CO2, CH4, and energy fluxes: insights from airborne eddy covariance measurements over the Mid-Atlantic region. Environmental Research Letters. 15(3), 035008. DOI: 10.1088/1748-9326/ab7391 Wolfe, G. M., Kawa, S. R., Hanisco, T. F., Hannun, R. A., Newman, P. A., Swanson, A., Bailey, S., Barrick, J., Thornhill, K. L., Diskin, G., DiGangi, J., Nowak, J. B., Sorenson, C., Bland, G., Yungel, J. K., Swenson, C. A. 2018. The NASA Carbon Airborne Flux Experiment (CARAFE): instrumentation and methodology. Atmospheric Measurement Techniques. 11(3), 1757-1776. DOI: 10.5194/amt-11-1757-2018 | |||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Wolfe, G. M., Kawa, S. R., Hanisco, T. F., Hannun, R. A., Newman, P. A., et al., 2018: The NASA Carbon Airborne Flux Experiment (CARAFE): instrumentation and methodology, Atmos. Meas.
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| Kennedy (CMS 2015) (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Tools to bridge the gap between static CMS maps, models, and stakeholders | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Robert Kennedy, Oregon State University
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
From its inception, the NASA Carbon Monitoring System (CMS) has largely been organized around two activities: observation-based mapping of biomass and model- based estimation of carbon flux. Although there has been significant progress in both biomass and flux activities at various scales, several challenges hinder the use of biomass products to inform flux modeling. Challenges include: biomass maps are often static or local scale, uncertainties are difficult to render and incorporate into models, and map products are not designed with the needs – and format standards – of modelers in mind.
To help address these challenges, we propose a set of research activities organized around two objectives. First, we will develop tools to integrate static and dynamic CMS products of any temporal, spatial, and semantic content into a consistent, continental- U.S.-wide, derived database of yearly land cover, biomass, disturbance and growth in terrestrial systems, along with spatially explicit and consistent uncertainties. These can be used to set states, hone parameters, schedule events, and constrain or benchmark models from which flux estimates ultimately are derived. Second, we will develop a smart application programming interface to allow modelers and stakeholders easy access to these data in the spatial, temporal, and information domain they require.
We have assembled a team of Collaborators and Co-Investigators to help guide success. Collaborators include numerous CMS colleagues who have produced or are producing the static or local-scale maps we will integrate into our yearly maps. They will ensure we interpret and use their products appropriately. Co-Investigators include process-level modelers who represent a series of carbon use-cases, ranging from regional scale DGVM implementations to global scale, multi-model ensembles. They will help develop and test the interface to ensure its applicability across a continuum of situations, and will help guide us toward visualization choices appropriate for their stakeholders. Finally, we have engaged key representatives from the Land Processes and Oak Ridge National Lab Distributed Active Archive Centers (LP and ORNL DAACs) to ensure that our interface complements and co-exists with the data access and archiving efforts they continue to lead.
Key deliverables include:
- A database of 30m resolution, yearly time-step maps from 1990 to present of
forest biomass, land cover, tree cover, crop type, and disturbance for the
continental U.S., along with uncertainties
- Computational interface (API) to allow CMS participants to easily access and
analyze that database
- Assessment of potential improvement in models derived from these dynamic
land surface drivers, including possible reduction in uncertainties.
This efforts explicitly addresses the CMS call for follow-on to existing CMS efforts, for development of new remotely-sensed MRV-relevant products, and for improvement of carbon modeling capacity. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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|
Participants: |
Dominique Bachelet, Oregon State University | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Hurtt, G. C., Andrews, A., Bowman, K., Brown, M. E., Chatterjee, A., Escobar, V., Fatoyinbo, L., Griffith, P., Guy, M., Healey, S. P., Jacob, D. J., Kennedy, R., Lohrenz, S., McGroddy, M. E., Morales, V., Nehrkorn, T., Ott, L., Saatchi, S., Sepulveda Carlo, E., Serbin, S. P., Tian, H. 2022. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps. Environmental Research Letters. 17(6), 063010. DOI: 10.1088/1748-9326/ac7407 Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., Healey, S. 2018. Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing. 10(5), 691. DOI: 10.3390/rs10050691 Liu, Y., Piao, S., Gasser, T., Ciais, P., Yang, H., Wang, H., Keenan, T. F., Huang, M., Wan, S., Song, J., Wang, K., Janssens, I. A., Penuelas, J., Huntingford, C., Wang, X., Altaf Arain, M., Fang, Y., Fisher, J. B., Huang, M., Huntzinger, D. N., Ito, A., Jain, A. K., Mao, J., Michalak, A. M., Peng, C., Poulter, B., Schwalm, C., Shi, X., Tian, H., Wei, Y., Zeng, N., Zhu, Q., Wang, T. 2019. Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO2 fertilization. Nature Geoscience. 12(10), 809-814. DOI: 10.1038/s41561-019-0436-1 Schwalm, C. R., Huntzinger, D. N., Michalak, A. M., Schaefer, K., Fisher, J. B., Fang, Y., Wei, Y. 2020. Modeling suggests fossil fuel emissions have been driving increased land carbon uptake since the turn of the 20th Century. Scientific Reports. 10(1). DOI: 10.1038/s41598-020-66103-9 Schwalm, C. R., Schaefer, K., Fisher, J. B., Huntzinger, D., Elshorbany, Y., Fang, Y., Hayes, D., Jafarov, E., Michalak, A. M., Piper, M., Stofferahn, E., Wang, K., Wei, Y. 2019. Divergence in land surface modeling: linking spread to structure. Environmental Research Communications. 1(11), 111004. DOI: 10.1088/2515-7620/ab4a8a Williams, C. A., Gu, H., Jiao, T. 2021. Climate impacts of U.S. forest loss span net warming to net cooling. Science Advances. 7(7). DOI: 10.1126/sciadv.aax8859 Zhou, Y., Williams, C. A., Hasler, N., Gu, H., Kennedy, R. 2021. Beyond biomass to carbon fluxes: application and evaluation of a comprehensive forest carbon monitoring system. Environmental Research Letters. 16(5), 055026. DOI: 10.1088/1748-9326/abf06d | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Williams, C.A., N. Hasler, H. Gu, and Y. Zhou. 2020. Forest Carbon Stocks and Fluxes from the NFCMS, Conterminous USA, 1990-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1829
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| Lin (CMS 2015) (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Towards a Complex Terrain Carbon Monitoring System (CMS-Mountains): Development and Testing in the Western U.S. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
John Lin, University of Utah
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Lin (CMS 2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Despite the need to understand terrestrial biospheric carbon fluxes to account for carbon cycle feedbacks and predict future CO2 concentrations, knowledge of these fluxes at the regional scale remains poor. This is particularly true in mountainous areas, where complex atmospheric flows and relative lack of observations lead to significant uncertainties in carbon fluxes. Yet many mountainous regions also have significant forest cover and biomass—i.e., they are areas with the potential to serve as terrestrial carbon sinks. However, these sinks are highly dynamic and vulnerable to disturbance events, such as drought, insect damage, and wildfires. A strong need exists for the use of satellite remote sensing and modeling to help shed light on carbon dynamics in regions of complex terrain.
Recent remote sensing advances from NASA can now be used to address the observational gap in mountainous areas. First, column-averaged CO2 (XCO2) yields atmospheric constraints on modeled biospheric fluxes in regions where in-situ CO2 observations are absent. Second, retrieval of Solar-Induced Fluorescence (SIF) from space has provided a powerful means to sense physiological signals of gross primary productivity (GPP) at regional to global scales. However, the relationship between SIF and GPP is complicated, and current uncertainties prevent scaling of well-established leaf-level fluorescence mechanisms to interpret GPP at larger scales, especially for coniferous species.
Our proposed research will address the following key scientific questions:
1) How can satellite, atmospheric in-situ, and ecological observations be combined
with atmospheric and biospheric models to inform carbon budgets in regions of
complex terrain?
2) How is satellite-retrieved SIF related to leaf-level physiology?
3) What are the impacts of drought on carbon cycling in mountainous regions?
We propose development and testing of a new Carbon Monitoring System over Mountains (CMS-Mountains) covering the Western U.S., where we will leverage numerous existing efforts in biospheric and atmospheric modeling. We will run the Community Land Model (CLM) at high spatial resolution, assimilating satellite observations of SIF, leaf area index, and snow cover within the Data Assimilation Research Testbed (DART). Signals of simulated biospheric fluxes from CLM-DART will be compared via atmospheric modeling to remotely sensed XCO2. Discrepancies will be minimized through adjustment of the regional fluxes as part of an atmospheric inversion.
In this way, CMS-Mountains will deliver estimates of regional scale carbon fluxes over the Western U.S., along with their uncertainties, constrained by remotely sensed datasets.
While the proposed project will focus on the Western U.S., the framework we develop will be applicable elsewhere. We anticipate the CMS-Mountains platform will ultimately be applied to other regions of complex terrain around the world, driven by remote sensing data in the absence of in-situ measurements.
This project directly addresses the objectives of NASA’s CMS program, as mentioned in the proposal call. We are proposing a study that uses “remote sensing data products to produce and evaluate prototype MRV system approaches”. It will contribute towards “U.S. national efforts toward integrated carbon monitoring” by helping to constrain the U.S. carbon budget for a region that is poorly understood (Western U.S.). Moreover, our project will help “improve the characterization and quantification of errors and uncertainties in existing and/or proposed NASA CMS products” for regions of complex terrain. To our knowledge, existing CMS projects either have a global scope or focus on regions outside of mountainous areas. By focusing on the carbon budget in the Western U.S., an area of complex terrain, our project will help quantify the magnitude and sources of uncertainties in other CMS products over this area. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Jeffrey Anderson, NCAR | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Kannenberg, S. A., Bowling, D. R., Anderegg, W. R. L. 2020. Hot moments in ecosystem fluxes: High GPP anomalies exert outsized influence on the carbon cycle and are differentially driven by moisture availability across biomes. Environmental Research Letters. 15(5), 054004. DOI: 10.1088/1748-9326/ab7b97 Magney, T. S., Bowling, D. R., Logan, B. A., Grossmann, K., Stutz, J., Blanken, P. D., Burns, S. P., Cheng, R., Garcia, M. A., Kohler, P., Lopez, S., Parazoo, N. C., Raczka, B., Schimel, D., Frankenberg, C. 2019. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proceedings of the National Academy of Sciences. 116(24), 11640-11645. DOI: 10.1073/pnas.1900278116 Peters, W., van der Velde, I. R., van Schaik, E., Miller, J. B., Ciais, P., Duarte, H. F., van der Laan-Luijkx, I. T., van der Molen, M. K., Scholze, M., Schaefer, K., Vidale, P. L., Verhoef, A., Warlind, D., Zhu, D., Tans, P. P., Vaughn, B., White, J. W. C. 2018. Increased water-use efficiency and reduced CO2 uptake by plants during droughts at a continental scale. Nature Geoscience. 11(10), 744-748. DOI: 10.1038/s41561-018-0212-7 Raczka, B., Hoar, T. J., Duarte, H. F., Fox, A. M., Anderson, J. L., Bowling, D. R., Lin, J. C. 2021. Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System. Journal of Advances in Modeling Earth Systems. 13(7). DOI: 10.1029/2020MS002421 Raczka, B., Porcar-Castell, A., Magney, T., Lee, J. E., Kohler, P., Frankenberg, C., Grossmann, K., Logan, B. A., Stutz, J., Blanken, P. D., Burns, S. P., Duarte, H., Yang, X., Lin, J. C., Bowling, D. R. 2019. Sustained Nonphotochemical Quenching Shapes the Seasonal Pattern of Solar-Induced Fluorescence at a High-Elevation Evergreen Forest. Journal of Geophysical Research: Biogeosciences. 124(7), 2005-2020. DOI: 10.1029/2018JG004883 Zuromski, L. M., Bowling, D. R., Kohler, P., Frankenberg, C., Goulden, M. L., Blanken, P. D., Lin, J. C. 2018. Solar-Induced Fluorescence Detects Interannual Variation in Gross Primary Production of Coniferous Forests in the Western United States. Geophysical Research Letters. 45(14), 7184-7193. DOI: 10.1029/2018GL077906 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Raczka, B.M., A. Porcar-Castell, T. Magney, J. Lee, P. Kohler, C. Frankenberg, K. Grossmann, B.A. Logan, J. Stutz, P.D. Blanken, S.P. Burns, H.F. Duarte, X. Yang, J.C. Lin, and D.R. Bowling. 2019. CLM Simulated Solar-Induced Fluorescence, Niwot Ridge, Colorado, USA, 1998-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1720
Raczka, B.M., T.J. Hoar, H.F. Duarte, A.M. Fox, J.L. Anderson, D.R. Bowling, and J.C. Lin. 2021. CLM5-DART Regional Carbon Fluxes and Stocks over the Western US, 1998-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1856 Bowling, D.R., and B.A. Logan. 2019. Conifer Needle Pigment Composition, Niwot Ridge, Colorado, USA, 2017-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1723 Bowling, D.R., and B.A. Logan. 2019. Conifer Needle Chlorophyll Fluorescence, Niwot Ridge, Colorado, USA, 2017-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1722 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lin (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Carbon Monitoring System in Mountains (CMS-Mountains): Leveraging Satellite-based Solar-Induced Fluorescence to Understand Forest Drought and Mortality in the Western U.S. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
John Lin, University of Utah
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Lin (CMS 2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Lin (CMS 2022) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Despite the need to understand terrestrial biospheric carbon fluxes to account for carbon cycle feedbacks and predict future CO2 concentrations, knowledge of these fluxes at the regional scale remains poor. This is particularly true in mountainous areas, where complex meteorology and relative lack of observations lead to significant uncertainties in carbon fluxes. Yet mountainous regions are also where significant forest cover and biomass are found—i.e., areas that have the potential to serve as terrestrial carbon sinks.
