Tobacco　is　an　important　economic　crop　in　the　southern　part　of　China,　e.g.,　Fujian　Province.　Detailed　spatial　information　of　tobacco　planting　is　essential　for　a　good　agriculture　plan　and　sustainable　management　of　tobacco.　Optical　remote　sensing　images　acquired　in　the　Fujian　region　are　heavily　affected　by　cloud　coverage　due　to　a　subtropical　climate.　In　this　study,　we　investigate　the　use　of　time　series　C-band　Sentinel-1　(S1)　SAR　data　to　extract　tobacco　planting　areas.　We　use　a　Long　Short-Term　Memory　(LSTM)　model　to　quantify　the　relations　between　tobacco＇s　phenological　information　and　the　time　series　of　features　extracted　from　S1　SAR　data.　More　specifically,　the　VH　polarization　channel　was　used　to　create　the　time　series　of　feature　datasets.　Experiments　were　conducted　on　the　S1　SAR　dataset　acquired　during　the　growth　cycle　of　tobacco　from　2019　to　2020　in　Nanping,　Fujian,　China.　To　evaluate　the　effectiveness　of　the　proposed　method,　we　compared　the　extraction　results　with　that　of　the　conventional　machine　learning　method,　i.e.,　Light　Gradient　Boosting　Machine　(Light　GBM).　Results　show　that　the　tobacco　areas　extracted　by　the　proposed　LSTM　method　have　an　overall　accuracy　of　82.9%,　based　on　validation　samples　derived　from　very　high　resolution　remote　sensing　images　and　a　field　survey　conducted　in　2020.　The　obtained　extraction　accuracy　is　higher　than　that　of　the　Light　GBM　method,　i.e.,　78.6%.　We　conclude　that　the　proposed　LSTM　method　has　a　high　potential　for　mapping　tobacco　planting　in　(sub)tropical　regions　using　time　series　of　S1　SAR　data,　and　can　be　used　as　an　alternative　method　for　mapping　the　planting　of　other　crop　types　from　remote　sensing　images.　©　2021　IEEE.