###### Abstract
However, there are still some problems in the detection and classification of different types of wetland using satellite remote sensing images. The spectral curves of the same vegetation may be different due to the influence of growth environment, diseases, and insect pests. Additionally, two different vegetation may present the same spectral characteristics or mixed spectral phenomenon in a certain spectral segment, which makes it difficult to identify wetland types well by only using spectral response curves. These two phenomena greatly influence the classification algorithm based on spectral information and easily cause misclassification [18]. The particularity of wetlands makes wetland classification a challenging topic in remote sensing study. Optical images can classify ground objects according to spectral features and various vegetation indices. Since the launch of the Landsat satellite in the late 1960s, wetland mapping has been an important application of remote sensing [19; 20; 21; 22]. In the early stages, single data source and classical algorithms were mainly used, but now mapping has gradually started using multisource data fusion and complex algorithms [23]. With the launch of hyperspectral satellites, hyperspectral remote sensing images are gradually becoming widely used [24; 25; 26]. Hyperspectral data are sensitive to tiny spectral details and can detect resonance absorption and other spectral features of materials within the wavelength range of the sensor [27]. [PERSON] and [PERSON] [21] introduced support vector machines (SVM) to class hyperspectral images and proved that SVM is an effective alternative to conventional pattern recognition approaches (feature-reduction procedures combined with a classification method) to classify hyperspectral remote sensing data. [PERSON] et al. [28] utilized Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) hyperspectral images for tree species mapping and indicated that hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects for the future. In recent years, the continuous launch of spaceborne synthetic aperture radar (SAR) systems have obtained a large number of on-orbit and historical archived data, providing an excellent opportunity for multi-temporal analysis, especially in coastal and cloudy areas [29]. Radar reflectivity is usually determined by the complex dielectric constant of the landcover, which in turn is dominated by the water content and geometric detail of the surface, e.g., smoothness or roughness of the surface and the adjacency of reflecting faces [27]. In the last two decades, many complicated and efficient classifiers and features have been investigated and integrated into the polarimetric SAR (hereinafter referred to as PolSAR) image classification framework to improve classification accuracy [30; 31; 32; 33]. [PERSON] et al. [34] used Sentinel-1 dual polarization VV and VH data to discriminate treed and non-treed wetlands in boreal ecosystems. [PERSON] et al. [35] use multi-temporal RADARSAT-2 fine resolution quad polarization (FQ) data to classify wetlands in Finland. The results show that the covariance matrix is a critical feature set of wetland mapping, and polarization and texture features can improve the overall accuracy. Therefore, the use of multi-temporal PolSAR classification shows considerable potential for wetland mapping. Full-polarization SAR data also have great advantages in wetland classification. Previous studies have shown that multisensor remote sensing information fusion can improve the final quality of information extraction by relying on the existing sensor data without increasing the cost [23; 36; 37; 38; 39]. Due to the variety and complexity of coastal wetland types, it is necessary to consider multisource data fusion to improve the accuracy of wetland classification [40; 41; 42; 7].