## 4 Discussion
In clear conditions, the flood inundation maps generated from Sentinel-2 images followed expectations of exhibiting greater detail and accuracy than those from Sentinel-1 images; however, in cloud-covered conditions, the Sentinel-2 images had a clear weakness. This contrast means that in a real-world flood inundation mapping system, it may depend on the weather conditions as to which image type is more appropriate, so it is vital that a model is able to adapt well to various types of images. The heatmap explanations reveal that the proposed model places a more consistent and higher magnitude of importance on the most relevant pixels than any of the other assessed models. This demonstrates that, not only is the model able to make more accurate predictions, but it is also learning more effectively than any of the state-of-the-art models, so can be more easily trusted to make correct decisions in the future. According to overall model performance, fusion of Sentinel-1 with Sentinel-2 bands and indices is largely unable to match the performance using Sentinel-2 alone, with the exception of the fusion of VV, VH, RGB, NIR and SWIR, which achieves a marginally higher IOU value in testing. The fusion results are, however, generally superior to using Sentinel-1 alone, except where only the RGB bands from Sentinel-2 and the extracted spectral indices NDVI and MNDWI are used. Although this seems to indicate that Sentinel-2 should be the chosen input source for practical applications, the visualizations of the generated flood inundation maps and the heatmap explanations underscore the need for further consideration. In clear conditions, the accuracy of the flood inundation maps largely follows the expectations given by the overall performance metrics. The magnitude of the heatmaps also exemplifies that the input data that provides better performance also makes the most appropriate decisions, as the magnitude of importance demonstrated in these instances is much more consistent, being higher in the relevant areas and lower in the falsely identified areas. However, in cloud-covered conditions, the results are much more varied. It is evident that Sentinel-1 provides the best map in these cases, with the most appropriate magnitude of importance demonstrated by the heatmaps; however, there are other instances where the accuracy of the inundation maps is still reasonable, but the overall performance of the model is improved over Sentinel-1 alone, in particular the combination of VV, VH and SWIR. In a practical setting, there may be limitations that make it unreasonable to change the input-data type depending on weather conditions, so utilization of this combination may provide a suitable compromise providing better overall performance than Sentinel-1, while still maintaining a reasonable ability to penetrate through cloud. Previous work involving the fusion of Sentinel-1 and Sentinel-2 images has often focused on using RGB and NIR as the Sentinel-2 bands [49; 50; 51; 52; 53] or extracting spectral indices. However, the findings of this study emphasize that the inclusion of SWIR has a more positive impact on the resulting maps than NIR. SWIR, with its longer wavelengths, is better suited to penetrating clouds, which primarily absorb shorter wavelengths. Additionally, it is less affected by atmospheric absorption and exhibits clearer contrast between water bodies and surrounding land, enabling more effective delineation of flooded areas. Deep learning models are highly capable of directly extracting relevant features from raw input data. Therefore, the extraction of spectral indices can introduce redundant features, potentially diminishing the wealth of spectral information available for the model to capture.