We propose to build upon the foundation that our team has laid in developing and testing a prototype of the Complex Terrain Carbon Monitoring System (aka “CMSMountains”) over the Western U.S., where we have constructed:
Physiological datasets and knowledge on the controls on leaf-level to stand-level solar-induced fluorescence (SIF), and their relationships to gross primary productivity (GPP)
Community Land Model (CLM) configured for the Western U.S. at high spatial resolution, with the ability to assimilate SIF observations and simulate physically consistent biomass quantities
An advanced assimilation system using the NCAR Data Assimilation Research Testbed (DART), with CLM at its core
We propose to further refine CMS-Mountains and deliver products to key stakeholders by addressing these objectives:
Objective #1: Extract fine-scale information regarding GPP in complex terrain using high resolution SIF and MODIS reflectance data, combined with flux tower data.
Objective #2: Use fine-scale SIF to improve photosynthetic phenology within CLM.
Objective #3: Assimilate fine-scale SIF and other satellite data within CLM to produce regional carbon stock and flux estimates over the Western U.S.
Objective #4: Construct forest health early warning capabilities to engage and support stakeholders.
While the project will focus on the Western U.S., with special attention to California’s Sierra Nevada region and the Colorado Rockies region, the developed framework will be of general applicability. In fact, we anticipate the Complex Terrain CMS that will emerge from this work to ultimately be applied to other regions of complex terrain around the world, driven by remote sensing data in the absence of in-situ measurements.
Project approaches:
• Satellite Remote Sensing: Solar-Induced Fluorescence (SIF) from GOME-2, TROPOMI, and OCO-2/-3 will be used to constrain CLM. We will also investigate the use of new products from ECOSTRESS to constrain land surface temperature (and thereby evapotranspiration and GPP).
• Field Data Analysis: In 2017, we installed and continue to run a tower-based custom spectrometer at the Niwot Ridge AmeriFlux Core flux tower in Colorado. Seven flux towers in the Sierra Nevada mountains of California are also currently running (run by Southern Sierra Critical Zone Observatory and the National Ecological Observatory Network). We will continue using flux tower data from Colorado and California to probe the mechanistic linkage between SIF and GPP.
• Biospheric Modeling: Building upon our team’s previous prototype CMS and DOE projects, in which the Community Land Model (CLM) was applied to the Western U.S., we will adopt the latest CLM release (CLM 5) as the main modeling platform and leverage field data observations to improve the mechanistic representation of SIF.
• Data Assimilation: We will continue using NCAR’s Data Assimilation Research Testbed (DART), a state-of-the-science ensemble Kalman filter data assimilation system widely adopted by the earth system science community. DART will be extended to enable non-linear data assimilation methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Jeffrey Anderson, NCAR | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Cheng, R., Kohler, P., Frankenberg, C. 2022. Impact of radiation variations on temporal upscaling of instantaneous Solar-Induced Chlorophyll Fluorescence. Agricultural and Forest Meteorology. 327, 109197. DOI: 10.1016/j.agrformet.2022.109197 Raczka, B., Hoar, T. J., Duarte, H. F., Fox, A. M., Anderson, J. L., Bowling, D. R., Lin, J. C. 2021. Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System. Journal of Advances in Modeling Earth Systems. 13(7). DOI: 10.1029/2020MS002421 Yang, J. C., Magney, T. S., Albert, L. P., Richardson, A. D., Frankenberg, C., Stutz, J., Grossmann, K., Burns, S. P., Seyednasrollah, B., Blanken, P. D., Bowling, D. R. 2022. Gross primary production (GPP) and red solar induced fluorescence (SIF) respond differently to light and seasonal environmental conditions in a subalpine conifer forest. Agricultural and Forest Meteorology. 317, 108904. DOI: 10.1016/j.agrformet.2022.108904 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Raczka, B.M., T.J. Hoar, H.F. Duarte, A.M. Fox, J.L. Anderson, D.R. Bowling, and J.C. Lin. 2021. CLM5-DART Regional Carbon Fluxes and Stocks over the Western US, 1998-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1856
Turner, A.J., P. Koehler, T. Magney, C. Frankenberg, I. Fung, and R.C. Cohen. 2021. CMS: Daily Gross Primary Productivity over CONUS from TROPOMI SIF, 2018-2021. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1875 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Miller (CMS 2015) (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Disaggregating Amazon Basin fire fluxes using remote sensing of atmospheric carbon monoxide and burned area | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
John Miller, NOAA Global Monitoring Laboratory
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
We propose to use the combination of remote sensing of atmospheric carbon monoxide (CO) from three different satellite sensors -- MOPITT, IASI and TROPOMI -- along with state of the art, high resolution, maps of burned area to determine fire emissions over the Amazon Basin, spatially, temporally, and by fire type. These data will be used with a data assimilation system that will appropriately scale burned area maps to match both in situ and satellite CO data. Calculating emissions from different fire types will allow us to better understand the net climate impact of fire emissions in the Amazon Basin (note that
not all fire emission have a net climate impact). Note that while fire emission modeling approaches based on burned area, like CASA/GFED also calculate fire by type and region, they are based on coarser land surface maps. They also likely underestimate understory fires have trouble identifying fires during high aerosol loading and persistent cloud cover. In contrast, an atmospheric approach, based on measurements of CO and high resolution burned area maps, will allow for integration of carbon emissions from various types of fires, whether or not they can be easily detected from space.
Fire carbon emission by type and area will be a valuable addition to existing methods used to calculate forest carbon emissions as part of REDD (Reducing Emissions from Deforestation and forest Degradation) projects. To this end, we will also conduct a stakeholders workshop involving Brazilian scientists and climate policy representatives to communicate our results and approach and learn about their information needs. The policy-relevant scientific research and stakeholder outreach we propose are both closely aligned with the goals of NASA’s Carbon Monitoring System (CMS).
Specific project deliverables proposed include development of novel burned area products at high resolution from 2010-2018 that will allow for improved classification of burned area and inclusion of hard to detect fires, such as understory fires, in burned area maps. On the atmospheric side, we will conduct a thorough bias assessment of the satellite data using in situ CO data, allowing us to apply bias correction algorithms. Bias corrections are critical to ensure that gradients in the spatially dense CO data are geophysical and do not result in flux biases. Moreover, bias corrected satellite CO products will be made available to the broader community. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Ilse Aben, SRON Netherlands Institute for Space Research | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Publications: | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Mitchell (CMS 2018) (2019) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Remote sensing methods to characterize, quantify and monitor carbon in a continental shelf sea | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Catherine Mitchell, Bigelow Laboratory For Ocean Sciences
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
The oceans play a vital role in the global carbon cycle, and, relative to their size, coastal waters and continental shelf seas are estimated to contribute disproportionately towards oceanic carbon exchange and uptake of carbon dioxide. Increasing levels of atmospheric carbon dioxide leads to an increase in acidity of coastal and oceanic waters, which can potentially have a detrimental effect on calcifying plants and animals. Carbon cycling supports the base of marine ecosystems, hence monitoring carbon stocks and fluxes in shelf seas is vital for coastal communities as these waters are of great economic importance in terms of fisheries, aquaculture and tourism. The overarching goal of this project is to characterize and quantify the carbon stocks and fluxes in the Gulf of Maine, a dynamic, continental shelf sea. We will (1) evaluate, develop, refine and validate remote sensing methods for monitoring different forms of carbon and carbon fluxes, and (2) apply these methods to satellite imagery to analyze the spatial and temporal variability of carbon standing stocks and fluxes. Specifically, the objectives of this proposal are to: (1) quantify the standing stocks of the four different carbon pools (particulate organic carbon, particulate inorganic carbon, dissolved organic carbon, dissolved inorganic carbon) via remote sensing methods and with well-constrained errors, (2) extend satellite surface measurements to determine euphotic-integrated standing stocks with quantified uncertainties for the different carbon pools, (3) understand and quantify the different carbon flux terms and their associated errors via remote sensing methods, and (4) characterize the ability of the Gulf of Maine to act as a net carbon source or sink via remote sensing observations.
The objectives of this project will be achieved by using in situ standing stock and rate observations from the Gulf of Maine North Atlantic Time Series (GNATS) as a means to evaluate and develop remote sensing methods for carbon standing stocks and associated fluxes. We will leverage the GNATS program data to provide well-constrained uncertainties on the carbon monitoring products. GNATS is a unique coastal time series which has been running since 1998, measuring all parts of the carbon cycle. We will apply the validated remote sensing methods to satellite data from 1997 to present (spanning the continuous ocean color satellite record). To calculate carbon standing stocks, we will develop a method to extend the satellite surface measurements to depth. We will use satellite observations to estimate carbon fluxes associated with: primary production, calcification, dissolved organic carbon transfer from rivers-to-sea, carbon dioxide air-sea fluxes, and carbon export from surface waters to depth. The spatial and temporal variability of standing stocks and carbon fluxes will be analyzed to synthesize the observations of different parts of the carbon cycle and determine the Gulf of Maine’s role as a net carbon source or sink.
The methods developed in this project to characterize carbon will result in parameters that are relevant not only to carbon monitoring but for monitoring ocean acidification as well. Coastal and ocean acidification is of concern in the Gulf of Maine region; hence we will collaborate with stakeholders to ensure the science outputs of this project are what are required by their network of state and federal resource managers and industry partners. This project aligns with key findings and recommendations from the Second State of the Carbon Cycle Report as we will (1) expand the GNATS program by creating remote sensing methods to characterize the exchange of carbon and extend this understanding across the whole region, (2) synthesize observations from all four carbon pools and key flux terms exchanging carbon across the system, and (3) provide data products essential for monitoring ocean acidification, a need for stakeholders around the Gulf of Maine. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
William (Barney) Balch, Bigelow Laboratory for Ocean Sciences | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Balch, W. M., Drapeau, D. T., Bowler, B. C., Record, N. R., Bates, N. R., Pinkham, S., Garley, R., Mitchell, C. 2022. Changing Hydrographic, Biogeochemical, and Acidification Properties in the Gulf of Maine as Measured by the Gulf of Maine North Atlantic Time Series, GNATS, Between 1998 and 2018. Journal of Geophysical Research: Biogeosciences. 127(6). DOI: 10.1029/2022JG006790 Brown, M. E., Mitchell, C., Halabisky, M., Gustafson, B., Gomes, H. D. R., Goes, J. I., Zhang, X., Campbell, A. D., Poulter, B. 2023. Assessment of the NASA carbon monitoring system wet carbon stakeholder community: data needs, gaps, and opportunities. Environmental Research Letters. 18(8), 084005. DOI: 10.1088/1748-9326/ace208 Campbell, A. D., Fatoyinbo, T., Charles, S. P., Bourgeau-Chavez, L. L., Goes, J., Gomes, H., Halabisky, M., Holmquist, J., Lohrenz, S., Mitchell, C., Moskal, L. M., Poulter, B., Qiu, H., Resende De Sousa, C. H., Sayers, M., Simard, M., Stewart, A. J., Singh, D., Trettin, C., Wu, J., Zhang, X., Lagomasino, D. 2022. A review of carbon monitoring in wet carbon systems using remote sensing. Environmental Research Letters. 17(2), 025009. DOI: 10.1088/1748-9326/ac4d4d | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Catherine Mitchell & Sunny Pinkham (2024), Ocean Biogeochemistry from Gliders as part of the Gulf of Maine North Atlantic Time Series, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), DOI: 10.5067/V9QLTOEZHY98
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| Moskal (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Teal Carbon – Stakeholder-driven Monitoring of Forested Wetland Carbon | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
L. Monika (Monika) Moskal, University of Washington
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Moskal (CMS 2022) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Terrestrial wetlands are the largest reservoir of carbon in North America, with roughly half of wetland area occurring in forested systems. Wetlands, defined here as areas saturated at a frequency and duration sufficient to support a prevalence of vegetation typically adapted for life in saturated conditions, usually contain more carbon in their soils than upland areas due to prolonged periods of soil saturation. While forested wetlands are important long-term carbon sinks and important in global carbon accounting, they have received relatively little research attention and are, therefore, a significant source of uncertainty in carbon inventories and monitoring systems. The overarching goal of this proposed study is to develop and implement a remote sensing driven, spatiotemporally explicit approach to monitoring total carbon stocks of forested wetlands. Thus, we propose to develop and demonstrate to our stakeholders a rigorous approach for detecting and assessing carbon stocks in forested wetlands and understanding the effects of disturbances and recovery on these stocks. This will improve understanding of differences in carbon storage between forested wetlands and uplands with similar aboveground carbon stocks, across a range of hydrodynamics and moisture regimes, and under pressure from a range of disturbances. Our multiple objectives aim to demonstrate and deploy a novel and accurate way of mapping of forested wetlands and the above- and below-ground carbon stocks associated with these wetlands. The results of this study will not only immediately inform our stakeholders, including about on-theground forest practices of state lands and adaptive management regulations of state forest practices, it also serves as one of few large-scale studies to quantify forest wetland carbon stocks - including belowground storage of carbon in wetland soils as well as the impacts of forestry practices on carbon sources and sinks that will improve regional and global carbon monitoring systems (CMS) and accounting. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Chad Babcock, University of Minnesota | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Campbell, A. D., Fatoyinbo, T., Charles, S. P., Bourgeau-Chavez, L. L., Goes, J., Gomes, H., Halabisky, M., Holmquist, J., Lohrenz, S., Mitchell, C., Moskal, L. M., Poulter, B., Qiu, H., Resende De Sousa, C. H., Sayers, M., Simard, M., Stewart, A. J., Singh, D., Trettin, C., Wu, J., Zhang, X., Lagomasino, D. 2022. A review of carbon monitoring in wet carbon systems using remote sensing. Environmental Research Letters. 17(2), 025009. DOI: 10.1088/1748-9326/ac4d4d | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nehrkorn (CMS 2015) (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Prototype regional carbon monitoring systems for urban regions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Thomas Nehrkorn, AER, Inc
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Nehrkorn (CMS 2013) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
The 2015 COP21 meeting in Paris fundamentally changed the approach to carbon monitoring, reporting, verification and validation (MRV/MRVV). The emphasis on voluntary measures, and the large number of ongoing GHG reduction efforts at sub- national levels in government, non-profit, and private sectors, require monitoring capability at policy-relevant scales: region, state, and city. Urban regions are particularly important because cities account for more than 70% of all global fossil-fuel CO2 emissions, and urban losses of natural gas CH4 equal or exceed emissions from production and processing.
