Accurate　and　timely　crop　mapping　is　essential　for　food　security　assessment,　and　high-quality　feature　factors　are　the　core　foundation　for　accurate　mapping.　However,　deep　learning　model　crop　classification　algorithms　have　achieved　some　success,　while　the　models　themselves　struggle　to　explain　the　specific　contribution　and　impact　of　different　features　on　the　results.　In　this　study,　a　self-adaptive　Feature-attention　Kolmogorov-Arnold　Network　(FKAN)　is　proposed　for　interpretable　and　scalable　crop　mapping.　The　model　integrated　the　adaptive　weighted　feature　attention　module　(AWFA)　and　the　interpretable　KAN　network,　which　can　visualize　the　complex　associations　between　features　and　target　crops　and　automatically　capture　and　filter　effective　key　spatiotemporal　features,　thus　enhancing　the　interpretability　of　the　model.　Experimental　results　demonstrate　that　integrating　optical,　radar,　and　terrain　features　yields　superior　performance　in　both　sample　prediction　and　crop　mapping,　surpassing　existing　methods.　The　proposed　FKAN　achieves　an　overall　accuracy　and　F1　score　exceeding　0.90.　Optical　and　radar　features　contribute　the　most　significantly　to　classification　accuracy,　while　terrain　data　provides　complementary　enhancement.　By　aligning　with　key　crop　phenology　and　leveraging　the　Google　Earth　Engine　(GEE),　FKAN　establishes　the　first　operational　platform　for　global　winter　wheat　identification,　enabling　accurate　and　scalable　crop　mapping.　The　migrated　model　achieves　over　85%　accuracy　across　different　regions　and　years,　demonstrating　strong　robustness　and　generalization　capability.　The　study　identifies　optimal　phenological　periods　and　feature　indices　for　different　crops,　providing　scientific　guidance　for　future　mapping　efforts.　The　FKAN　model　demonstrated　robustness,　scalability,　and　interpretability,　was　able　to　automatically　extract　high-confidence　pixels　and　generate　crop　planting　probabilities,　providing　an　efficient　and　scalable　solution　for　large-scale　crop　monitoring.　This　study　generated　the　first　global　winter　wheat　map　GlobalWinterWheat10m　dataset　by　the　FKAN　algorithm.　The　code　and　demo　link　is　accessible　at　https://github.com/FZUcheng123/FKAN.　©　2025　Elsevier　B.V.