Guan, Kaiyu: University of Illinois (Project Lead)
Guan, Kaiyu: University of Illinois (Project Lead)
Alden, Caroline: University of Colorado (Co-Investigator)
Berry, Joseph (Joe): Carnegie Institution for Science (Co-Investigator)
DeLucia, Evan: University of Illinois (Co-Investigator)
Frankenberg, Christian: Caltech (Co-Investigator)
Andrews, Arlyn: NOAA Earth System Research Laboratory (Collaborator)
Miller, John: NOAA Global Monitoring Laboratory (Collaborator)
Yang, Xi: University of Virginia (Collaborator)
Jiang, Chongya: University of Illinois (Post-Doc)
Peng, Bin: University of Illinois Urbana-Champaign (Post-Doc)
Rastogi, Bharat: University of Colorado Boulder (Post-Doc)
Lurkins, Lauren: Illinois Farm Bureau (Stakeholder)
McBay, Dion: Monsanto Company (Stakeholder)
Ma, Zewei: University of Illinois (Student-Graduate)
Project Funding:
2017 - 2020
NRA: 2016 NASA: Carbon Monitoring System
Funded by NASA
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?”
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
More details may be found in the following project profile(s):