We propose research to develop a prototype MRV system for Boston and the urban Northeastern US, leveraging results of our current CMS project. We will advance our framework and help deploy a similar system in the San Francisco Bay Area, collaborating with the Bay Area Air Quality Management District (BAAQMD). Both cities have strong GHG reduction efforts (Boston's plan was honored at COP21, and the BAAQMD has ambitious GHG reduction goals for their 10-point Climate Action Work Program). We propose new or enhanced capabilities in four key areas: (1) observational networks that
 ground-based remote sensing from new solar-viewing spectrometers and Lidar with observations from space-borne platforms (OCO-2, OCO-3, TROPOMI, and CALIPSO) and in situ networks; (2) novel bottom-up approaches to generate high- resolution flux inventories in urban and surrounding areas; (3) a high-resolution transport modeling (WRF-STILT) framework, coupled to inversion algorithms to provide posterior estimates of fluxes and uncertainties on scales from urban region to neighborhood; and (4) strong engagement with stakeholder communities and local and state entities.
Quantification and reduction of uncertainties are a key focus. We assess bottom-up inventories by comparing with independent estimates; verify meteorological fields used for transport modeling against a wide range of observations; and undertake intensive field studies to quantify systematic errors in emissions estimates.
The San Francisco Bay area and Boston have contrasting meteorological (e.g., marine vs. continental inflow) and biophysical characteristics (e.g., biomes, seasonality, topographical heterogeneity). We plan an intensive study in the Bay Area under auspices of the BAAQMD, and will focus on transferring to the District methods we have developed for bottom-up inventories at high resolution and elements of our network design and analysis. This work will help us to apply our techniques and findings from the Northeast to elsewhere in the US and the world.
We will assess the MRV capability of column-integrated measurements, both from new ground-based FTS instruments, and space-borne platforms (OCO-2 and OCO-3). Our transport modeling framework will take advantage of recent advances in the treatment of near-field emissions and high-resolution modeling for urban areas. Our proposed bottom- up inventory approach for anthropogenic emissions leverages working relationships with stakeholders to enable use of non-standard activity data, and it treats previously neglected sectors (urban biosphere, human respiration) needed to interpret observational data. We plan to widen stakeholder interactions and address user needs by involving interested parties through exposure to pilot data products and methods transfer.
Our proposal addresses core goals of the NNH15ZDA001N-CMS solicitation: 'using remote sensing data products to produce and evaluate prototype MRV system approaches' and 'studies to improve the characterization and quantification of errors and uncertainties [...] in the algorithms, models, and associated methodologies', and 'studies of stakeholder interests and requirements'. The proposed work will benefit from the team's involvement with the OCO-2 Science Team, the Environmental Defense Fund Methane Initiative, and the CMS project led by Dr. A. Andrews. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Bill Callahan, Earth Networks | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Publications: |
Barrera, Y. D., Nehrkorn, T., Hegarty, J., Sargent, M., Benmergui, J., Gottlieb, E., Wofsy, S. C., DeCola, P., Hutyra, L., Jones, T. 2019. Using Lidar Technology To Assess Urban Air Pollution and Improve Estimates of Greenhouse Gas Emissions in Boston. Environmental Science & Technology. 53(15), 8957-8966. DOI: 10.1021/acs.est.9b00650 Barrera, Yanina Débora: Using Lidar Technology and the STILT Model to Assess Air Pollution and Improve Estimates of Greenhouse Gas Emissions in Cities, Ph.D. thesis, July, 2019, 102pp. Decina, S. M., Templer, P. H., Hutyra, L. R. 2018. Atmospheric Inputs of Nitrogen, Carbon, and Phosphorus across an Urban Area: Unaccounted Fluxes and Canopy Influences. Earth's Future. 6(2), 134-148. DOI: 10.1002/2017EF000653 Floerchinger, Cody: Airborne methane flux quantification and source identification using high resolution measurements of ethane and methane, Ph.D. thesis, Harvard University, July, 2019, 161pp. Gately, C. K., Hutyra, L. R. 2017. Large Uncertainties in Urban-Scale Carbon Emissions. Journal of Geophysical Research: Atmospheres. 122(20). DOI: 10.1002/2017JD027359 Gately, C. K., Hutyra, L. R., Peterson, S., Sue Wing, I. 2017. Urban emissions hotspots: Quantifying vehicle congestion and air pollution using mobile phone GPS data. Environmental Pollution. 229, 496-504. DOI: 10.1016/j.envpol.2017.05.091 Hardiman, B. S., Wang, J. A., Hutyra, L. R., Gately, C. K., Getson, J. M., Friedl, M. A. 2017. Accounting for urban biogenic fluxes in regional carbon budgets. Science of The Total Environment. 592, 366-372. DOI: 10.1016/j.scitotenv.2017.03.028 Jones, Taylor: Advances in Environmental Measurement Systems: Remote Sensing of Urban Methane Emissions and Tree Sap Flow Quantification, Ph. D. Thesis, Harvard University, Sep. 2019. Propp, Adrienne M., "MethaneSat: Detecting Methane Emissions from the Barnett Shale Region", Senior Thesis in Applied Mathematic, Harvard Paulson School of Engineering and Applied Science, 2017, 83pp. Reinmann, A. B., Hutyra, L. R. 2016. Edge effects enhance carbon uptake and its vulnerability to climate change in temperate broadleaf forests. Proceedings of the National Academy of Sciences. 114(1), 107-112. DOI: 10.1073/pnas.1612369114 Sargent, M. R., Floerchinger, C., McKain, K., Budney, J., Gottlieb, E. W., Hutyra, L. R., Rudek, J., Wofsy, S. C. 2021. Majority of US urban natural gas emissions unaccounted for in inventories. Proceedings of the National Academy of Sciences. 118(44). DOI: 10.1073/pnas.2105804118 Sargent, M., Barrera, Y., Nehrkorn, T., Hutyra, L. R., Gately, C. K., Jones, T., McKain, K., Sweeney, C., Hegarty, J., Hardiman, B., Wang, J. A., Wofsy, S. C. 2018. Anthropogenic and biogenic CO 2 fluxes in the Boston urban region. Proceedings of the National Academy of Sciences. 115(29), 7491-7496. DOI: 10.1073/pnas.1803715115 Viatte, C., Lauvaux, T., Hedelius, J. K., Parker, H., Chen, J., Jones, T., Franklin, J. E., Deng, A. J., Gaudet, B., Verhulst, K., Duren, R., Wunch, D., Roehl, C., Dubey, M. K., Wofsy, S., Wennberg, P. O. 2017. Methane emissions from dairies in the Los Angeles Basin. Atmospheric Chemistry and Physics. 17(12), 7509-7528. DOI: 10.5194/acp-17-7509-2017 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Gately, C., and L.R. Hutyra. 2018. CMS: CO2 Emissions from Fossil Fuels Combustion, ACES Inventory for Northeastern USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1501
Sargent, M., S.C. Wofsy, and T. Nehrkorn. 2018. CO2 Observations, Modeled Emissions, and NAM-HYSPLIT Footprints, Boston MA, 2013-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1586 Nehrkorn, T., M. Sargent, S.C. Wofsy, and M. Mountain. 2018. WRF-STILT Gridded Footprints for Boston, MA, USA, 2013-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1572 Nehrkorn, T., M. Sargent, S.C. Wofsy, and M. Mountain. 2018. WRF-STILT Particle Trajectories for Boston, MA, USA, 2013-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1596 Gately, C., L.R. Hutyra, and I.S. Wing. 2019. DARTE Annual On-road CO2 Emissions on a 1-km Grid, Conterminous USA, V2, 1980-2017. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1735 Sargent, M., S.C. Wofsy, C. Floerchinger, J. Buddy, and E.W. Gottlieb. 2022. Methane and Ethane Observations for Boston, MA, 2012-2020. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1982 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Olofsson (CMS 2015) (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Tracking carbon emissions and removals by time series analysis of the land surface: prototype application in tropical MRV systems compliant with IPCC Tier 3 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Pontus Olofsson, NASA MSFC
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Woodcock (CMS 2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Many tropical countries are experiencing high rates of forest disturbance with of cycles of degradation, cultivation and recovery, but neither the activities nor the terrestrial carbon dynamics associated with the activities are properly tracked in existing REDD+ related Measurement, Reporting and Verification (MRV) systems. This situation is especially true for post-disturbance landscapes and degraded forests, as the trajectories of the land surface activities and carbon dynamics following disturbance are gradual in nature and inherently difficult to monitor. We propose to improve modeling of the carbon dynamics of areas that have experienced disturbance by combining a time series-based approach for monitoring changes on the land surface with a spatially and temporally explicit carbon bookkeeping approach. We have developed algorithms that track the land surface by analyzing time series of all available observations from the Landsat sensors complemented by data from space-borne radar instruments and other optical sensors. Implementations are currently underway across the United States and the Colombian Amazon. Additionally, we have developed open source software tools and educational materials that provide detailed hands-on instructions in support of capacity building efforts in collaboration with SilvaCarbon.
