## 2 Study Area and Dataset  
The study area under investigation is St. John's, located on the Avalon Peninsula in the southeastern region of Newfoundland Island, Canada. This island has a humid continental climate, significantly influenced by the nearby Atlantic Ocean. The majority of land cover type in Newfoundland is wetlands, encompassing various wetland categories such as bogs, fens, marshes, swamps, and water bodies. Other land cover types, such as forests, shrublands, pastures, barren, and urban areas, are also considered in this study. Reference Data  
To gather accurate annotated ground truth data, a methodology similar to previous studies was employed [47; 48; 49; 50]. Previous classifications and wetland ground-truth data from 2015 to 2019 were reviewed to identify additional wetlands for field verification, ensuring a distributed and representative sample within the study area. The field campaign involved visiting wetlands identified through prior classifications and remote sensing data to ensure a distributed and representative sample within the study area. During the campaign, wetlands were classified according to the Canadian Wetland Classification System [51]. Additionally, the fieldwork included collecting Global Positioning System (GPS) data, photographs, and field notes to determine the land cover types. Due to accessibility and time constraints, only wetlands within 200 m of roads and pathways were surveyed. These wetlands were later determined using very high-resolution (VHR) imagery, LiDAR-derived digital elevation models (DEMs), and ancillary field data. To verify data accuracy, the collected polygons were cross-validated against multi-seasonal VHR imagery and previous datasets collected in prior years. Remote sensing-based vegetation indices, such as the normalized difference vegetation index (NDVI), were used to verify vegetation patterns and validate classifications. Additionally, non-wetland land cover information was integrated using VHR imagery, Google Earth multi-seasonal imagery, and the Agriculture and Agri-food Canada's 2018 Crop Inventory map [52], resulting in a comprehensive dataset of both wetland and non-wetland classes. The annotated dataset is shown in Figure 1 and detailed in Table 1. It includes 829 classified polygons and 221,598 sample pixels. Of these, 115,280 samples represent wetland classes, while 106,318 samples represent non-wetland classes. Among the wetland samples, bog and fen were the most common categories, accounting for more than 30% of all samples. Figure 1: (**a**) True-color RGB Sentinel-2 image of the study area and (**b**) ground truth samples over the study area. It is evident from Table 1 that there is a high class imbalance between the different land cover classes. For example, in our dataset, the bog and fen classes have significantly more samples (42,148 and 29,648, respectively) compared to classes like marsh and barren (3445 and 1789, respectively). Class imbalance can potentially affect the training and testing phases of machine learning models by biasing the model towards the majority classes. The proposed Wet-ConViT model and other compared models are inherently robust to class imbalances due to their complex architectures, which are capable of learning from limited samples. The use of convolutional and transformer blocks helps capture both local and global patterns, improving the model's ability to generalize across classes with fewer samples. Despite the imbalance, the results indicate that the proposed model achieved high accuracy in classifying various wetland classes, demonstrating its effectiveness even in the presence of class imbalance. Sentinel-1 SAR and Sentinel-2 multispectral time series data from the summer of 2021 were obtained through the Google Earth Engine (GEE) cloud platform.