Abstract: Hangzhou City possesses diverse and abundant wetland resources. However, with the rapid urbanization, the area of wetlands has been shrinking, and issues such as degradation of ecosystem quality and functional decline have become increasingly severe. As a result, conducting a refined classification research on land cover and wetland types in Hangzhou City is crucial for creating effective wetland protection and management policies. In this study, we employed the Google Earth Engine (GEE) cloud platform and Sentinel-2 satellite imagery data to perform a classification of wetland in Hangzhou City using the Random Forest algorithm. Our findings revealed that a combination of multiple feature information substantially enhanced classification accuracy, compared to using single-feature information when executing refined wetland classification. The optimal feature combination encompassed traditional spectral features, red-edge spectral features, transformed features, texture features, and topographic features, yielding an overall accuracy of 81.2% and a Kappa coefficient of 0.75. Different feature information significantly contributed to the extraction of various wetland types. Traditional spectral features were particularly advantageous for identifying tidal flats and aquaculture ponds, while red-edge spectral features and transformed features were more effective for recognizing herbaceous marshes and lakes, respectively. Furthermore, combining red-edge spectral features and texture features proved beneficial for identifying canals and water channels. The feature comparison experiment in this study serves as a reference for future case studies on refined wetland classification, and the wetland classification results provide data support for the remote sensing identification of wetland information in Hangzhou City.