We propose a novel framework for estimation of carbon emissions and removals by including detailed information on the fate of the landscape. We will modify a recently developed bookkeeping model so that it runs at the pixel-level (spatially explicit) by directly integrating the results of time series information on conversion between land categories and forest degradation. The characterization of post-disturbance tropical landscapes is critical for accurate accounting of terrestrial carbon pools and fluxes because of the high productivity and carbon density of forests in this region. Therefore, in addition to the time series analysis of the land surface, the temporal dynamics of vegetation structure and recovery following disturbance will be investigated using existing space-borne lidar data in combination with data from upcoming NASA lidar missions. Following best practices protocols for statistical inference of change in area and carbon emissions, unbiased estimates with the uncertainty quantified in the form of confidence intervals will be constructed. Prototype applications of the proposed methodology will be implemented in Colombia and Cambodia, two tropical countries representing different levels of capacity and different types of forest disturbance. A SilvaCarbon effort is underway to complete a comprehensive time series-based analysis of the conversions between the land categories and post-disturbance landscapes across the Colombian Amazon that will be used together with a set of existing field measurements of biomass in a prototype application of the proposed methodology. The methodology will be implemented in Cambodia, where field-measured data on biomass are scarcer, capacity needs greater and the rate of deforestation and forest degradation higher. Engagement with stakeholders and countries will be enhanced by collaboration with in-country SilvaCarbon activities focused on enhancing and supporting systems of MRV for REDD+ activities (including the provision of input for designing field measurement programs). In addition, a spatially and temporally explicit model for estimating the carbon dynamics related to land surface activities will be added to the open source suite of software to provide a more complete framework for the enhancement of MRV systems in the tropics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Gustavo Galindo, IDEAM, Ministry of Environment and Sustainable Development, Colombia Government | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Publications: |
Arevalo, P., Bullock, E. L., Woodcock, C. E., Olofsson, P. 2020. A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine. Frontiers in Climate. 2. DOI: 10.3389/fclim.2020.576740 Arevalo, P., Olofsson, P., Woodcock, C. E. 2020. Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting. Remote Sensing of Environment. 238, 111051. DOI: 10.1016/j.rse.2019.01.013 Bullock, E. L., Woodcock, C. E., Souza, C., Olofsson, P. 2020. Satellite-based estimates reveal widespread forest degradation in the Amazon. Global Change Biology. 26(5), 2956-2969. DOI: 10.1111/gcb.15029 Olofsson, P., Arevalo, P., Espejo, A. B., Green, C., Lindquist, E., McRoberts, R. E., Sanz, M. J. 2020. Mitigating the effects of omission errors on area and area change estimates. Remote Sensing of Environment. 236, 111492. DOI: 10.1016/j.rse.2019.111492 Tang, X., Hutyra, L. R., Arevalo, P., Baccini, A., Woodcock, C. E., Olofsson, P. 2020. Spatiotemporal tracking of carbon emissions and uptake using time series analysis of Landsat data: A spatially explicit carbon bookkeeping model. Science of The Total Environment. 720, 137409. DOI: 10.1016/j.scitotenv.2020.137409 Tang, X., Woodcock, C. E., Olofsson, P., Hutyra, L. R. 2021. Spatiotemporal assessment of land use/land cover change and associated carbon emissions and uptake in the Mekong River Basin. Remote Sensing of Environment. 256, 112336. DOI: 10.1016/j.rse.2021.112336 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Arévalo, P. 2020. CMS: Landsat-derived Annual Land Cover Maps for the Colombian Amazon, 2001-2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1783
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| Ott (CMS 2016) (2017) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | GEOS-Carb III: Delivering mature carbon flux and concentration datasets in support of NASA's Carbon Monitoring System | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Lesley Ott, NASA GSFC GMAO
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Ott (CMS 2014) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Ott (CMS 2020) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
This proposal is to extend NASA GSFC's contributions to the Carbon Monitoring System (CMS). Since its 2010 inception, these efforts by GSFC-based modeling teams have continuously provided the only complete and physically consistent set of global flux and atmospheric concentration data products to CMS. The proposed work will draw on the unique capabilities of NASA's Goddard Earth Observing System (GEOS) models and data assimilation system and consists of three main components: (i) production and refinement of observationally constrained 'bottom-up' atmosphere-ocean and atmosphere- land biosphere fluxes, and fossil fuel emissions from 2003 to 2019; (ii) production of global carbon reanalyses at unprecedented spatial resolution that incorporate multiple satellite (GOSAT, OCO-2) and in situ datasets; (iii) evaluation of 'bottom-up' flux estimates through comparison with 'top-down' inversion flux estimates. A central component of these efforts has been the use of meteorological forcing provided by NASA's Modern Era Retrospective-analysis for Research and Applications 2 (MERRA-
2) to produce a consistent picture of the interactions between weather, climate, and the carbon cycle. By extending land and ocean model-based flux estimates over a 17-year period that includes notable climatic variability, we will evaluate the ability of these models to reproduce the interannual variability of atmospheric carbon observations. These flux estimates will also incorporate a number of improvements implemented during earlier phases of CMS and refine methods of uncertainty quantification. We will use a combination of diagnostic and prognostic land biosphere models to enhance understanding of carbon flux processes. Ocean flux estimates will be further constrained through assimilation of multiple satellite ocean color observations. We will also exploit information on meteorological uncertainty produced by GMAO's new ensemble-based data assimilation system to refine transport uncertainty estimates that were provided for the first time in Phase 3 of CMS. These ocean and land fluxes, fossil fuel emissions and their associated uncertainties will be used together in the GEOS-5 carbon data assimilation system (CDAS) to produce a carbon reanalysis at 12.5-km resolution, providing the most complete, data-driven picture of atmospheric greenhouse gases to date. An important component of this effort will be to reduce the latency of flux datasets, providing information on the global carbon in support of scientific and stakeholder end- users. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Nikolay Balashov, NASA GSFC / ESSIC UMD | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Fu, Z., Stoy, P. C., Poulter, B., Gerken, T., Zhang, Z., Wakbulcho, G., Niu, S. 2019. Maximum carbon uptake rate dominates the interannual variability of global net ecosystem exchange. Global Change Biology. 25(10), 3381-3394. DOI: 10.1111/gcb.14731 Gregg, W. W., Rousseaux, C. S., Franz, B. A. 2017. Global trends in ocean phytoplankton: a new assessment using revised ocean colour data. Remote Sensing Letters. 8(12), 1102-1111. DOI: 10.1080/2150704X.2017.1354263 Oda, T., Bun, R., Kinakh, V., Topylko, P., Halushchak, M., Marland, G., Lauvaux, T., Jonas, M., Maksyutov, S., Nahorski, Z., Lesiv, M., Danylo, O., Horabik-Pyzel, J. 2019. Errors and uncertainties in a gridded carbon dioxide emissions inventory. Mitigation and Adaptation Strategies for Global Change. 24(6), 1007-1050. DOI: 10.1007/s11027-019-09877-2 Oda, T., Maksyutov, S., Andres, R. J. 2018. The Open-source Data Inventory for Anthropogenic CO<sub>2</sub>, version 2016 (ODIAC2016): a global monthly fossil fuel CO<sub>2</sub> gridded emissions data product for tracer transport simulations and surface flux inversions. Earth System Science Data. 10(1), 87-107. DOI: 10.5194/essd-10-87-2018 Wang, J. S., Oda, T., Kawa, S. R., Strode, S. A., Baker, D. F., Ott, L. E., Pawson, S. 2020. The impacts of fossil fuel emission uncertainties and accounting for 3-D chemical CO2 production on inverse natural carbon flux estimates from satellite and in situ data. Environmental Research Letters. 15(8), 085002. DOI: 10.1088/1748-9326/ab9795 Weir, B., Crisp, D., O'Dell, C. W., Basu, S., Chatterjee, A., Kolassa, J., Oda, T., Pawson, S., Poulter, B., Zhang, Z., Ciais, P., Davis, S. J., Liu, Z., Ott, L. E. 2021. Regional impacts of COVID-19 on carbon dioxide detected worldwide from space. Science Advances. 7(45). DOI: 10.1126/sciadv.abf9415 Weir, B., Ott, L. E., Collatz, G. J., Kawa, S. R., Poulter, B., Chatterjee, A., Oda, T., Pawson, S. 2021. Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems. Atmospheric Chemistry and Physics. 21(12), 9609-9628. DOI: 10.5194/acp-21-9609-2021 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Lesley Ott (2020), GEOS-Carb CASA-GFED Daily Fire and Fuel Emissions 0.5 degree x 0.5 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/7TQL49XLIMBD
Lesley Ott (2020), GEOS-Carb CASA-GFED Daily Fire and Fuel Emissions 0.5 degree x 0.5 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/IYZIZJ8ZFZHU Lesley Ott (2020), GEOS-Carb CASA-GFED Monthly Fire Fuel NPP Rh NEE Fluxes 0.5 degree x 0.5 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/03147VMJE8J9 Lesley Ott (2020), GEOS-Carb CASA-GFED 3-hourly Ecosystem Exchange Fluxes 0.5 degree x 0.625 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/5MQJ64JTBQ40 Lesley Ott (2020), GEOS-Carb CASA-GFED Monthly Fire Fuel NPP Rh NEE Fluxes 0.5 degree x 0.5 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/FZU47Y00Q79U Lesley Ott (2020), GEOS-Carb CASA-GFED 3-hourly Ecosystem Exchange Fluxes 0.5 degree x 0.625 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/VQPRALE26L20 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Poulter (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Continuation of CMS Applications Efforts: Stakeholder Engagement and Socioeconomic Studies on the Value of CMS Data Products for User Organizations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Benjamin (Ben) Poulter, NASA GSFC
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Escobar (CMS 2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
The overall goal of our project is the continuation of the stakeholder engagement and applications efforts started in 2013, to enable a greater impact of NASA space-based observations in science and applications in service to the nation, and global society. The Carbon Monitoring System, CMS, applications efforts have already developed and implemented successfully a CMS Applications Framework, which can be used as guidance for future CMS projects and/or by other NASA Earth science missions and programs in their stakeholder engagement and applications efforts. We plan to continue and expand the work layout in the framework, with a particular emphasis on the coordination of joint applications workshops and data tutorials on how to use CMS data products for diverse applications and under different scenarios; the publication of synthesis reports on the data needs, interests, applications, challenges, lessons learned, and impact of the use of CMS data products for the stakeholder community; the development of flow diagrams illustrating how CMS data evolves from science to beneficial support of agency decisions and operations for some specific federal government agencies; and conduct socioeconomic analysis on the impact of CMS products on earth-system process representation/improvement and uncertainty analysis, as well as the development of case studies to evaluate the socioeconomic benefits of select CMS products in advancing carbon-climate science and stakeholder organizations decision processes. We expect to answer the following research question: what are the economic impacts of utilizing CMS products to reduce uncertainty in the climate system, how does this information translate to impacts on mitigation efforts, and how can CMS products bring value to stakeholder needs and decisions? We propose to research the utility of the carbon monitoring data products for advancing carbon science, management and policy decision, and for providing guidance on key attributes of the current and potential future CMS products to the NASA CMS program. We plan to identify scientist and stakeholder interests and requirements, and to ensure that CMS engages and understands the relationships within the user community for carbon monitoring products. This will facilitate greater uptake of these products as they become available, and enhance their scientific and societal impacts. The data needs and lessons learned reports will be developed from the feedback and results of the policy speaker series seminars, and the applications workshops & data tutorials. We expect to engage the private sector and non-profit partners in these efforts, and look for long-lasting partnerships. We will also enhance the current framework that we have developed for evaluating socioeconomic benefits of archived and planned NASA CMS products with regard to their value and benefits (public, policy and socio-economic) for advancing carbonclimate science and decision-making needs. This enhancement includes using a data assimilation component in the socioeconomic evaluation framework to assess the impact of short duration (i.e. less than a decade) and limited spatial scale (i.e. ecosystem/region specific) CMS data products. The impact of this work is vital for: (1) an in-depth understanding of the real data/information needs and interests of key stakeholders related to carbon monitoring and MRV; (2) providing guidance on uses and applications to potential users of CMS data products; (3) facilitate a smooth incorporation of the CMS data products into the decision-making process of the stakeholder, and ensure that the product becomes operational within the organization; and (4) a detailed evaluation of the socioeconomic value of select CMS data products. Finally, this study will also contribute to both improving representation of key underlying processes in carbon-climate models and uncertainties in their future projections. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Farhan Akhtar, U.S. Department of State | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Brown, M. E., Cooper, M. W., Griffith, P. C. 2020. NASA's carbon monitoring system (CMS) and arctic-boreal vulnerability experiment (ABoVE) social network and community of practice. Environmental Research Letters. 15(11), 115014. DOI: 10.1088/1748-9326/aba300 Brown, M. E., Escobar, V. M., Younis, F. M., Sepulveda Carlo, E., McGroddy, M., Arias, S. D., Griffith, P., Hurtt, G. 2022. Scientist-stakeholder relationships drive carbon data product transfer effectiveness within NASA program. Environmental Research Letters. 17(9), 095004. DOI: 10.1088/1748-9326/ac87bf Brown, M. E., Mitchell, C., Halabisky, M., Gustafson, B., Gomes, H. D. R., Goes, J. I., Zhang, X., Campbell, A. D., Poulter, B. 2023. Assessment of the NASA carbon monitoring system wet carbon stakeholder community: data needs, gaps, and opportunities. Environmental Research Letters. 18(8), 084005. DOI: 10.1088/1748-9326/ace208 McDowell, N. G., Allen, C. D., Anderson-Teixeira, K., Aukema, B. H., Bond-Lamberty, B., Chini, L., Clark, J. S., Dietze, M., Grossiord, C., Hanbury-Brown, A., Hurtt, G. C., Jackson, R. B., Johnson, D. J., Kueppers, L., Lichstein, J. W., Ogle, K., Poulter, B., Pugh, T. A. M., Seidl, R., Turner, M. G., Uriarte, M., Walker, A. P., Xu, C. 2020. Pervasive shifts in forest dynamics in a changing world. Science. 368(6494). DOI: 10.1126/science.aaz9463 Murray-Tortarolo, G., Poulter, B., Vargas, R., Hayes, D., Michalak, A. M., Williams, C., Windham-Myers, L., Wang, J. A., Wickland, K. P., Butman, D., Tian, H., Sitch, S., Friedlingstein, P., O'Sullivan, M., Briggs, P., Arora, V., Lombardozzi, D., Jain, A. K., Yuan, W., Seferian, R., Nabel, J., Wiltshire, A., Arneth, A., Lienert, S., Zaehle, S., Bastrikov, V., Goll, D., Vuichard, N., Walker, A., Kato, E., Yue, X., Zhang, Z., Chaterjee, A., Kurz, W. 2022. A Process-Model Perspective on Recent Changes in the Carbon Cycle of North America. Journal of Geophysical Research: Biogeosciences. 127(9). DOI: 10.1029/2022JG006904 Poulter, B, JG Canadell, DJ Hayes, RL Thompson. Balancing Greenhouse Gas Budgets: Accounting for Natural and Anthropogenic Flows of CO2 and Other Trace Gases. 1st ed. Elsevier, 2022. Rosentreter, J. A., Borges, A. V., Deemer, B. R., Holgerson, M. A., Liu, S., Song, C., Melack, J., Raymond, P. A., Duarte, C. M., Allen, G. H., Olefeldt, D., Poulter, B., Battin, T. I., Eyre, B. D. 2021. Half of global methane emissions come from highly variable aquatic ecosystem sources. Nature Geoscience. 14(4), 225-230. DOI: 10.1038/s41561-021-00715-2 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Qi (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||
| Project Title: | An Aquatic Ecosystem Carbon Monitoring System (AECMS) for Quantifying Carbon Fluxes, Sources and Sinks of Inland Waters: Development and Verification in the Upper Mississippi River Basin | |||||||||||||||||||||||||||||||||||||
|
Science Team |
Junyu Qi, University of Maryland
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||
| Abstract: |
Recent studies clearly show that inland waters not only transport C from land to coast, but also internally produce new and modify C, and act as significant C sinks and sources. However, our understanding and quantification of C stocks and flows of aquatic ecosystems is subject to large uncertainties. For example, the current estimates of C fluxes, sources and sinks (e.g. terrestrially-derived C, burial, and outgassing) of North American aquatic ecosystems are subject to an uncertainty of 100%. Furthermore, the components of the terrestrially-derived C that enters inland waters are very poorly constrained. The large uncertainties of aquatic C budgets impede reliable monitoring and effective management of C important to human sustainability. Therefore, our goal is to develop and test an Aquatic Ecosystem Carbon Monitoring System (AECMS) that integrates NASA remote sensing data, in situ measurements and process-based modeling to monitor major organic C (OC) fluxes and stocks of inland waters.
