Wetlands provide important ecosystem functions for water alteration and conservation of biodiversity. Mapping wetland plant communities at a fine scale is crucial for assessing wetland functions and for better management. However, optical remote sensing images are limited by clouds and fog, making it difficult to obtain timely information on wetlands over large areas. Synthetic aperture radar (SAR) time series are widely utilized for mapping and characterizing wetlands by examining the correlation between the distribution of plant community formations and the duration of flooding. We aim to identify the optimal number and crucial dates of SAR images for classification to map wetland plant communities. Using a time series of Sentinel-1 SAR data on Google Earth Engine, a long-term remote sensing images dataset of Poyang Lake in 2019 was constructed. Combining the phenological features of wetland plant communities, we calculated and selected the separability of their monthly backscatter features using the threshold separation method. With the optimal temporal radar classification features identified, the random forest model was then applied to delineate the spatial distribution of wetland plant communities. The results show that time-series SAR images can be used to classify plant communities. SAR backscatter intensity under VH polarization is more sensitive to wetland plant communities than VV polarization, with optimal radar data acquisition dates being January, May, September, and November. The good result achieves a classification accuracy of 88.5% for Poyang Lake wetland plant communities in 2019, along with a Kappa coefficient of 0.821. We introduce a new approach for herbaceous wetland monitoring using SAR imagery, which is of great significance to the ecological protection and restoration of Poyang Lake.