## 3 Results
### Comparison of Sentinel-1 and Sentinel-2 Combinations
#### 3.2.2 Qualitative Evaluation  
Figures 13 and 14 display the predicted flood inundation maps from each combination of input data in clear and cloud-covered conditions. From Figure 13 it is evident that in clear conditions Sentinel-1 provided a coarser prediction of the flooded area. However, in the Sentinel-2 image, the area in the upper left was falsely identified as a flooded area. In the majority of the combinations, the flooded area prediction was slightly coarser than in the Sentinel-2 image, and the upper left area was falsely identified as flooded. There are, however, some exceptional cases. For instance, from the combinations of VV, VH and RGB; VV, VH, RGB and SWIR; VV, VH and NDVI; and VV, VH and MNDWI, the area in the upper left identified as flooded was significantly reduced. Additionally, from the combinations of VV, VH, RGB and SWIR; and VV, VH, RGB, NIR and SWIR, the predicted maps were at least as finely detailed as the map generated from the Sentinel-2 image. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline Sentinel-1 & 0.9432 & 0.2237 & 0.7176 & 0.5862 \\ Sentinel-2 & 0.9718 & 0.1143 & 0.8054 & 0.7031 \\ \hline VV + VH + Sentinel-2 & 0.9632 & 0.2442 & 0.7582 & 0.6425 \\ VV + VH + NIR & 0.9550 & 0.2980 & 0.7155 & 0.6125 \\ VV + VH + SWIR & 0.9620 & 0.2808 & 0.7509 & 0.6310 \\ VV + VH + NIR + SWIR & 0.9588 & 0.2793 & 0.7495 & 0.6385 \\ VV + VH + RGB & 0.9353 & 0.3849 & 0.6011 & 0.4769 \\ VV + VH + RGB + NIR & 0.9513 & 0.1821 & 0.7584 & 0.6318 \\ VV + VH + RGB + SWIR & 0.9636 & 0.2180 & 0.7854 & 0.6760 \\ VV + VH + RGB + NIR + SWIR & 0.9641 & 0.1833 & 0.8019 & 0.7053 \\ VV + VH + NDVI & 0.9464 & 0.2612 & 0.6983 & 0.5734 \\ VV + VH + MNDWI & 0.9464 & 0.3270 & 0.6950 & 0.5638 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Testing scores for each combination. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline VV + VH + RGB + NIR + SWIR & 0.9684 & 0.0764 & 0.8232 & 0.7064 \\ VV + VH + NDVI & 0.9509 & 0.1275 & 0.7444 & 0.6068 \\ VV + VH + MNDWI & 0.9536 & 0.1224 & 0.7585 & 0.6215 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline Sentinel-1 (VV + VH) & 0.9436 & 0.1724 & 0.7376 & 0.5908 \\ Sentinel-2 (All Bands) & 0.9763 & 0.0906 & 0.8238 & 0.7241 \\ \hline VV + VH + Sentinel-2 & 0.9662 & 0.1019 & 0.7673 & 0.6620 \\ VV + VH + NIR & 0.9610 & 0.1776 & 0.7510 & 0.6417 \\ VV + VH + SWIR & 0.9537 & 0.1629 & 0.6581 & 0.5963 \\ VV + VH + NIR + SWIR & 0.9564 & 0.118  
Figure 14: Sentinel-1 image, Sentinel-2 image, ground truth, and predicted flood inundation maps from each input-data combination for an example image in cloud-covered conditions. Figure 13: Sentinel-1 image, Sentinel-2 image, ground truth, and predicted flood inundation maps from each input-data combination for an example image in clear conditions. From Figure 14, the weakness of Sentinel-2 in cloud-covered conditions can clearly be observed. Here the Sentinel-1 image significantly outperformed any combination that includes Sentinel-2 information. This issue was much more pronounced for the combinations that included the RGB bands, especially where no supplementary information from NIR and SWIR was provided. The combination of Sentinel-1 with SWIR performed better than any other combination, suggesting that this band is able to penetrate cloud more effectively than the RGB or NIR bands. The combinations with the spectral indices NDVI and MNDWI were also able to produce comparatively accurate maps, suggesting that the extraction of these indices also aids in alleviating the problem of cloud penetration. Figures 15 and 16 depict the heatmap explanations for the above flood inundation maps, where the magnitude of importance the model placed on each pixel can be observed.