To achieve the proposed AECMS, we will perform research tasks in five aspects: [1] enhance coupled terrestrial-aquatic C cycling modeling by further improving the Soil and Water Assessment Tool with C cycling representation (SWAT-C) with respect to forest and dissolved OC simulation); [2] synthesize and collect in situ measurements of ecosystem variables (including source attribution information (terrestrial vs internal) and aquatic photosynthesis and respiration derived from isotopic and chemical measurements) that depicts the cycling of C and other relevant elements across the terrestrial-aquatic continuum; [3] integrate a wide array of NASA remote sensing data products, as well as numerous other sources of geospatial data, for characterizing both terrestrial and aquatic ecosystems and their interactions; [4] constrain and characterize uncertainties of the AECMS using the remote sensing observations and in situ measurements; and [5] apply the AECMS to estimate major OC fluxes, sources and sinks (e.g. terrestrially derived particulate and dissolved OC through different pathways, aquatic primary production and respiration, transformation and mineralization of particulate dissolved OC, and OC burial) along the river networks of the Upper Mississippi River Basins, and assess their response to changes in land use land cover and climate. Collectively, these research efforts will help address knowledge gaps in aquatic OC budgets by refining estimates of terrestrially-derived OC entering river networks and quantifying the fate of OC in inland waters.
The outcome of this research will directly contribute to the NASA CMS solicited research topic on “Develop and/or refine aquatic carbon sources, sinks, and fluxes using data products or approaches that integrate, or provide the basis for integrating, remote sensing data from current or future NASA missions.” The new data-model tool and resultant new C budget datasets will be shared with the scientific community and stakeholders through multiple channels, such as sharing the CMS data at ORNL DAAC for long-term archive, transferring the tool and knowledge to benefit long-term land and water research programs (e.g. USDA-CEAP, EPA-HAWQS, and USGS inland water carbon research), benefiting local land and water management, and seeking opportunities to synergize with other CMS activities. These efforts are expected to facilitating sustained use of NASA remote sensing data to support societal benefits. | |||||||||||||||||||||||||||||||||||||
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|
Participants: |
Jeffrey Arnold, USDA | |||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: | None provided. | |||||||||||||||||||||||||||||||||||||
| Randerson (CMS 2016) (2017) | |||||||||||||||||||||||||||||||||||||||
| Project Title: | Optimizing the Global Fire Emissions Database for carbon monitoring | ||||||||||||||||||||||||||||||||||||||
|
Science Team |
James (Jim) Randerson, University Of California, Irvine
(Project Lead)
| ||||||||||||||||||||||||||||||||||||||
| Solicitation: | NASA: Carbon Monitoring System (2016) | ||||||||||||||||||||||||||||||||||||||
| Abstract: |
Fire is a critical component of the Earth system. NASA’s Earth observing satellites monitor active fires, map burned area, and estimate trace gas and aerosol emissions from fires worldwide. Globally, fires emit more than 2 Pg C per year, yet important challenges remain with respect to integrating fire emissions into carbon monitoring systems. One impediment to routine monitoring, reporting, and verification (MRV) of fires is the need for emissions information over a range of temporal and spatial scales. On daily to weekly time scales, near-real time fire emissions data are needed to support forecasts and response efforts during wildfire emergencies. Time series of annual fire emissions by fire type, such as products from the Global Fire Emissions Database (GFED), are important for greenhouse gas reporting at regional, national, and global scales, including the Global Carbon Project’s annual Carbon Budget. Over longer time scales, the 17-year Moderate Resolution Imaging Spectrometer (MODIS) data record now captures important year-to- year variability and secular trends in global fire activity from changing land use and climate.
Here, we propose to develop a suite of GFED products to better integrate fire emissions information into existing carbon monitoring systems. New products specifically target carbon monitoring system and stakeholder needs for low-latency data products, improved estimates of global burning and trends, and a detailed assessment of the direct and indirect contributions from fire to the global methane budget. First, we will create a near- real time GFED emissions product using new, 375 m resolution Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections. In parallel, we propose to develop and release GFED Version 5, building on improvements to Collection 6 MODIS burned area, VIIRS active fire detections, and novel constraints on fuel loads from biomass datasets developed by prior NASA Carbon Monitoring System (CMS) projects. Third, we will run GEOS-Chem atmospheric model simulations to estimate the influence of global fire activity on methane emissions and methane lifetimes based on changing hydroxyl radical (OH) concentrations. Fourth, satellite data suggest a strong decline in savanna and grassland fires over the past two decades; we propose to evaluate changing fire dynamics using individual fire information and higher resolution Landsat 8 and Sentinel-2 data for case study regions with declining fire activity. Finally, we will update and expand the online GFED Analysis Tool to serve near-real time GFED5 products and support stakeholder interest in fire activity and reporting at a range of spatial and temporal scales. This suite of GFED5 products specifically targets data needs for ongoing CMS-Flux research, global analysis of CO2 and CH4 by the Global Carbon Project and NOAA's Carbon Tracker, and scientific and media interest in large wildfire complexes as they develop.
The proposed research directly responds to three components of the ROSES A.7 CMS research announcement, including the need to “advance remote sensing-based approaches to monitoring, reporting, and verification,” “extend, and/or improve existing CMS products for biomass or flux resulting from NASA’s first phases of CMS pilot studies,” and “enhance national reported carbon emissions inventories.” The proposed effort will provide consistent global fire emissions data products for over two decades, grounded
in NASA satellite observations, to support greenhouse gas MRV efforts and advance our understanding of fire in the Earth System. Investments in near-real time GFED products and an online data delivery and analysis system will harness the full potential of
NASA’s remote sensing observations for stakeholder engagement and research needs on fire carbon losses, atmospheric chemistry, and attribution of changing fire dynamics to human activity and climate. | ||||||||||||||||||||||||||||||||||||||
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|
Participants: |
Niels Andela, NASA GSFC | ||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Andela, N., Morton, D. C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., van der Werf, G. R., Randerson, J. T. 2019. The Global Fire Atlas of individual fire size, duration, speed and direction. Earth System Science Data. 11(2), 529-552. DOI: 10.5194/essd-11-529-2019 Andela, N., Morton, D. C., Schroeder, W., Chen, Y., Brando, P. M., Randerson, J. T. 2022. Tracking and classifying Amazon fire events in near real time. Science Advances. 8(30). DOI: 10.1126/sciadv.abd2713 Chen, Y., Hantson, S., Andela, N., Coffield, S. R., Graff, C. A., Morton, D. C., Ott, L. E., Foufoula-Georgiou, E., Smyth, P., Goulden, M. L., Randerson, J. T. 2022. California wildfire spread derived using VIIRS satellite observations and an object-based tracking system. Scientific Data. 9(1). DOI: 10.1038/s41597-022-01343-0 Chen, Y., Langenbrunner, B., Randerson, J. T. 2018. Future Drying in Central America and Northern South America Linked With Atlantic Meridional Overturning Circulation. Geophysical Research Letters. 45(17), 9226-9235. DOI: 10.1029/2018GL077953 Chen, Y., Morton, D. C., Andela, N., van der Werf, G. R., Giglio, L., Randerson, J. T. 2017. A pan-tropical cascade of fire driven by El Nino/Southern Oscillation. Nature Climate Change. 7(12), 906-911. DOI: 10.1038/s41558-017-0014-8 Chen, Y., Randerson, J. T., Coffield, S. R., Foufoula-Georgiou, E., Smyth, P., Graff, C. A., Morton, D. C., Andela, N., Werf, G. R., Giglio, L., Ott, L. E. 2020. Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales. Journal of Advances in Modeling Earth Systems. 12(9). DOI: 10.1029/2019MS001955 Chen, Y., Randerson, J. T., Coffield, S. R., Foufoula-Georgiou, E., Smyth, P., Graff, C. A., Morton, D. C., Andela, N., Werf, G. R., Giglio, L., Ott, L. E. 2020. Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales. Journal of Advances in Modeling Earth Systems. 12(9). DOI: 10.1029/2019MS001955 Coffield, S. R., Graff, C. A., Chen, Y., Smyth, P., Foufoula-Georgiou, E., Randerson, J. T. 2019. Machine learning to predict final fire size at the time of ignition. International Journal of Wildland Fire. 28(11), 861. DOI: 10.1071/WF19023 Gorris, M. E., Treseder, K. K., Zender, C. S., Randerson, J. T. 2019. Expansion of Coccidioidomycosis Endemic Regions in the United States in Response to Climate Change. GeoHealth. 3(10), 308-327. DOI: 10.1029/2019GH000209 Langenbrunner, B., Pritchard, M. S., Kooperman, G. J., Randerson, J. T. 2019. Why Does Amazon Precipitation Decrease When Tropical Forests Respond to Increasing CO 2 ? Earth's Future. 7(4), 450-468. DOI: 10.1029/2018EF001026 Levine, P. A., Randerson, J. T., Chen, Y., Pritchard, M. S., Xu, M., Hoffman, F. M. 2019. Soil Moisture Variability Intensifies and Prolongs Eastern Amazon Temperature and Carbon Cycle Response to El Nino-Southern Oscillation. Journal of Climate. 32(4), 1273-1292. DOI: 10.1175/JCLI-D-18-0150.1 Wang, J. A., Baccini, A., Farina, M., Randerson, J. T., Friedl, M. A. 2021. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nature Climate Change. 11(5), 435-441. DOI: 10.1038/s41558-021-01027-4 Wiggins, E. B., Andrews, A., Sweeney, C., Miller, J. B., Miller, C. E., Veraverbeke, S., Commane, R., Wofsy, S., Henderson, J. M., Randerson, J. T. 2021. Boreal forest fire CO and CH<sub>4</sub> emission factors derived from tower observations in Alaska during the extreme fire season of 2015. Atmospheric Chemistry and Physics. 21(11), 8557-8574. DOI: 10.5194/acp-21-8557-2021 Wiggins, E. B., Czimczik, C. I., Santos, G. M., Chen, Y., Xu, X., Holden, S. R., Randerson, J. T., Harvey, C. F., Kai, F. M., Yu, L. E. 2018. Smoke radiocarbon measurements from Indonesian fires provide evidence for burning of millennia-aged peat. Proceedings of the National Academy of Sciences. 115(49), 12419-12424. DOI: 10.1073/pnas.1806003115 Woodard, D. L., Davis, S. J., Randerson, J. T. 2018. Economic carbon cycle feedbacks may offset additional warming from natural feedbacks. Proceedings of the National Academy of Sciences. 116(3), 759-764. DOI: 10.1073/pnas.1805187115 | ||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Andela, N., D.C. Morton, L. Giglio, and J.T. Randerson. 2019. Global Fire Atlas with Characteristics of Individual Fires, 2003-2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1642
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| Saatchi (CMS 2015) (2016) | |||||||||||||||||||||||||||||||||||||||
| Project Title: | Annual GHG Inventory and MRV System for the US Forestlands | ||||||||||||||||||||||||||||||||||||||
|
Science Team |
Sassan Saatchi, Jet Propulsion Laboratory / Caltech
(Project Lead)
| ||||||||||||||||||||||||||||||||||||||
| Solicitation: | NASA: Carbon Monitoring System (2015) | ||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Saatchi (CMS 2011) Masek-Nemani-Saatchi-Tucker (2009) | ||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Saatchi (CMS 2020) | ||||||||||||||||||||||||||||||||||||||
| Abstract: |
We propose to use the CMS infrastructure developed in our earlier pilot project and MRV prototypes to perform an updated annual Green House Gas (GHG) inventory of the US forestlands and contribute to the existing national MRV and the US Forest Service and the Environmental Protection Agency (EPA) national reporting to the United Nation Framework Convention on Climate Change (UNFCCC). The proposed work will produce spatial products on carbon stocks and fluxes that include stakeholder requirements on attributions and uncertainty and deliver at low-latency in order to be integrated in the national carbon management, decision making, and the official national MRV system. With the participation of stakeholders in the process of developing the products, for the first time, NASA CMS program will have the opportunity to directly contribute in the national GHG inventory.
The overall objectives of the proposed work are:
1. Develop spatial products on carbon pools and fluxes over the US forestlands
including Alaska with the low latency to be used for annual reporting
2. Quantify all sources and sinks and attributions by combining spatial data on
forest cover change, pools, and fluxes into the CARDAMOM model data
fusion framework
3. Quantify and report uncertainty for all components of sources and sinks in the
US forestlands
4. Benchmark the methodology and products for integration in the national MRV
system and future stakeholder’s activities.
