B007-04
Synthesis of using SIF and spectral indices in estimating GPP for corn and soybean

Monday, 7 December 2020: 05:42
Virtual
Genghong Wu, University of Illinois at Urbana Champaign, College of Agricultural, Consumers, and Environmental Sciences, Urbana, IL, United States, Kaiyu Guan, University of Illinois at Urbana Champaign, College of Agricultural, Consumer and Environmental Sciences, Urbana, IL, United States, Chongya Jiang, University of Illinois at Urbana-Champaign, College of Agricultural, Consumers, and Environmental Sciences, Urbana, IL, United States, Hyungsuk Kimm, University of Illinois at Urbana Champaign, College of Agricultural, Consumer and Environmental Sciences, Urbana, United States, Sheng Wang, University of Illinois at Urbana-Champaign, Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, United States, Xi Yang, University of Virginia, Environmental Sciences, Charlottesville, VA, United States, Carl Bernacchi, University of Illinois at Urbana-Champaign, Department of Plant Biology, Urbana, IL, United States, Caitlin Moore, University of Illinois at Urbana Champaign, Urbana, IL, United States, Andrew Suyker, University of Nebraska Lincoln, Lincoln, NE, United States and Joseph A Berry, Carnegie Institution for Science, Global Ecology, Stanford, CA, United States
Recorded Presentation
Abstract:
Accurate monitoring of crop gross primary production (GPP) is critical for designing effective management practices and policies and that can contribute to increasing crop yield. Large uncertainties exist in the current crop GPP estimation because of the assumptions and complexity in models, coarse resolution of climate data, and the indirect link between GPP and the “greenness” (e.g. NDVI). Recent advances in solar-induced chlorophyll fluorescence (SIF) may provide a better measure of crop GPP, since it directly measures plant photosynthetic activity (i.e. electron transport rate) and has been shown to be strongly correlated with GPP. New spectral indices such as near-infrared radiance of vegetation (NIRv,Rad) have also been proposed as an accurate proxy of crop GPP. In this study, we aim to improve crop GPP estimation through integrating SIF and spectral indices. By analyzing 12 site-year SIF and hyperspectral data from an established ground observation network in the U.S. Corn Belt, we have found: 1) The seasonal trajectory of SIF is dominated by canopy structure, and particularly there is a seasonal mismatch between SIF and GPP in multiple soybean fields. 2) NIRv,Rad, is an accurate proxy for GPP of corn and soybean because of its strong correlation with photosynthetically active radiation absorbed (APAR). 3) Another spectral index, canopy chlorophyll content index (CCCI), is highly correlated with canopy photosynthetic capacity Vcmax. 4) NIRv,Rad and CCCI well account for light-limited photosynthesis and light-saturated photosynthesis, respectively under normal conditions. 5) SIF is more sensitive to anomalies in crop photosynthetic activities than NIRv,Rad and CCCI. Based on these findings, we have developed a novel model synthesizing SIF, NIRv,Rad and CCCI. Overall, this model explains ~90% variations in daily GPP of both corn and soybean across all of our sites. The proposed model integrating these three distinct proxies indicates a potential to estimate crop GPP from high-resolution satellite remote sensing data.
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