Spatial　features　retrieved　from　satellite　data　play　an　important　role　for　improving　crop　classification.　In　this　study,　we　proposed　a　deep-learning-based　time-series　analysis　method　to　extract　and　organize　spatial　features　to　improve　parcel-based　crop　classification　using　high-resolution　optical　images　and　multi-temporal　synthetic　aperture　radar　(SAR)　data.　Central　to　this　method　is　the　use　of　multiple　deep　convolutional　networks　(DCNs)　to　extract　spatial　features　and　to　use　the　long　short-term　memory　(LSTM)　network　to　organize　spatial　features.　First,　a　precise　farmland　parcel　map　was　delineated　from　optical　images.　Second,　hundreds　of　spatial　features　were　retrieved　using　multiple　DCNs　from　preprocessed　SAR　images　and　overlaid　onto　the　parcel　map　to　construct　multivariate　time-series　of　crop　growth　for　parcels.　Third,　LSTM-based　network　structures　for　organizing　these　time-series　features　were　constructed　to　produce　a　final　parcel-based　classification　map.　The　method　was　applied　to　a　dataset　of　high-resolution　ZY-3　optical　images　and　multi-temporal　Sentinel-1A　SAR　data　to　classify　crop　types　in　the　Hunan　Province　of　China.　The　classification　results,　showing　an　improvement　of　greater　than　5.0%　in　overall　accuracy　relative　to　methods　without　spatial　features,　demonstrated　the　effectiveness　of　the　proposed　method　in　extracting　and　organizing　spatial　features　for　improving　parcel-based　crop　classification.