## 5 Conclusions  
This study has presented a comprehensive analysis of flood inundation mapping using data from the Sentinel-1 and Sentinel-2 satellites. The proposed model has demonstrated remarkable versatility by consistently outperforming state-of-the-art models when provided with both Sentinel-1 and Sentinel-2 data, affirming its suitability for practical applications. This study has highlighted the importance of considering various aspects in flood inundation mapping. While quantitative metrics favor Sentinel-2 images, qualitative analysis reveals that the choice of input data is highly dependent on weather conditions. Sentinel-1 excels in cloud-covered conditions. When used in combination with SWIR, it is able to retain reasonable cloud penetration ability, while improving its overall accuracy. On the other hand, Sentinel-2 images outperform Sentinel-1 in clear conditions, providing detailed and accurate flood inundation maps, where the model places a higher magnitude of importance in the most relevant regions of the image. These nuances underscore the significance of a model's ability to adapt to different data types and environmental conditions. The extraction of spectral indices, such as NDVI and MNDWI, appears to hinder model performance. Deep learning models excel at feature extraction from raw data, rendering spectral indices redundant and potentially limiting the range of spectral information available for effective modeling. In practical terms, the results from this study hold significant implications for flood management and disaster response. The ability to accurately predict and map flood inundation in various conditions is essential for timely decision-making and resource allocation. The model proposed in this study is able to generate more accurate flood inundation maps than the state-of-the-art model, and, importantly, its ability to make appropriate decisions has been demonstrated, ensuring that it can be trusted in its application, which is crucial given the direct impact on human lives and infrastructure. For future work, investigation of the performance of the proposed model when provided with a larger set of input images should be conducted, as larger datasets allow deep learning models to learn complex feature representations more effectively, typically resulting in superior performance, as well as reducing the likelihood of overfitting. Additionally, different types of input data should be further explored. Sentinel-1 and Sentinel-2 are freely available, so represent a clear choice for initial study; however, satellites such as Radarsat-2 provide higher resolution SAR data, which may improve the performance of the model. In optical imaging, hyperspectral images contain a much richer spectral profile, which may aid in improved delineation of floodeded areas, so determining the model's ability to adapt to these images is an important avenue for future research. The incorporation of additional data sources such as Digital Elevation Models (DEM) and Light Detection and Ranging (LiDAR) would also provide more detailed information about the terrain and topography of the area, so will enable the model to generate flood inundation maps with more precision. This detailed information can also enable more detailed and actionable insights through the use of XAI, by providing a deeper understanding of how specific terrain and topographic features influence flood inundation. While the performance of the proposed model is relatively strong, it could be further enhanced through more rigorous hyperparameter tuning as well as postprocessing of the maps generated by the proposed model, with the use of conditional random fields, which can refine the output of a segmentation model. Finally, it was found that the speckle noise of the Sentinel-1 images had a significant negative impact on model performance where these images were employed, so incorporating deep learning-based methods to reduce the noise while preserving object boundaries is another promising area of future research.