Wu, GH; Guan, KY; Jiang, CY; Peng, B; Kimm, H; Chen, M; Yang, X; Wang, S; Suyker, AE; Bernacchi, CJ; Moore, CE; Zeng, YL; Berry, JA; Cendrero-Mateo, MP (2020). Radiance-based NIRv as a proxy for GPP of corn and soybean. ENVIRONMENTAL RESEARCH LETTERS, 15(3), 34009.

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.