The proposed CMS activity will advance the remote sensing techniques and product by: 1) quantifying changes of forest cover with all natural and anthropogenic attributions at the annual cycle with low-latency delivery, 2) integrating remote sensing and in-situ observations on carbon pools and fluxes in a diagnostic ecosystem carbon balance model to improve carbon sinks and sources for different attributions associated with annual changes in the US forestlands, 3) improve characterization and quantification of errors and uncertainty following the IPCC good practice guidelines, and 4) including stakeholders interests and requirements by directly involving the user community and allowing the evaluation of CMS products for decision making and integration in the national MRV system.
By including Alaska, the proposed work will use satellite and airborne and existing in- situ observations to compensate for the lack of extensive forest inventory data and provide, for the first time, the GHG inventory including all pools and fluxes, for both managed and unmanaged forests of the region. The methodology, including the CMS infrastructure for data processing, analysis, uncertainty assessment and data products will be benchmarked to allow integration in national MRV system. The benchmarking will
also provide transparency in the entire performance of the carbon monitoring infrastructure for reporting and verification in future carbon trading protocols. | ||||||||||||||||||||||||||||||||||||||
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|
Participants: |
Alexis (Anthony) Bloom, Jet Propulsion Lab, California Institute of Technology | ||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Hogan, J. A., Domke, G. M., Zhu, K., Johnson, D. J., Lichstein, J. W. 2024. Climate change determines the sign of productivity trends in US forests. Proceedings of the National Academy of Sciences. 121(4). DOI: 10.1073/pnas.2311132121 Yu, Y., Saatchi, S., Domke, G. M., Walters, B., Woodall, C., Ganguly, S., Li, S., Kalia, S., Park, T., Nemani, R., Hagen, S. C., Melendy, L. 2022. Making the US national forest inventory spatially contiguous and temporally consistent. Environmental Research Letters. 17(6), 065002. DOI: 10.1088/1748-9326/ac6b47 | ||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Yu, Y., S.S. Saatchi, B.F. Walters, S. Ganguly, S. Li, S. Hagen, L. Melendy, R.R. Nemani, G.M. Domke, and C.W. Woodall. 2021. Carbon Pools across CONUS using the MaxEnt Model, 2005, 2010, 2015, 2016, and 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1752
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| Sayers (CMS 2016) (2017) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | New Carbon Monitoring Products for Global Freshwater Lakes using Satellite Remote Sensing Time Series Data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Michael (Mike) Sayers, Michigan Tech Research Institute
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Shuchman (CMS 2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
A three year $660K program is proposed to develop and evaluate ocean color satellite- based primary production models for estimating carbon fixation of freshwater lakes on a global scale and to provide carbon fixation estimates for the world’s freshwater lakes. Generation of this first of its kind data set is an important initial step to determining complete freshwater lake carbon budgets for the world’s lakes.
These new remote sensing based tools and data products generated at local and regional scales would support the Monitoring, Reporting and Verification (MRV) aspect of NASA’s CMS program objectives and specifically addresses the two CMS research topics “using remote sensing data products to produce and evaluate prototype MRV system approaches and/or calibration and validation data sets for future NASA missions;” and “Studies that build upon, extend, and/or improve the existing CMS products for biomass and flux resulting from NASA’s first phases of CMS pilot studies”. This project builds upon and extends a methodology for estimating primary production established in an initial pilot study under a NASA CMS Phase 2 (2012 Solicitation) program to “Develop new regional carbon monitoring products in the Great Lakes” (Shuchman et al. 2013; Fahnenstiel et al. 2016; Grant #NN12AP94G).
Specifically, a simplified Depth Integrated Model (DIM) for estimating primary production or carbon fixation (thereafter referred to as carbon fixation) would generate a 2011 snapshot of the total carbon fixation in all freshwater lakes on a 300m grid, while a more sophisticated regionally optimized Vertically Generalized Production Model (VGPM) developed under this program would be used to generate annual carbon fixation estimates for 1000 freshwater lakes of the world from 2002-2011. Finally, monthly estimates of carbon fixation would be generated for 10 of the world’s largest lakes from 2002-2016. In addition to supporting CMS, the time series of carbon fixation products to be generated under this new program can be used to provide a better understanding of how anthropogenic forcing, invasive species, and climate change affect carbon fixation of the freshwater lakes in the various ecological regions throughout the globe. The carbon fixation estimates to be generated under this program are also useful in calculating freshwater fish production and better understanding lake ecosystems throughout the world. The Globolakes research programme (http://www.globolakes.ac.uk/) is an integral part of this research program and our goals fit closely with their research mission. Globolakes is providing a robust database of satellite and in situ observations that will be directly used in the generation of freshwater carbon fixation products. The simplified DIM and VGPM developed under this proposed program can utilize a variety of multispectral satellite aircraft and UAS data sources. The ocean color satellite data that will be used in this proposed program includes: MERIS and VIIRS. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
David Bunnell, USGS, Great Lakes Science Center | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Bosse, K. R., Sayers, M. J., Shuchman, R. A., Fahnenstiel, G. L., Ruberg, S. A., Fanslow, D. L., Stuart, D. G., Johengen, T. H., Burtner, A. M. 2019. Spatial-temporal variability of in situ cyanobacteria vertical structure in Western Lake Erie: Implications for remote sensing observations. Journal of Great Lakes Research. 45(3), 480-489. DOI: 10.1016/j.jglr.2019.02.003 Sayers, M. J., Bosse, K. R., Shuchman, R. A., Ruberg, S. A., Fahnenstiel, G. L., Leshkevich, G. A., Stuart, D. G., Johengen, T. H., Burtner, A. M., Palladino, D. 2019. Spatial and temporal variability of inherent and apparent optical properties in western Lake Erie: Implications for water quality remote sensing. Journal of Great Lakes Research. 45(3), 490-507. DOI: 10.1016/j.jglr.2019.03.011 Sayers, M. J., Fahnenstiel, G. L., Shuchman, R. A., Bosse, K. R. 2021. A new method to estimate global freshwater phytoplankton carbon fixation using satellite remote sensing: initial results. International Journal of Remote Sensing. 42(10), 3708-3730. DOI: 10.1080/01431161.2021.1880661 Sayers, M. J., Grimm, A. G., Shuchman, R. A., Bosse, K. R., Fahnenstiel, G. L., Ruberg, S. A., Leshkevich, G. A. 2019. Satellite monitoring of harmful algal blooms in the Western Basin of Lake Erie: A 20-year time-series. Journal of Great Lakes Research. 45(3), 508-521. DOI: 10.1016/j.jglr.2019.01.005 Sayers, M., Bosse, K., Fahnenstiel, G., Shuchman, R. 2020. Carbon Fixation Trends in Eleven of the World's Largest Lakes: 2003-2018. Water. 12(12), 3500. DOI: 10.3390/w12123500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Sedano (CMS 2016) (2017) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Forest degradation driven by charcoal production: characterization, quantification and forecasting to improve carbon monitoring systems in southern Africa | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Fernando Sedano, University of Maryland
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
African urban population is rapidly growing. While only 30% of the African population lived in urban centers in 2000, this figure will reach 60% by year 2050. Close to eighty percent of African urban households use charcoal as main source of cooking fuel. Charcoal is expected to remain the main source of energy in the coming future and its overall consumption will rise by 2040. Charcoal production is already the main driver of forest degradation in sub Saharan Africa.
The Miombo region of southern Africa includes the largest tropical woodlands ecosystems in Africa. These ecosystems are a source of large uncertainties in the global carbon balance. The urban demand for energy poses an increasing pressure on these woodlands. Yet, forest degradation driven by charcoal production is still insufficiently
understood and poorly quantified. This knowledge gap and the growing importance of this process stresses the need of developing specific monitoring and quantifying strategies as a first step to reduce carbon emissions uncertainties in the region.
The overarching goal of this research proposal is developing remote sensing-based and modeling tools to characterize, quantify, understand and predict forest degradation in tropical woodlands of the Miombo region of southern Africa.
In a first objective, we will prototype a remote sensing-based approach to map, monitor and quantify forest degradation from charcoal production combining multitemporal analysis of very high-resolution remote sensing images and field measurements. A second objective will develop a modeling framework to generate spatially explicit estimates of current forest degradation area and carbon emissions at national level and predict the evolution of carbon stocks under future scenarios. Finally, we will evaluate the potential and limitations of upcoming NASA GEDI mission to detect changes in forest structure associated to forest degradation in tropical woodlands of southern Africa. Ultimately, the methods and products developed under this project will provide the knowledge base at relevant spatial and temporal scales for understanding a poorly understood forest degradation process of high significance at regional level. This proposal will contribute to advance remote sensing-based approaches to characterize, monitor and quantify forest degradation in tropical woodlands. This knowledge will support the development of more precise MRV REDD+ systems in the countries of the Miombo region. The findings of the project could potentially be incorporated in the national REDD+ strategies in Africa, becoming a key tool for targeted policy interventions within the context of REDD+. The methods developed in this project will be valuable for US and international institutions involved in the independent monitoring of emission inventories in support of international climate agreements. The proposed effort will also contribute to predict the evolution of carbon sources and sinks in a large ecosystem of global importance and source of large uncertainties in the carbon balance. Lastly, this research proposal will also produce information for the calibration and validation of future GEDI data for degradation studies and inform future space-borne LiDAR missions. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
John David, NASA GSFC | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Sedano, F., Lisboa, S. N., Duncanson, L., Ribeiro, N., Sitoe, A., Sahajpal, R., Hurtt, G., Tucker, C. J. 2020. Monitoring forest degradation from charcoal production with historical Landsat imagery. A case study in southern Mozambique. Environmental Research Letters. 15(1), 015001. DOI: 10.1088/1748-9326/ab3186 Sedano, F., Lisboa, S. N., Sahajpal, R., Duncanson, L., Ribeiro, N., Sitoe, A., Hurtt, G., Tucker, C. J. 2021. The connection between forest degradation and urban energy demand in sub-Saharan Africa: a characterization based on high-resolution remote sensing data. Environmental Research Letters. 16(6), 064020. DOI: 10.1088/1748-9326/abfc05 Sedano, F., Lisboa, S., Duncanson, L., Ribeiro, N., Sitoe, A., Sahajpal, R., Hurtt, G., Tucker, C. 2020. Monitoring intra and inter annual dynamics of forest degradation from charcoal production in Southern Africa with Sentinel - 2 imagery. International Journal of Applied Earth Observation and Geoinformation. 92, 102184. DOI: 10.1016/j.jag.2020.102184 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Vargas (CMS 2016) (2017) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | Carbon monitoring systems across Mexico to support implementation of REDD+: maximizing benefits and knowledge | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Science Team |
Rodrigo Vargas, University of Delaware
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2016) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Vargas (CMS 2013) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Successor Projects: | Vargas (CMS 2020) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Rationale: Mexico is a high-biodiversity country with nearly 40% of its territory forested. During the last decade carbon cycle science efforts have rapidly increased, and state-of- the-art measurements on carbon (C) stocks, dynamics, and forest architecture are available at representative landscapes and at the national level. Mexico is now recognized to be one of the few non-Annex I countries capable of implementing Reducing Emissions from Deforestation and Forest Degradation plus improving forest management, carbon stock enhancement and conservation (REDD+). This proposal builds on previous NASA CMS efforts to improve monitoring, reporting and verification (MRV) for implementation of REDD+ in Mexico. Furthermore, this proposal takes advantage of other NASA CMS efforts to develop algorithms and apply high performance computing
(HPC) approaches to develop a framework for estimating high-resolution (30 m resolution) carbon-related estimates at national scales. Combining CMS efforts and experiences are important to (a) increase interoperability across CMS products, (b) test their applicability and uncertainty, (c) identify their strengths and areas for improvements, and (d) move to higher Application Readiness Levels (ARLs). Mexico can be considered a “data rich” country, and this proposal is an opportunity to develop, test, and improve the applicability of different NASA CMS products across North America.
The goal of this proposal is to: improve a national carbon monitoring framework to synthetize forest inventory and remote sensing information, while increasing spatial resolution and knowledge to provide support for implementation of REDD+ across Mexico.
Specific objectives: 1) Harmonize available data to increase interoperability and synthesis efforts; 2) Build multi-scale resolution products at the national level (1km to 30 m); 3) Develop high-resolution estimates (15 and 1 m) at intensive monitoring sites; and 4) Collaborate with stakeholders to improve a national carbon monitoring framework where information is available to support research and management/policy decisions.
Approach: This proposal builds upon ongoing efforts supported by NASA, the USDA Forest Service (supported by USAID), the Mexican Carbon Program, and multiple institutions represented by participants in this proposal. This proposal will a) harmonize and synthetize available national information to increase data interoperability for synthesis studies, and development/validation of CMS products; b) build multi-scale resolution products (between 1 km to 30 m) of forest cover change, aboveground biomass, forest structural variables (e.g., tree height), soil carbon, and gross primary productivity (GPP) with associate uncertainties at the national level; and c) generate a framework for high-resolution (15 m to 1 m) estimates of aboveground biomass, forest structural variables, soil carbon, and GPP across a network of intensive monitoring sites. These efforts will be supported by already available data sets (site level and national level), NASA-derived remote sensing information, and using the NASA Earth Exchange (NEX) HPC framework.
