Precisely　delineating　the　distribution　of　moso　bamboo　forests　is　critical　for　forestry　management　and　regional　carbon　cycle　research.　The　unique　phonological　characteristics　(i.e.,　on-　and　off-year　phenomenon)　of　bamboo　impose　difficulties　in　bamboo　identification.　This　study　aims　to　develop　a　new　algorithm　for　mapping　bamboo　distribution　using　remote　sensing　data　with　the　consideration　of　bamboo　phenological　characteristics.　Three　optical　indices　were　proposed　based　on　canopy　reflectance　retrieved　from　Sentinel-2　and　field　inventory　data,　including　MBI　(Modified　Bamboo　Index),　BPCI　(Bamboo　Phenological　Characteristic　Index),　and　BPCI-2　(Bamboo　Phenological　Characteristic　Index　2).　The　collaboration　of　these　three　indices　with　the　RFE　(recursive　feature　elimination)　and　XGBoost　(extreme　gradient　boosting)　methods　can　precisely　mapping　bamboo　distribution　and　its　phenological　status.　The　model　based　on　MBI,　BPCI,　and　BPCI-2　outperformed　the　model　driven　by　existing　bamboo　extracting　indices,　i.e.,　BI　(Bamboo　Index),　YCBI　(Yearly　Change　Bamboo　Index),　MCBI　(Monthly　Change　Bamboo　Index),　increasing　in　overall　accuracy　(OA)　by　about　1.5%.　Additionally,　proposed　indices　were　calculated　using　the　data　synthesized　from　Sentinel-1　SAR　(synthetic　aperture　radar)　imageries　by　the　CycleGAN　(cycle-consistent　adversarial　network)　method　under　the　condition　without　cloudy-free　Sentinel-2　data　available　to　fill　the　time　series　data　gaps.　The　performance　of　model　based　on　augmented　data　improved　notably　in　comparison　with　the　model　driven　only　by　indices　from　original　optical　images,　with　the　identification　accuracy　for　on-　and　off-year　bamboo　samples　over　96%.　The　generated　moso　bamboo　distribution　map　aligns　well　with　forestry　inventory　data　in　terms　of　both　area　and　spatial　distribution.　The　proposed　indices　are　less　sensitive　to　terrain　than　existing　bamboo　extracting　indices.　This　merit　is　valuable　for　better　mapping　bamboo　forests,　which　are　mostly　distributed　in　mountainous　areas.　IEEE