Abstract
A comprehensive analytical framework has been developed for the spatio-temporal forecasting of spring wheat yield in risk-prone rainfed agricultural zones. The study is grounded in 25-year time series integrating remote sensing data, meteorological reanalysis products, and soil parameters. The implementation of the XGBoost algorithm enabled the modeling of complex nonlinear biophysical relationships. To account for spatial autocorrelation and Tobler’s First Law of Geography, a two-level validation strategy was employed. The interpolation performance achieved an accuracy of R2 = 0.69 (RMSE = 0.33 t/ha), while extrapolation to unseen regions yielded R2 = 0.65 (RMSE = 0.35 t/ha), demonstrating the robustness and transferability of the proposed architecture. Application of the TreeSHAP interpretability framework revealed the dominant influence of agroclimatic drivers, highlighting the critical role of April soil moisture recharge and the significance of winter insolation as a proxy for snow cover persistence and surface albedo dynamics. The superiority of NDWI over NDVI for detecting latent water stress during the grain-filling stage was empirically confirmed. Unlike prior frameworks that rely predominantly on growing-season vegetation indices, the present study demonstrates that pre-seasonal agroclimatic drivers—particularly winter solar radiation and April moisture recharge—exert a stronger influence on yield than mid-season NDVI in arid rainfed systems. Geospatial analysis identified a pronounced domain shift in foothill and irrigated clusters, attributed to the coarse spatial resolution of climate grids and the irrigation-induced decoupling of crop phenology from precipitation regimes.