## 1 Introduction
Previous studies have demonstrated the efficacy of this multi-sensor data fusion in mapping wetlands across diverse scales, ranging from local to large scales [21; 22; 23]. Large-scale wetland mapping has led to comprehensive inventories, laying the foundation for further endeavors on the development of wetland regulations, mapping of vulnerable wetland ecosystems to facilitate their preservation, and the discernment of evolving patterns within wetland environments [10]. The fusion of Sentinel-1 SAR and Sentinel-2 multispectral sensors from the Copernicus program of the European Space Agency (ESA) provides opportunities for precise LULC classification, along with facilitating wetland mapping. Both missions involve dual satellite constellations, with Sentinel-1 encompassing dual-polarized C-band SAR sensors and Sentinel-2 incorporating visible, near-infrared (NIR), and shortwave infrared bands. The Sentinel-1 SAR data provide insights into the physical and geometric structure of targets, while the Sentinel-2 multispectral data capture details regarding the molecular composition and chemical characteristics of these targets [5; 24]. The fusion of these two comprehensive Sentinel datasets has gained widespread attention for the purpose of wetland mapping, exploiting their complementary features delivered at frequent revisit intervals and decent spatial resolutions [25; 26]. Despite the presence of suitable datasets, such as fused SAR and multispectral imagery, the classification of wetlands remains a challenging task using remote sensing data. Each wetland class has distinctive features but shares many similarities with other wetland and non-wetland classes [27]. These similarities lead to difficulties in distinguishing between different classes, even when using spectral and backscattering information from optical and SAR imageries [28]. The application of nonparametric machine learning (ML) methodologies has addressed this issue to a feasible extent. ML algorithms, such as decision tree [29], random forest [30; 31], and support vector machines [32; 33], have shown great potential for wetland classification. For example, the fusion of Sentinel-1 and Sentinel-2 data to produce successive generations of Canadian wetland inventory maps is an example of recent development using the twin Sentinel mission [34; 35]. They utilized random forest classification techniques and yielded comprehensive insights into the spatial extent, condition, and distribution of wetlands across Canada. Nevertheless, the capability of ML techniques remains circumscribed by the procedure of feature selection. Consequently, rigorous feature selection has a vital role in the context of ML classification techniques [5; 36], yet this could sometimes be a challenging and tedious process that requires significant domain expertise. Deep learning (DL) models have emerged as efficient tools for automatically extracting optimal features, addressing the challenge of feature selection in ML methods [37; 38]. In the field of remote sensing, DL techniques have achieved considerable success across various applications, including LULC as well as wetland classification. A range of DL methods, such as feed-forward multilayer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), graph neural network, generative adversarial network (GAN), and the more recent transformer-based models, have been used for wetland mapping. Using their kernel abilities to extract neighboring pixel information, CNN models have particularly demonstrated efficacy in image classification tasks. [PERSON] et al. [39] presented the superior performance of CNNs when compared to conventional ML and shallow learning approaches in the classification of Alberta wetlands. [PERSON] et al. [40] compared twelve well-known CNN architectures in the classification of urban wetlands within Shenzhen, China, using Sentinel-2 multispectral data. This analysis revealed the superior performance of the DenseNet121 architecture compared to the other well-known CNN models.