## 4 Experiments and Results
### Impact of Multi-Source Satellite Data  
To determine the influence of multispectral Sentinel-2 (S2) and Synthetic Aperture Radar Sentinel-1 (S1) data on the classification results obtained by the Wet-ConViT model, we conducted an ablation study, extending this analysis to other hybrid models as well. As represented in Table 3, the result of this analysis revealed that using only the S2 dataset yields significantly higher accuracies for all land cover categories when compared to the S1 dataset. For the proposed model, the single use of S2 data obtained an overall accuracy of approximately 93%, which is 11% higher than that obtained with a single source S1 dataset. Similar trends were observed for other hybrid models, although the proposed Wet-ConViT consistently outperformed them.  
When using S1 data, the proposed Wet-ConViT model showed superior performance with an OA of 82.56%, outperforming other models such as CoAtNet (82.36%) and WMF (67.52%). This highlights the effectiveness of the proposed model in leveraging SAR data for wetland classification tasks. In comparison, CvT and CoAtNet models demonstrated moderate performance, with CoAtNet achieving relatively higher accuracy among these models.  
For S2 data, the proposed model achieved 93.73% OA, which was significantly higher than the OAs of CoAtNet (84.42%) and CvT (82.23%). WMF showed competitive results with other hybrid models achieving an OA of 83.89%, still falling behind the proposed model. These results indicate the robustness of the proposed model in effectively utilizing multispectral data for improved classification accuracy.  
The combination of S1 and S2 data (S1S2) yielded the highest overall performance for all models. These improvements can be attributed primarily to the distinct, complementary information provided by SAR data. While SAR data alone showed inferior capability compared to optical data, it serves as a valuable source of information in the classification task by playing a key role in discriminating categories with similar optical features. For example, despite the close optical feature distribution of shrubland and grassland categories, the combined dataset led to notable enhancements for the separation of these classes, attaining 3-4% improvements for the proposed model. This could be attributed to the greater capability of SAR data compared to optical data in capturing different soil and canopy moisture levels within shrulands and grasslands, thus enabling a better separation of these two classes [61; 62]. The complementary utilization of multi-source satellite data not  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**S1**} & \multicolumn{6}{c}{**S2**} & \multicolumn{6}{c}{**S1S2**} \\ \cline{2-13}
**Class** & **CvT** & **CoAtNet** & **WMF**  
only leverages the strengths of each but also compensates for their individual limitations, thereby highlighting the importance of their combined application in remote sensing.  
The results indicated that the proposed Wet-ConViT model achieved the highest performance among all tested models, even when using single-source data. For example, using only S2 data, the proposed model achieved 93.73% OA, whereas the second-best model, CoAtNet, achieved 84.42% OA. This demonstrates the superior capability of the proposed Wet-ConViT model in effectively utilizing multi-source satellite data for enhanced classification accuracy.