Significance: This proposal supports NASA research through a) validation and improvement of CMS-related applications; b) advancement of remote sensing-based approaches to MRV; c) supporting implementation of REDD+ projects; d) building synergy and collaboration between different NASA CMS efforts; and f) working with scientists and stakeholders to increase ARLs and transfer CMS efforts and products across North America. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Gregorio Ángeles-Pérez, Colegio de Postgraduados | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
2020. State of the Climate in 2019. Bulletin of the American Meteorological Society. 101(8), S1-S429. DOI: 10.1175/2020BAMSStateoftheClimate.1 Barba, J., Cueva, A., Bahn, M., Barron-Gafford, G. A., Bond-Lamberty, B., Hanson, P. J., Jaimes, A., Kulmala, L., Pumpanen, J., Scott, R. L., Wohlfahrt, G., Vargas, R. 2018. Comparing ecosystem and soil respiration: Review and key challenges of tower-based and soil measurements. Agricultural and Forest Meteorology. 249, 434-443. DOI: 10.1016/j.agrformet.2017.10.028 Basu, S., Mukhopadhyay, S., Karki, M., DiBiano, R., Ganguly, S., Nemani, R., Gayaka, S. 2018. Deep neural networks for texture classification--A theoretical analysis. Neural Networks. 97, 173-182. DOI: 10.1016/j.neunet.2017.10.001 Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M., Vargas, R. 2018. Globally rising soil heterotrophic respiration over recent decades. Nature. 560(7716), 80-83. DOI: 10.1038/s41586-018-0358-x Cueva, A., Bullock, S. H., Mendez-Alonzo, R., Lopez-Reyes, E., Vargas, R. 2021. Foliage Senescence as a Key Parameter for Modeling Gross Primary Productivity in a Mediterranean Shrubland. Journal of Geophysical Research: Biogeosciences. 126(1). DOI: 10.1029/2020JG005839 Delgado-Balbuena J, Yépez EA, Paz-Pellat F, Ángeles-Pérez G, Aguirre-Gutiérrez C, Alvarado-Barrientos MS, Arredondo T, Ayala-Niño F, Bullock S, Castellanos AE, Cueva A, Figueroa-Espinoza B, Garatuza- Payán J, González-del Castillo E, González-Sosa E, Guevara-Escobar A, Hinojo-Hinojo C, Kyaw-Tha PU, Lizárraga-Celaya C, Maya-Delgado Y, Oechel W, Pérez-Ruiz ER, Quesada-Avendaño M, Robles-Zazueta CA, Rodríguez JC, Rojas-Robles NE, Tarin-Terrazas T, Troyo-Diéguez E, Uuh-Sonda J, Vargas-Terminel ML, Vargas R, Vega-Puga MG, Verduzco VS, Vivoni ER, Watts CJ (2019) Database of vertical carbon dioxide fluxes at terrestrial and coastal ecosystems in Mexico. Elementos para Politicas Publicas. 2(2)93-108. http://www.elementospolipub.org/ojs/index.php/epp/article/view/41/49 Delgado-Balbuena, J., Arredondo, J. T., Loescher, H. W., Pineda-Martinez, L. F., Carbajal, J. N., Vargas, R. 2019. Seasonal Precipitation Legacy Effects Determine the Carbon Balance of a Semiarid Grassland. Journal of Geophysical Research: Biogeosciences. 124(4), 987-1000. DOI: 10.1029/2018JG004799 Ganguly S, Basu S, Nemani R, Mukhopadhyay S, Michaelis A, Votava P, Milesi C, Kumar U (2018) Deep Learning for Very High-Resolution Imagery Classification, Large-Scale Machine Learning in the Earth Sciences, Chapter 7, in Large-Scale Machine Learning in the Earth Sciences. Srivastava, A.N., Nemani, R. and Steinhaeuser, K. eds., CRC Press. ISBN: 9781498703888. Guevara, M., Arroyo, C., Brunsell, N., Cruz, C. O., Domke, G., Equihua, J., Etchevers, J., Hayes, D., Hengl, T., Ibelles, A., Johnson, K., Jong, B., Libohova, Z., Llamas, R., Nave, L., Ornelas, J. L., Paz, F., Ressl, R., Schwartz, A., Victoria, A., Wills, S., Vargas, R. 2020. Soil Organic Carbon Across Mexico and the Conterminous United States (1991-2010). Global Biogeochemical Cycles. 34(3). DOI: 10.1029/2019GB006219 Guevara, M., Olmedo, G. F., Stell, E., Yigini, Y., Aguilar Duarte, Y., Arellano Hernandez, C., Arevalo, G. E., Arroyo-Cruz, C. E., Bolivar, A., Bunning, S., Bustamante Canas, N., Cruz-Gaistardo, C. O., Davila, F., Dell Acqua, M., Encina, A., Figueredo Tacona, H., Fontes, F., Hernandez Herrera, J. A., Ibelles Navarro, A. R., Loayza, V., Manueles, A. M., Mendoza Jara, F., Olivera, C., Osorio Hermosilla, R., Pereira, G., Prieto, P., Ramos, I. A., Rey Brina, J. C., Rivera, R., Rodriguez-Rodriguez, J., Roopnarine, R., Rosales Ibarra, A., Rosales Riveiro, K. A., Schulz, G. A., Spence, A., Vasques, G. M., Vargas, R. R., Vargas, R. 2018. No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America. SOIL. 4(3), 173-193. DOI: 10.5194/soil-4-173-2018 Guevara, M., Vargas, R. 2021. Prediccion de carbono organico en los suelos de Mexico a 1 m de profundidad y 90 m de resolucion espacial (1999-2009). REVISTA TERRA LATINOAMERICANA. 39. DOI: 10.28940/terra.v39i0.1241 Harden, J. W., Hugelius, G., Ahlstrom, A., Blankinship, J. C., Bond-Lamberty, B., Lawrence, C. R., Loisel, J., Malhotra, A., Jackson, R. B., Ogle, S., Phillips, C., Ryals, R., Todd-Brown, K., Vargas, R., Vergara, S. E., Cotrufo, M. F., Keiluweit, M., Heckman, K. A., Crow, S. E., Silver, W. L., DeLonge, M., Nave, L. E. 2017. Networking our science to characterize the state, vulnerabilities, and management opportunities of soil organic matter. Global Change Biology. 24(2). DOI: 10.1111/gcb.13896 Hashimoto, H., Wang, W., Melton, F. S., Moreno, A. L., Ganguly, S., Michaelis, A. R., Nemani, R. R. 2019. High-resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States. International Journal of Climatology. 39(6), 2964-2983. DOI: 10.1002/joc.5995 Hayes, D. J., Vargas, R., Alin, S., Conant, R. T., Hutyra, L. R., Jacobson, A. R., Kurz, W. A., Liu, S., McGuire, A. D., Poulter, B., Woodall, C. W. 2018. Chapter 2: The North American Carbon Budget. Second State of the Carbon Cycle Report DOI: 10.7930/SOCCR2.2018.Ch2 Hinojo-Hinojo, C., Castellanos, A. E., Huxman, T., Rodriguez, J. C., Vargas, R., Romo-Leon, J. R., Biederman, J. A. 2019. Native shrubland and managed buffelgrass savanna in drylands: Implications for ecosystem carbon and water fluxes. Agricultural and Forest Meteorology. 268, 269-278. DOI: 10.1016/j.agrformet.2019.01.030 Hinojo-Hinojo, C., Castellanos, A. E., Llano-Sotelo, J., Penuelas, J., Vargas, R., Romo-Leon, J. R. 2018. High Vcmax, Jmax and photosynthetic rates of Sonoran Desert species: Using nitrogen and specific leaf area traits as predictors in biochemical models. Journal of Arid Environments. 156, 1-8. DOI: 10.1016/j.jaridenv.2018.04.006 Jian, J., Vargas, R., Anderson-Teixeira, K., Stell, E., Herrmann, V., Horn, M., Kholod, N., Manzon, J., Marchesi, R., Paredes, D., Bond-Lamberty, B. 2021. A restructured and updated global soil respiration database (SRDB-V5). Earth System Science Data. 13(2), 255-267. DOI: 10.5194/essd-13-255-2021 Kim, M., Ham, B., Kraxner, F., Shvidenko, A., Schepaschenko, D., Krasovskii, A., Park, T., Lee, W. 2020. Species- and elevation-dependent productivity changes in East Asian temperate forests. Environmental Research Letters. 15(3), 034012. DOI: 10.1088/1748-9326/ab71a2 Kumar, U., Ganguly, S., Nemani, R. R., Raja, K. S., Milesi, C., Sinha, R., Michaelis, A., Votava, P., Hashimoto, H., Li, S., Wang, W., Kalia, S., Gayaka, S. 2017. Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing. Remote Sensing. 9(11), 1105. DOI: 10.3390/rs9111105 Liu, Q., Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., Nemani, R. 2019. DeepSat V2: feature augmented convolutional neural nets for satellite image classification. Remote Sensing Letters. 11(2), 156-165. DOI: 10.1080/2150704X.2019.1693071 Peano, D., Hemming, D., Materia, S., Delire, C., Fan, Y., Joetzjer, E., Lee, H., Nabel, J. E. M. S., Park, T., Peylin, P., Warlind, D., Wiltshire, A., Zaehle, S. Plant phenology evaluation of CRESCENDO land surface models - Part I: start and end of growing season DOI: 10.5194/bg-2020-319 Piao, S., Wang, X., Park, T., Chen, C., Lian, X., He, Y., Bjerke, J. W., Chen, A., Ciais, P., Tommervik, H., Nemani, R. R., Myneni, R. B. 2019. Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth & Environment. 1(1), 14-27. DOI: 10.1038/s43017-019-0001-x Rojas-Robles, N. E., Garatuza-Payan, J., Alvarez-Yepiz, J. C., Sanchez-Mejia, Z. M., Vargas, R., Yepez, E. A. 2020. Environmental Controls on Carbon and Water Fluxes in an Old-Growth Tropical Dry Forest. Journal of Geophysical Research: Biogeosciences. 125(8). DOI: 10.1029/2020JG005666 Saatchi, S., Longo, M., Xu, L., Yang, Y., Abe, H., Andre, M., Aukema, J. E., Carvalhais, N., Cadillo-Quiroz, H., Cerbu, G. A., Chernela, J. M., Covey, K., Sanchez-Clavijo, L. M., Cubillos, I. V., Davies, S. J., De Sy, V., De Vleeschouwer, F., Duque, A., Sybille Durieux, A. M., De Avila Fernandes, K., Fernandez, L. E., Gammino, V., Garrity, D. P., Gibbs, D. A., Gibbon, L., Gowae, G. Y., Hansen, M., Lee Harris, N., Healey, S. P., Hilton, R. G., Johnson, C. M., Kankeu, R. S., Laporte-Goetz, N. T., Lee, H., Lovejoy, T., Lowman, M., Lumbuenamo, R., Malhi, Y., Albert Martinez, J. M., Nobre, C., Pellegrini, A., Radachowsky, J., Roman, F., Russell, D., Sheil, D., Smith, T. B., Spencer, R. G., Stolle, F., Tata, H. L., Torres, D. D. C., Tshimanga, R. M., Vargas, R., Venter, M., West, J., Widayati, A., Wilson, S. N., Brumby, S., Elmore, A. C. 2021. Detecting vulnerability of humid tropical forests to multiple stressors. One Earth. 4(7), 988-1003. DOI: 10.1016/j.oneear.2021.06.002 Soriano-Luna, M., Angeles-Perez, G., Guevara, M., Birdsey, R., Pan, Y., Vaquera-Huerta, H., Valdez-Lazalde, J., Johnson, K., Vargas, R. 2018. Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production. Forests. 9(8), 490. DOI: 10.3390/f9080490 Stell, E., Warner, D., Jian, J., Bond-Lamberty, B., Vargas, R. 2021. Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions? Global Change Biology. 27(16), 3923-3938. DOI: 10.1111/gcb.15666 Vandal, T., Kodra, E., Dy, J., Ganguly, S., Nemani, R., Ganguly, A. R. 2018. Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. DOI: 10.1145/3219819.3219996 Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., Ganguly, A. R. 2017. DeepSD. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. DOI: 10.1145/3097983.3098004 Vazquez-Lule A*, Bejarano M, Olguin M, Villeda E, Vargas R (2018). Integración y síntesis de datos para el monitoreo de los manglares de México. In: Métodos para la caracterización de los manglares mexicanos: un enfoque espacial multi-escala. Edited by CONABIO. pp 245-265. ISBN: 978-607-8570-03-4. http://www.biodiversidad.gob.mx/ecosistemas/manglares2013/pdf/metodos/caracterizacion_manglares.pdf Vazquez-Lule, A., Colditz, R., Herrera-Silveira, J., Guevara, M., Rodriguez-Zuniga, M. T., Cruz, I., Ressl, R., Vargas, R. 2019. Greenness trends and carbon stocks of mangroves across Mexico. Environmental Research Letters. 14(7), 075010. DOI: 10.1088/1748-9326/ab246e Villarreal, S., Guevara, M., Alcaraz-Segura, D., Brunsell, N. A., Hayes, D., Loescher, H. W., Vargas, R. 2018. Ecosystem functional diversity and the representativeness of environmental networks across the conterminous United States. Agricultural and Forest Meteorology. 262, 423-433. DOI: 10.1016/j.agrformet.2018.07.016 Villarreal, S., Guevara, M., Alcaraz-Segura, D., Vargas, R. 2019. Optimizing an Environmental Observatory Network Design Using Publicly Available Data. Journal of Geophysical Research: Biogeosciences. 124(7), 1812-1826. DOI: 10.1029/2018JG004714 Villarreal, S., Vargas, R. 2021. Representativeness of FLUXNET Sites Across Latin America. Journal of Geophysical Research: Biogeosciences. 126(3). DOI: 10.1029/2020JG006090 Warner, D. L., Bond-Lamberty, B., Jian, J., Stell, E., Vargas, R. 2019. Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale. Global Biogeochemical Cycles. 33(12), 1733-1745. DOI: 10.1029/2019GB006264 Wheeler, K. I., Levia, D. F., Vargas, R. 2019. Visible and near-infrared hyperspectral indices explain more variation in lower-crown leaf nitrogen concentrations in autumn than in summer. Oecologia. 192(1), 13-27. DOI: 10.1007/s00442-019-04554-2 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
Guevara, M., C.E. Arroyo-cruz, N. Brunsell, C.O. Cruz-gaistardo, G.M. Domke, J. Equihua, J. Etchevers, D.J. Hayes, T. Hengl, A. Ibelles, K. Johnson, B. de Jong, Z. Libohova, R. Llamas, L. Nave, J.L. Ornelas, F. Paz, R. Ressl, A. Schwartz, S. Wills, and R. Vargas. 2020. Soil Organic Carbon Estimates for 30-cm Depth, Mexico and Conterminous USA, 1991-2011. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1737
