Accurate　mapping　of　winter　wheat　provides　essential　information　for　food　security　and　ecosystem　protection.　Deep　learning　approaches　have　achieved　promising　crop　discrimination　performance　based　on　multitemporal　satellite　imagery.　However,　due　to　the　high　dimensionality　of　the　data,　sequential　relations,　and　complex　semantic　information　in　time-series　imagery,　effective　methods　that　can　automatically　capture　temporal-spatial　features　with　high　separability　and　generalizability　have　received　less　attention.　In　this　study,　we　proposed　a　U-shaped　CNN-Transformer　hybrid　framework　based　on　an　attention　mechanism,　named　the　U-Temporal-Spatial-Transformer　network　(UTS-Former),　for　winter　wheat　mapping　using　Sentinel-2　imagery.　This　model　includes　an　“encoder-decoder”　structure　for　multiscale　information　mining　of　time　series　images　and　a　temporal-spatial　transformer　module　(TST)　for　learning　comprehensive　temporal　sequence　features　and　spatial　semantic　information.　The　results　obtained　from　two　study　areas　indicated　that　our　UTS-Former　achieved　the　best　accuracy,　with　a　mean　MCC　of　0.928　and　an　F1-score　of　0.950,　and　the　results　of　different　band　combinations　also　showed　better　performance　than　other　popular　time-series　methods.　We　found　that　the　MCC　(MCC/All)　of　the　UTS-Former　using　only　RGB　bands　decreased　by　4.53　%,　while　it　decreased　by　13.36　%　and　35.02　%　for　UNet2d-LSTM　and　CNN-BiLSTM,　respectively,　compared　with　that　of　all　the　band　combinations.　The　comparison　demonstrated　that　the　proposed　UTS-Former　could　capture　more　global　temporal-spatial　information　from　winter　wheat　fields　and　achieve　greater　precision　in　terms　of　local　details　than　other　methods,　resulting　in　high-quality　mapping.　The　analysis　of　attention　scores　for　the　available　acquisition　dates　revealed　significant　contributions　of　both　beginning　and　ending　growth　images　in　winter　wheat　mapping,　which　is　valuable　for　making　appropriate　selections　of　image　dates.　These　findings　suggest　that　the　proposed　approach　has　great　potential　for　accurate,　cost-effective,　and　high-quality　winter　wheat　mapping.　©　2024　International　Society　for　Photogrammetry　and　Remote　Sensing,　Inc.　(ISPRS)