Context:　Wheat,　as　the　world＇s　largest　cereal　crop,　contributes　significantly　to　agricultural　intensification　through　crop　rotation　systems.　Updated　knowledge　of　cropping　patterns　(CP)　describing　crop　rotations　is　crucial　for　the　development　of　sustainable　agricultural　systems.　However,　there　is　a　gap　in　data　availability　and　finer　resolution　CP　maps　are　not　available　for　most　countries,　which　hampers　our　knowledge　of　geographically　targeted　crop　rotation　for　sustainable　management.　It　is　challenging　to　automatically　map　CP　at　large　scales　due　to　the　lack　of　ground-truth　datasets,　the　complexity　of　crop　rotation　systems,　and　the　limited　applicability　of　existing　algorithms.　Objective:　This　paper　has　three　objectives:　1)　propose　approaches　for　automatic　mapping　of　wheat　cropping　patterns;　2)　assess　its　capability　through　its　applications　over　conterminous　China;　3)　explore　the　distribution　patterns　for　wheat　of　crop　rotation　systems　in　China.　Methods:　This　study　introduced　a　novel　framework　for　automatic　agricultural　mapping　by　proposing　CP　indices　based　on　coupled　patterns　of　multi-source　imagery　and　inter-seasonal　variations.　This　study　developed　the　first　10-m　wheat　Cropping　Patterns　(ChinaCP-Wheat10m)　distribution　map　over　conterminous　China　by　proposing　a　robust　algorithm　for　mapping　Wheat　cropping　Patterns　by　fusing　Sentinel-1　SAR　and　Sentinel-2　MSI　data　(WPSS).　Results　and　conclusion:　The　ChinaCP-Wheat10m　map　showed　that　wheat　dominated　the　north　of　the　Yangtze　River　and　east　of　the　Taihang　Mountain,　with　a　distinctive　spatial　pattern　of　winter　wheat-rice　or　upland　crops　divided　by　the　Huaihe　River.　There　was　206,919　km2　of　wheat　sown　area　in　China　in　2020,　and　over　90　%　of　national　wheat　cultivation　was　implemented　by　double　cropping.　More　than　half　of　national　wheat　farming　was　intensified　through　rotation　by　maize　(51.39　%),　followed　by　paddy　rice　(21.12　%)　and　other　upland　crops　(18.90　%).　There　was　a　small　proportion　of　single　cropping　by　spring　wheat　(6.86　%)　and　winter　wheat　(1.73　%).　The　reliability　of　the　WPSS　was　validated　by　17,627　widely　distributed　reference　sites　with　an　overall　accuracy　of　92.57　%　and　good　agreement　with　the　agricultural　census　data　(R2　=　0.96).　Significance:　This　study　opens　a　new　direction　to　move　from　crop　type　identification　to　the　automatic　generation　of　crop　rotation　maps　at　the　national　scale,　which　would　facilitate　the　progress　of　the　Sustainable　Development　Goals　(SDGs)　to　reduce　poverty　and　hunger.　The　processing　codes　and　wheat　CP　records　produced　in　China　can　be　downloaded　from　the　following　link:　https://doi.org/10.6084/m9.figshare.28668173.v1