Estimating crop net primary production using national inventory data and MODIS-derived parameters |
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| Affiliation: | 1. Joint Global Change Research Institute, Pacific Northwest National Laboratory, University of Maryland, College Park, MD 20740, USA;2. Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN 37831, USA;1. The World Bank, 1818 H Street NW, Washington, DC, United States;2. National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine;3. Joint Research Center of the European Commission, Ispra, Italy;4. Space Research Institute NASU-SSAU, Kyiv, Ukraine;1. Key Lab. of Computational Geodynamics, University of Chinese Academy of Sciences, Beijing 100049, China;2. Institute of Eco-Environment and Agro-meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China;3. Key Lab. of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;4. College of Geoscience, Yangtze University, Wuhan 430100, China;1. School of Ecology and Environment, Hainan University, Hainan 570228, China;2. Center for Eco-Environmental Restoration Engineering of Hainan Province, Hainan 570228, China;3. Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan 570228, China;1. Scripps Institution of Oceanography, University of California San Diego, CA, USA;2. SINTEF Ocean, Trondheim, Norway;3. University of British Columbia, Institute for the Oceans and Fisheries, British Columbia, Canada;4. Université du Québec à Rimouski, Québec, Canada;5. University of Washington, School of Oceanography, Seattle, WA, USA;6. Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, USA;7. University of Victoria, British Columbia, Canada;8. ARCTUS inc, Rimouski, QC, Canada |
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| Abstract: | 
National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917 g C m−2 yr−1 and 409 g C m−2 yr−1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents. |
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| Keywords: | Agriculture Carbon flux Crop production Geospatial scaling Phenology Satellite remote sensing |
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