Sentinel-2 Mapping of Sympodial Ma Bamboo (Dendrocalamus latiflorus Munro) Based on Seasonal Metrics and a SWIR2–RE1 Spectral Index

Ma bamboo (Dendrocalamus latiflorus Munro) is an ecologically and economically important sympodial bamboo species widely cultivated in southern China. Mapping its spatial distribution with satellite imagery remains challenging due to spectral response overlaps between Ma bamboo and other forest types. This study assessed the potential of integrating seasonal spectral variation and a targeted SWIR2–RE1 spectral contrast from Sentinel-2 to improve Ma bamboo mapping. Four normalized seasonal metrics were derived from winter and summer observations. A Ma Bamboo Spectral Index (MBSI) was developed based on the normalized contrast between SWIR2 and RE1. Four feature configurations were evaluated using XGBoost and nested spatial cross-validation. SHAP and accumulated local effects were further employed to interpret the model. The results showed that the model integrating annual spectral variables, seasonal metrics, and MBSI performed best, with an overall accuracy of 0.833, an F1 score of 0.819, and an AUC of 0.875. Seasonal metrics primarily improved producer’s accuracy, whereas MBSI contributed more to user’s accuracy, indicating complementary effects on reducing omission and commission errors. MBSI was the most important predictor, while RE1c was the most important seasonal metric and showed the strongest interaction with MBSI. These results show that combining seasonal spectral variation with the SWIR2–RE1 contrast can improve the completeness and reliability of Ma bamboo mapping and provide an interpretable framework for mapping in southern China.