High-Precision Crop Identification and Feature Contribution Mechanism in Plateau Mountainous Regions Based on Adaptive Geographic Partitioning and Local Modeling

Accurate crop identification in plateau mountainous regions is essential for food security, yet geospatial non-stationarity and topography-induced spectral paradoxes often compromise global model performance due to a “homogenization constraint.” This study developed an adaptive local modeling framework that partitions the landscape into biophysically homogeneous subregions using the Spectral Angle Mapper (SAM), effectively isolating terrain-induced illumination variance from intrinsic spectral responses. Independent local classifiers were coupled with SHAP and GAMs to interpret the resulting spatial variations in feature contributions. Results demonstrate that: (1) the partitioned local models significantly outperform the global baseline (OA = 93.1%, Kappa = 0.919) by mitigating the suppression of local signals inherent in global datasets; (2) the framework captures a mechanistic shift in feature importance, transitioning from a strong “Hydro-Topographic” coupling in highlands (Interaction Strength > 0.15) to “Spectral-Texture” complementarity in plains; and (3) major crop distributions are governed by quantifiable biophysical thresholds—such as a <28.5 °C thermal limit for maize and a >971 mm precipitation boundary for rice—which exhibit consistency with regional agrometeorological principles. These findings suggest that integrating adaptive partitioning with interpretable local modeling transforms geospatial non-stationarity from a source of classification error into explicit, zone-specific decision rules, providing a robust and scientifically grounded solution for precision agriculture in heterogeneous terrains.