Abstract: Accurately identifying plant communities in coastal wetlands is critical for strengthening the ecological quality monitoring and enhancing the ecosystem functions of coastal wetlands. With the Yellow River Delta as the study area, this study constructed a feature vector set including phenological, optical, red-edge, and radar features based on Sentinel-1/2 image data using the Google Earth Engine (GEE) platform. It classified the wetland plant communities in the Yellow River Delta in 2021 using the random forest algorithm. Moreover, it explored the effects of phenological features in classification. The results reveal an overall classification accuracy of 97.91 % and a Kappa coefficient of 0.97. In 2021, the distribution areas of Phragmites australis, Suaeda glauca, Spartina alterniflora, and Tamarix chinensis were 49.91 km², 39.91 km², 79.36 km², and 20.86 km², respectively. The phenological features of typical plant communities in the Yellow River Delta wetlands were effectively extracted based on the normalized difference vegetation index (NDVI) time-series fitting curves. The highly distinguishable features included the maximum value date, base value, growth amplitude, beginning-of-season growth rate, and end-of-season decline rate. Compared to other feature variables, phenological features contributed more significantly to the overall classification accuracy, suggesting their prominent role in classification. The results of this study provide a methodological reference and scientific basis for the plant community monitoring and ecological conservation of coastal wetlands in the Yellow River Delta.