## 5 Conclusions  
Wetland classification is a challenging task for remote sensing research due to the similarity of different wetland types in spectrum and texture, but this challenge could be eased by the use of multi-source satellite data. In this study, a synergetic classification method for GF-3 full-polarization SAR and OHS hyperspectral imagery was proposed in order to offer an updated and reliable spatial distribution map for the entire YRD coastal wetland. Three classical machine learning algorithms (ML, MD, and SVM) were used for the synergetic classification of 18 spectral, index, polarization, and texture features. According to the field investigation and visual interpretation, the overall synergetic classification accuracy of 97% for ML and SVM algorithms is higher than that of single GF-3 or OHS classification, which proves the performance of the fusion of fully polarized SAR data and hyperspectral data in wetland mapping.  
The spatial distribution of coastal wetlands affects their ecological functions. Detailed and reliable wetland classification can provide important wetland type information to better understand the habitat range of species, migration corridors, and the consequences of habitat change caused by natural and anthropogenic disturbances. The synergy of PolSAR and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. Therefore, the proposed method has the potential to provide accurate results in different regions.  
**Author Contributions:** Conceptualization, P.L. and Z.L.; methodology, C.T., P.L., D.L., and Z.L.; formal analysis and validation, C.T., D.L., and P.L.; investigation, C.T., P.L., D.L., Q.Z., M.C., J.L., G.W., and H.W.; resources, P.L., S.Y., and Z.L.; writing--original draft preparation, C.T. and P.L.; writing--review and editing, C.T., P.L., Z.L., H.W., M.C., and Q.Z.; project administration, P.L., Z.L., and H.W.; data curation, C.T., S.Y., and P. L.; visualization, C.T. and P. L.; supervision, P.L., Z.L., and H.W.; funding acquisition, P.L., Z.L., and H.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was jointly supported by the Natural Science Foundation of China (no. 42041005-4; no. 41806108), National Key Research and Development Program of China (no. 2017 YFE0133500; no. 2016 YFA0600903), Open Research Fund of State Key Laboratory of Estuarine and Coastal Research (no. SKLEC-KF202002) from East China Normal University, as well as State Key Laboratory of Geodesy and Earth's Dynamics from Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences (SKLGED2021-5-2). [PERSON] was supported by the European Space Agency through the ESA-MOST DRAGON-5 Project (ref.: 59339).  
**Acknowledgments:** The authors are grateful to colleagues from the Ocean University of China for conducting the field GPS RTK measurements in the Yellow River Delta. ERA5 data are freely available from the Copernicus Climate Change Service (C3S). Tide level data are provided by the National Marine Data and Information Service (NMDIS), China. The authors thank the National Satellite Ocean Application Service for providing the GF-2 and GF-3 data. We also thank the Zhuhai Orbita Aerospace Science & Technology Co. Ltd. for providing the Zhuhai-1 OHS data. Finally, the authors sincerely thank all anonymous reviewers and the editors for their constructive and excellent reviews that have greatly improved the quality of the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.