###### Abstract  
The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhaihai-1 Constellation Orbits Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances. which is crucial to reduce atmospheric carbon dioxide concentration and slow down global climate change [2; 3]. In addition, the mudflats [4], mangroves, and vegetation (e.g., _Tamarix chinensis_, _Sueda salsa_, and _Spartina alterniflora_) [5] in coastal wetlands have strong carbon sequestration ability. Therefore, the coastal wetland is called the main body of the blue carbon ecosystem in the coastal zone [6]. The Yellow River Delta (hereinafter referred to as YRD) has a complete range of estuarine wetland types, including salt marshes, mudflats, and tidal creeks [7; 8]. However, intense anthropogenic activities in recent decades, such as dam building, agricultural irrigation, groundwater pumping, hydrocarbon extraction, and the artificial diversion of the estuary, have posed serious threats to the coastal wetlands of YRD [9; 10; 11; 12; 13]. Therefore, it is of great significance to carry out dynamic monitoring and obtain a reliable and up-to-date classification of coastal wetlands over the YRD for studying the impact of human activities on habitat area [14]. Wetland classification can illustrate the distribution and area of wetlands over geographical regions, which are helpful tools for evaluating the effectiveness of wetland policies [14]. In the last sixty years, wetland mapping and monitoring methods have been varied, mainly divided into field-based methods and remote sensing (RS) methods. Field-based wetland classification requires field work, which is labor-intensive, high in cost, time-consuming, and usually impractical due to poor accessibility. Therefore, it is only practical for relatively small areas [15]. In contrast, RS imagery can currently provide spatial coverage and repeatable observations in long-term series from local to regional scales, enabling effective detection and monitoring of different wetlands at a lower cost. However, wetland RS classification needs to be combined with sufficient field observations to train and evaluate the accuracy of classification [14]. RS has been demonstrated to be the most effective and economical method in wetland classification [15]. In addition, large-scale coastal wetland mapping is becoming a reality thanks to cloud computing platforms such as Google Earth Engine (GEE) [16; 17].