Interannual Multicrop Identification in Large Area Based on Optimized Monthly Tile Classification Model With Spatio-Temporal Distance Features Fusion

Accurate crop identification is crucial for agricultural trade, market risk management, and food security. Current research on automatic interannual sample extraction for crop mapping often emphasizes multisource feature fusion but overlooks the importance of feature distance differences in crop seeding processes. This study focuses on crop mapping in the Hetao Plain from 2020 to 2023, using high-resolution (HR) Sentinel-1 and Sentinel-2 remote sensing data. We introduce a method called BGSI-DFF-WMRF, which combines the bidirectional global selection index (BGSI) and distance feature fusion (DFF) under the interannual weaving net month probability random forest (WMRF). BGSI captures the coupling between phenological and spatial factors necessary for crop growth in different regions under feature fusion. Additionally, sample migration under feature fusion enhances the accuracy and representativeness of sample points across different years. WMRF integrates a monthly classifier with multisource feature distance interpolation. The BGSI-DFF-WMRF method achieved over 82% classification accuracy for crops like wheat, corn, and sunflower in the Hetao Irrigation District (HID) region, with an accuracy of 91.81% in the western area. Field samples from 2023 were successfully applied to previous years (2020-2022) through feature fusion expression (FFE) transfer. The method outperformed existing products and local statistical data, particularly for corn, demonstrating high accuracy and robustness in crop mapping. Coupling spatio-temporal factors enhances large-scale crop identification and holds great significance for the advancement and widespread adoption of large-scale crop identification techniques.