NIRvP-EEO: An NIRvP-based eco-evolutionary optimality model for near-real-time daily crop gross primary productivity estimation at field scale

Near-real-time daily high-resolution estimates of crop gross primary productivity (GPP) are crucial for accurate biomass and yield estimation. The multiplicative combination of near-infrared reflectance of vegetation and photosynthetically active radiation (NIRvP) serves as a biophysically grounded proxy that enhances the responsiveness of GPP estimation. However, a mechanistic model for accurately estimating GPP using NIRvP remains lacking, which limits the potential for enhanced crop productivity assessments. In this study, we developed a model based on the NIRvP and eco-evolutionary optimality (EEO) theory (NIRvP-EEO) with Sentinel-2 imagery and meteorological data on Google Earth Engine for crop GPP estimation without the need for calibration. Specifically, we integrated the Ball-Berry stomatal conductance model into NIRvP-EEO to balance carbon and water vapor fluxes. To enable near-real-time daily monitoring of crop GPP, we employed temporal-weighted interpolation and Whittaker-smoothing filtering methods to fill data gaps. Compared to benchmark models such as enhanced SatelLite Only Photosynthesis Estimation (ESLOPE), crop SLOPE (CSLOPE), GPP network (GPP-net) and P-model, the NIRvP-EEO model demonstrated improved daily GPP estimation for four major crops including corn, soybean, wheat and rice. We found that NIRvP-EEO could reliably GPP estimation not only in drought and heatwave years but also in flood years. Additionally, the model effectively captures the fine spatial details and interannual variations in GPP for these crops. By leveraging the Google Earth Engine platform, our model enables conduct near-real-time daily continuous monitoring of crop GPP at a high spatial resolution anywhere in the world.