Radiance-based NIR v as a proxy for GPP of corn and soybean
ENVIRONMENTAL RESEARCH LETTERS(2020)
摘要
Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIRv, Rad), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIRv, Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIRv, Ref), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF760). The strong linear relationship between NIRv, Rad and absorbed photosynthetically active radiation by green leaves (APAR(green)), and that between APARgreen and GPP, explain the good NIRv,Rad-GPP relationship. The NIRv,Rad-GPP relationship is robust and consistent across sites. The scalability and simplicity of NIRv, Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.
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关键词
photosynthesis,gross primary production,NIRv,near-infrared radiance of vegetation
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