This study employed Sentinel-1A C-band and Sentinel-2A multispectral data combined with the decision tree ensemble algorithms to map the spatial distribution of five mangrove communities in a coastal area in North Vietnam. The results show that the rotation forests (RoFs) model achieved better overall accuracy and kappa coefficient in mapping mangrove species than those of the canonical correlation forests (CCFs) and the random forests (RFs) models. This research demonstrates the potential of using optical and SAR data together with machine learning techniques to map mangrove species in tropical areas.