Bamboo　groves　predominantly　thrive　in　tropical　or　subtropical　regions.　Assessing　the　efficacy　of　remote　sensing　data　of　various　types　in　extracting　bamboo　forest　information　from　bright　and　shadow　areas　is　a　critical　issue　for　achieving　precise　identification　of　bamboo　forests　in　complex　terrain.　In　this　study,　34　features　were　obtained　from　Sentinel-1　SAR　and　Sentinel-2　optical　images　using　the　Google　Earth　Engine　platform.　The　normalized　shaded　vegetation　index　(NSVI)　was　then　employed　to　segment　the　bright　and　shadow　woodlands.　Different　features　from　diverse　data　sources　were　evaluated　to　extract　bamboo　forest　information　in　the　bright　and　shadow　areas,　then　use　the　random　forest　(RF)　classification　algorithm　to　extract　bamboo　forest.　The　results　showed　that　(1)　the　red-edge　and　short-wave　infrared　bands　of　Sentinel-2　optical　images　and　their　corresponding　vegetation　indices　are　significant　in　bamboo　forest　information　extraction.　(2)　The　dissimilarity　and　homogeneity　of　Sentinel-2　texture　features　in　the　bright　area　and　dissimilarity　in　the　shadow　area,　the　Sentinel-1　backscatter　features　in　the　bright　area　and　the　VV　and　VH　in　the　bright　area　and　VV-VH　in　the　shadow　area　have　some　variability　between　bamboo　and　nonbamboo　forests,　which　can　be　used　as　effective　features　for　bamboo　forest　extraction.　(3)　The　combination　of　spectral,　texture　and　backscatter　features　yields　the　highest　overall　classification　accuracy　and　Kappa　coefficient,　at　87.96%　and　0.7435,　respectively.　This　study　has　the　potential　for　remote　sensing　refinement　of　bamboo　forest　identification　in　complex　terrain　areas　by　utilizing　subregion　classification　methods　combined　with　optical　and　radar　image　features.