## 1 Introduction  
Wetlands represent a critical and invaluable ecological asset situated at the intersection of land and water environments [1]. Typically experiencing periods of inundation or water saturation for a part of the year, these areas play a pivotal role in the ecosystem [2]. Within wetland regions, woodlands, shrubs, and emergent plants make up the dominant coverage [1]. Their influence on biological, ecological, and hydrological services, encompassing regional and global scales, holds profound significance. Noteworthy among these services are climate regulation, water purification, flood mitigation, and preservation of wildlife habitats [3, 4, 5]. Functioning as downstream recipients, wetlands receive water and waste from natural and anthropogenic sources, which is why they are known as the environment's kidneys [6, 7]. These intrinsic services and processes have collectively established wetlands as the most valuable component of nature [8]. Despite the advantages and significance of wetlands, these ecosystems have experienced considerable decline and degradation in recent decades. This process has been further exacerbated by climate change [9; 10]. Wetlands are now the most endangered ecosystems due to the convergence of climate change and anthropogenic activities, characterized by shifts in land-use patterns [11]. Consequently, the implementation of effective management programs and precise monitoring techniques becomes essential in comprehensively assessing and protecting wetlands against further degradation. The conventional method of wetland monitoring through field campaign measurements yields essential reference data for the assessment of wetlands. However, these approaches are time-consuming, demanding significant fieldwork, and are mainly applicable to localized scales [2]. Moreover, the presence of numerous wetlands in difficult-to-reach regions further emphasizes the ineffectiveness of these strategies [4]. Satellite remote sensing observations effectively address the deficiencies of traditional approaches and offer valuable insights for monitoring land-use land-cover (LULC) and wetland dynamics with frequent data collection [12; 13]. Multispectral satellite sensors have gained widespread recognition and achieved significant success in the field of wetland monitoring [14; 15]. Leveraging their high temporal and spatial resolution and extensive global coverage, these sensors have emerged as invaluable assets for wetland classification. However, their efficacy is constrained in specific circumstances, such as cloud cover and night-time data acquisition. Furthermore, the separation of certain wetland classes solely through the utilization of multispectral data is a challenging task [10]. The adoption of synthetic aperture radar (SAR) has expanded wetland classification due to its remarkable ability for data collection independent of solar conditions and cloud presence, along with its capacity to penetrate vegetation canopies [16] at high resolution and wide swath coverage [17]. Despite the distinct advantages offered by SAR data, the classification of wetland areas is still challenging when relying solely on SAR information. This complexity arises from the similarity in SAR backscatter response to the presence of water, a common characteristic among various wetland land covers, whether situated under the surface of vegetation canopies, at the canopy, or within the soil [18; 19]. The fusion of diverse satellite data sources, such as SAR and multispectral data, facilitates the precise monitoring of various land-cover characteristics and dynamics [5; 19]. The integration of multispectral and SAR data holds the potential to improve the accuracy of each individual data type. The fused data could effectively differentiate between classes exhibiting similar characteristics, such as bog and fen, surpassing the performance achieved with singular data types [20].