Spatial　information　on　tobacco　planting　is　crucial　to　many　agricultural　applications　regarding　tobacco　production　and　management.　This　paper　presents　a　deep　learning　model,　i.e.,　Attention　Long　Short-Term　Memory　Fully　Convolutional　Network　(ALSTM-FCN),　to　extract　tobacco　planting　areas　using　time-series　Sentinel-1A　(S1A)　SAR　images.　Using　the　ALSTM-FCN　model,　high-level　temporal　and　spatial　image　features　are　fused　to　characterize　the　growth　of　tobacco　planting.　We　applied　the　ALSTM-FCN　to　extract　tobacco　in　the　Fujian　area　using　time-series　S1A　SAR　data　acquired　in　2020.　We　compared　the　proposed　method　with　a　conventional　LSTM　and　a　machine　learning　method　(e.g.,　Light　GBM).　Our　results　show　that　the　extracted　results　by　the　ALSTM-FCN　model　have　a　higher　extraction　accuracy　of　0.93　than　that　of　the　LSTM　of　0.92　and　the　Light　GBM　of　0.91.　We　conclude　that　the　proposed　ALSTM-FCN　method　can　be　used　as　a　promising　solution　for　extracting　tobacco　using　time-series　SAR　data　in　cloudy　and　rainy　areas.　©　2022　IEEE.