Integrating Remote Sensing and Crop Physiological Processes for Large-Scale Winter Wheat Yield Estimation in the Indo-Gangetic Plain

Accurate and large-scale prediction of winter wheat yield during the rabi season in parts of the Indo-Gangetic Plain (IGP) is crucial for food security and efficient agricultural management. Despite significant advances, existing yield prediction methods often face trade-offs among accuracy, interpretability, robustness, and generalizability. To address these challenges, this study developed a remote sensing-based biophysical framework integrating satellite observations, meteorological data, and crop physiology. Gross Primary Productivity (GPP) was estimated using the Vegetation Photosynthesis Model (VPM) and subsequently converted to Net Primary Productivity (NPP), aboveground biomass (AGB), and grain yield using crop-specific carbon allocation and harvest index parameters. The results showed that the spatial distribution of wheat yield from 2001 to 2021 was relatively stable, with higher yield in the northwest and lower yield in the east and southeast, indicating persistent spatial heterogeneity. The mean wheat yield for 2001-2020 was 2566.92 kg/ ha with a standard deviation of 194.56 kg/ ha and a coefficient of variation of 7.58%, indicating relatively low inter-annual variability. Annual mean yields varied from 2231.66 to 2914.89 kg/ha due to the influence of inter-annual variations in climatic conditions, water availability and crop management practices. Validation with district-level wheat yield data from the Directorate of Economics and Statistics (DES) demonstrated a strong agreement between estimated and observed yields (r = 0.47-0.91), supporting the reliability of the proposed framework. Overall, the study demonstrates the potential of remote sensing and biophysical modelling integration for large-scale wheat yield monitoring and forecasting in the IGP.