Abstract 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 (SIF₇₆₀). 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.