Villarreal, S., R. Vargas, and D. Alcaraz-segura. 2019. Ecosystem Functional Type Distribution Map for the Conterminous USA, 2001-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1659 Guevara, M., G.F. Olmedo, E. Stell, Y. Yigini, C.A. Hernandez, G. Arevalo, C.E. Arroyo-cruz, A. Bolivar, S. Bunning, N.B. Canas, C.O. Cruz-gaistardo, F. Davila, M.D. Acqua, A. Encina, F. Fontes, J.A.H. Herrera, A.R.I. Navarro, V. Loayza, A.M. Manueles, F.M. Jara, C. Olivera, G. Pereira, P. Prieto, I.A. Ramos, J.C.R. Brina, R. Rivera, J. Rodriguez-Rodriguez, R. Roopnarine, A. Rosales, K.A.R. Rivero, G.A. Schulz, A. Spence, G.M. Vasques, R.R. Vargas, and R. Vargas. 2019. Soil Organic Carbon Stock Estimates with Uncertainty across Latin America. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1615 Villarreal, S., D. Alcaraz-Segura, M. Guevara, and R. Vargas. 2019. Ecosystem Functional Type Distribution Map for Mexico, 2001-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1693 Warner, D.L., B.P. Bond-Lamberty, J. Jian, E. Stell, and R. Vargas. 2019. Global Gridded 1-km Annual Soil Respiration and Uncertainty Derived from SRDB V3. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1736 Guevara, M., and R. Vargas. 2020. Soil Organic Carbon Estimates and Uncertainty at 1-m Depth across Mexico, 1999-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1754 Hashimoto H, Wang W, Melton F, Moreno A, Michaelis A, and Nemani R. (2019). NEX-Gridded Daily Meteorology (NEX-GDM) land surface climate data. https://data.nas.nasa.gov/geonex/data.php?dir=/geonexdata/NEX-GDM Vázquez-Lule, A., R. Colditz, J. Herrera-silveira, M. Guevara, M.G. RodrÃguez-Zúñiga, I. Cruz, R. Ressl, and R. Vargas. 2021. Greenness Trends and Carbon Stocks of Mangrove Forests Across Mexico, 2001-2015. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1853 Park, T., and R. Vargas. 2022. Tree Cover Estimates at 30 m Resolution for Mexico, 2016-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2137 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Woodcock (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project Title: | A pantropical monitoring system of carbon emissions and removals from forest degradation, deforestation, and forest expansion and growth | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Science Team |
Curtis Woodcock, Boston University
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Precursor Projects: | Olofsson (CMS 2015) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
Carbon emissions associated with the conversion of forestlands to other land uses in the tropics account for 7-14% of global emissions to the atmosphere. Additional carbon is emitted through forest degradation, which new evidence suggests is more widespread than previously thought. As both tropical and global emissions of terrestrial carbon continue to increase, the need to reduce tropical deforestation and forest degradation is urgent. But despite international frameworks and large investments devoted to reducing terrestrial carbon emissions in the tropics, the uncertainties in rates of tropical deforestation and forest degradation and associated emissions are large – often large enough to prevent determining if reductions have been achieved. Forest degradation in particular is often excluded or defined by imprecise proxies in emissions reduction programs. The result is a limited ability to establish and evaluate policy aimed at decreasing deforestation and forest degradation and enhancing terrestrial carbon stocks., Current methods are inadequate for monitoring of forest degradation, inadequate in their characterization of post-disturbance landscapes, provide inadequate information on the carbon content of disturbed and degraded forestlands, and are inadequate in their treatment of errors and bias in maps derived from remote sensing data.
Today, we are witnessing an exceptional increase in the kinds, quality and quantity of satellite data suitable for studying Earth’s surface. Powerful cloud-based platforms provide direct access to the data and unprecedented computing power. Algorithms, sampling and estimation techniques, and monitoring approaches have been developed that allow us to study Earth’s surface in new and exciting ways. If properly combined and utilized, these advancements enable a more complete and precise analysis of terrestrial carbon dynamics.
Here, we propose a comprehensive monitoring system of carbon emissions and removals from forest dynamics across the tropics. The proposed system builds on the prototype monitoring system developed in a 2016-2019 NASA CMS project (PI Olofsson) and the tropical forest degradation monitoring approach developed in a 2016-2019 NASA Earth Science Fellowship (PI Woodcock), and leverages the output of a 2018-2023 NASA MEaSUREs project (PI Friedl) and a 2015-2018 CMS project (PI Baccini). In essence, we propose to scale up the monitoring of forest disturbance and degradation, as well as land cover dynamics following deforestation and forest clearing to the pantropics. Maps of forest recovery and expansion will be leveraged from a NASA MEaSUREs project (PI Friedl), which also provides an infrastructure for global mapping using the algorithms developed in the mentioned NASA projects on Google Earth Engine at Landsat resolutions. Population-scale estimates of area bias and uncertainty, and pixel-level likelihood of errors will be combined with maps of forest dynamics in a spatially and temporally explicit carbon bookkeeping model. The project will be carried out in close collaboration with stakeholders through SilvaCarbon, UN-FAO, the World Bank Forest Carbon Partner Facility and the GOFC-GOLD Regional Networks to ensure that the project deliverables assist efforts to reduce deforestation, forest disturbance and carbon emissions in tropical countries. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Alessandro (Ale) Baccini, Boston University | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Publications: |
Chen, S., Woodcock, C. E., Saphangthong, T., Olofsson, P. 2023. Satellite data reveals a recent increase in shifting cultivation and associated carbon emissions in Laos. Environmental Research Letters. 18(11), 114012. DOI: 10.1088/1748-9326/acffdd | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Archived Data Citations: |
N/A at this point
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| Worden (CMS 2018) (2019) | ||||||||||||||||||||||||||||||||||||||
| Project Title: | Quantifying and Partitioning the Global Methane Budget Using Satellite and Ground Based Measurements Of CH4 and Tracers of Its Sources and Sinks | |||||||||||||||||||||||||||||||||||||
|
Science Team |
John Worden, JPL
(Project Lead)
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| Solicitation: | NASA: Carbon Monitoring System (2018) | |||||||||||||||||||||||||||||||||||||
| Successor Projects: | Worden (CMS 2022) | |||||||||||||||||||||||||||||||||||||
| Abstract: |
We will deliver the annual global methane budget and its uncertainties from January 2018 through December 2021. As demonstrated in this proposal, the budget is partitioned by source type (e.g. fossil fuel, wetlands, fires, agriculture) and includes the global sink from oxidation by the OH radical. The budget will include well-characterized uncertainties that are traceable back to the uncertainties in the data and model. Where possible we will generate emissions estimates for individual countries in order to evaluate national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC).
We utilize the tools, data, and expertise built from previous ROSES Carbon Monitoring System (CMS), Interdisciplinary Science (IDS), Carbon Cycle and Ecosystem (CCE), and Aura mission grants to accomplish this goal. State-of-the science bottom-up inventories (e.g., wetlands from inundation / rainfall, fossil fuels from country reported totals) are used as prior knowledge. Top down flux estimates are provided by integrating satellite based measurements of total column methane with two state-of-the science models (GEOS-Chem and LMDz). After fluxes of methane are computed, a Bayesian attribution approach integrates the quantified methane fluxes with fire emission estimates of methane based on MODIS burned area and MOPITT CO data, and surface measurements of methane and its isotopic composition, to better quantify methane emissions by source and geographical region, including uncertainties. The initial system uses total column methane data from the Greenhouse Gases Observing Satellite (GOSAT). However, we expect to greatly reduce uncertainties for annual fluxes, relative to the GOSAT based emissions, by using new total column methane data from the new Tropospheric Monitoring Instrument (TROPOMI), with its greater than 1000 times sampling relative to GOSAT in many geographical areas. Specific focus will be placed on quantifying emissions from oil/gas production regions for the benefit of our stakeholders. We will also implement a high-resolution emissions estimate for these regions using TROPOMI to target stakeholder needs.
In order to ensure that reported uncertainties for these products are robust and testable, our proposal will advance the state-of-the art for quantifying uncertainties of global methane emissions through a combination of analytical and empirical approaches. As the GEOS-Chem model uses an analytical solution to the inverse problem, it provides closedform characterization of error and information content by calculating the posterior error covariance, averaging kernel, and gain matrices. We can test these errors by projecting differences between independent methane measurements and the posterior GEOS-Chem methane concentrations to a flux error. We will use methane concentration measurements from the Atmospheric Infrared Sounder (AIRS), the NASA EV-S ATOM campaigns, NOAA and DOE aircraft for this purpose. We will also compare calculated uncertainties in the fluxes to variations in methane fluxes derived from an ensemble of LMDz model based inversions to further test the calculated uncertainties.
Our NGO stakeholders include the Environmental Defense Fund and Carnegie Institute for Peace who will use our yearly estimates of emissions from oil/gas production at the regional scale to evaluate how they could be remediated. We are also already working with the Global Carbon Project (GCP) through our on-going ROSES IDS grant and will continue to provide them with the latest methane budgets as they are quantified; these budgets are reported periodically to the IPCC in its evaluation of the state of the climate. | |||||||||||||||||||||||||||||||||||||
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Participants: |
Alexis (Anthony) Bloom, Jet Propulsion Lab, California Institute of Technology | |||||||||||||||||||||||||||||||||||||
| Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||
| Data Products: |
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| Publications: |
Cusworth, D. H., Bloom, A. A., Ma, S., Miller, C. E., Bowman, K., Yin, Y., Maasakkers, J. D., Zhang, Y., Scarpelli, T. R., Qu, Z., Jacob, D. J., Worden, J. R. 2021. A Bayesian framework for deriving sector-based methane emissions from top-down fluxes. Communications Earth & Environment. 2(1). DOI: 10.1038/s43247-021-00312-6 Ma, S., Worden, J. R., Bloom, A. A., Zhang, Y., Poulter, B., Cusworth, D. H., Yin, Y., Pandey, S., Maasakkers, J. D., Lu, X., Shen, L., Sheng, J., Frankenberg, C., Miller, C. E., Jacob, D. J. 2021. Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions. AGU Advances. 2(3). DOI: 10.1029/2021AV000408 Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J., Zhang, Y., Lu, X., Bloom, A. A., Bowman, K. W., Worden, J. R., Parker, R. J. 2021. 2010-2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane. Atmospheric Chemistry and Physics. 21(6), 4339-4356. DOI: 10.5194/acp-21-4339-2021 Worden, J. R., Cusworth, D. H., Qu, Z., Yin, Y., Zhang, Y., Bloom, A. A., Ma, S., Byrne, B. K., Scarpelli, T., Maasakkers, J. D., Crisp, D., Duren, R., Jacob, D. J. 2022. The 2019 methane budget and uncertainties at 1deg resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates. Atmospheric Chemistry and Physics. 22(10), 6811-6841. DOI: 10.5194/acp-22-6811-2022 Worden, J. R., Pandey, S., Zhang, Y., Cusworth, D. H., Qu, Z., Bloom, A. A., Ma, S., Maasakkers, J. D., Byrne, B., Duren, R., Crisp, D., Gordon, D., Jacob, D. J. 2023. Verifying Methane Inventories and Trends With Atmospheric Methane Data. AGU Advances. 4(4). DOI: 10.1029/2023av000871 Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S., Worden, J., Parker, R. J., Boesch, H. Attribution of the accelerating increase in atmospheric methane during 2010-2018 by inverse analysis of GOSAT observations DOI: 10.5194/acp-2020-964 | |||||||||||||||||||||||||||||||||||||