Linking Remote Sensing Spectral Signals to Agricultural Decision‐Making via a Novel Agrohydrological Model With Explicit Canopy Radiative Transfer and Plant Physiology

Abstract

Remote‐sensing spectral observations are widely used to monitor crop growth and productivity, yet their potential for agrohydrological forecasting remains limited by a lack of mechanistic links to crop and soil processes. Existing models often prescribe canopy states, simplify physiological responses, or omit explicit spectral simulations, limiting their ability to use spectral signals to constrain key crop and soil variables. To address this, we developed the STEMMUS‐SCOPE‐PG model, integrating a process‐based plant growth (PG) module into the STEMMUS‐SCOPE framework. In this system, simulated carbon assimilation and soil moisture jointly drive dynamic changes in leaf area index (LAI) and biomass, which in turn feed back into canopy radiative transfer, enabling bidirectional coupling between spectral signals (e.g., reflectance and solar‐induced chlorophyll fluorescence (SIF)) and hydrological fluxes. The model was evaluated using three years of field data from maize (C4) and soybean (C3) AmeriFlux sites (US‐Bo1 and US‐Ne3), together with MODIS/TROPOMI satellite observations. Results show that STEMMUS‐SCOPE‐PG reproduced LAI and biomass across both sites, and captured seasonal SIF dynamics consistent with TROPOMI observations. Relative to the original SCOPE and STEMMUS‐SCOPE configurations, the model reduced mean RMSE by 28.8% and 28.3% for gross primary productivity, and by 15.4% and 14.3% for evapotranspiration, respectively. Moreover, we revealed a linear relationship between daily LAI change and SIF during vegetative stages, and a nonlinear relationship during reproductive stages, highlighting phenological effects on crop‐SIF dynamics. These findings demonstrate the value of linking crop physiology with satellite observations, supporting applications in precision agriculture and land‐surface modeling.