Radiance-based NIRv as a proxy for GPP of corn and soybean - Genghong Wu; Kaiyu Guan; Chongya Jiang; Bin Peng; Hyungsuk Kimm; Min Chen; Xi Yang; Sheng Wang; Andrew E Suyker; Carl J Bernacchi; Caitlin E Moore; Yelu Zeng; et al. (Environmental Research Letters, 2020)
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 (NIR _v,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, NIR _v,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIR _v,Ref ), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF _760 ). The strong linear relationship between NIR _v,Rad and absorbed photosynthetically active radiation by green leaves (APAR _green ), and that between APAR _green and GPP, explain the good NIR _v,Rad -GPP relationship. The NIR _v,Rad -GPP relationship is robust and consistent across sites. The scalability and simplicity of NIR _v,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.