Abstract: Using advanced remote sensing technology to classify and map coastal wetlands is vital for wetland environment management and protection. Taking the core area of Yancheng National Rare Bird Nature Reserve, Jiangsu, as a typical area, the study obtained Sentinel-1 and Sentinel-2 time-series images of the critical period of vegetation growth (May to September) to composite monthly images on GEE platform. A total of 63 test sets were formed of five single month datasets and four multi-month datasets with different time spans using 7 different band combinations. Wetland classification mapping and comparative analysis were performed on each test set using the random forest method. The results showed that, compared to those that used only Sentinel-2 data in the visible and NIR bands, adding the SWIR bands could  significantly improve the classification results with an overall accuracy improvement over 8%. An overall accuracy improvement of about 5% was achieved by adding the red-edge band. Compared to those that used only Sentinel-2 spectral information, adding vegetation index and Sentinel-1 SAR information to wetland classification achieved the highest overall accuracy of 90.6%. Further, the experiments also verified that the classification effect of using multi-month time-series remote sensing data was better than that of using single month data. This research provided technical reference for wetland classification in regional scale with medium/high resolution remote sensing images.