Phenological　information　on　crop　growth　aids　in　identifying　crop　types　from　remote　sensing　images,　but　its　incorporation　into　classification　models　is　insufficiently　exploited,　especially　in　deep　learning　frameworks.　This　study　presents　a　new　model,　Phenological-Temporal-Spatial　LSTM　(PST-LSTM),　for　mapping　tobacco　planting　areas　in　smallholder　farmlands　using　time-series　Sentinel-1　Synthetic　Aperture　Radar　(SAR)　images.　The　PSTLSTM　model　is　built　on　a　multi-modal　learning　framework　that　fuses　phenological　information　with　deep　spatial-temporal　features.　We　applied　the　model　to　extract　tobacco　planting　areas　in　Ninghua,　Pucheng,　and　Shanghang　Counties,　in　Fujian　Province,　and　Luoping　County　in　Yunnan　Province,　China.　We　compared　PSTLSTM　with　existing　methods　based　on　phenological　rules　and　Dynamic　Time　Warping　(DTW)　methods,　and　analyzed　its　strength　in　feature　fusion.　Results　showed　that　our　model　outperformed　these　methods,　achieving　an　overall　accuracy　(OA)　of　93.16%　compared　to　86.69%　and　85.93%　for　the　phenological　rules　and　DTW　methods,　respectively,　in　the　Ninghua　area.　PST-LSTM　effectively　integrated　time-series　data　with　phenological　information　derived　from　different　strategies　at　the　feature　level　and　performed　better　than　existing　feature　fusion　methods　(based　upon　fuzzy　sets)　at　the　decision　level.　It　also　demonstrated　a　better　spatial　transferability　than　other　methods　when　applied　to　different　study　areas,　achieving　an　OA　of　90.95%,　91.41%,　and　80.75%　for　the　Pucheng,　Shanghang,　and　Luoping　areas,　respectively,　using　training　samples　from　Ninghua.　We　conclude　that　PST-LSTM　can　effectively　extract　tobacco　planting　areas　in　smallholder　farming　from　time-series　SAR　images　and　has　the　potential　for　mapping　other　crop　types.