# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 5 Discussion
The same applies considering the F1-score computed as the average of the F1-scores of the two water classes only (0.85-0.87). As for the errors of omission and commission, even better results were obtained in the literature (e.g., [23; 31; 54]), but it must be considered that in this study, 3 classes are considered, while in previous studies, only non-water and floodwater were generally distinguished. It has been found that the major source of disagreement between AUTOWADE-derived maps and reference maps is the misclassification between permanent water and floodwater. On the one hand, this happens because CEMS does not use a pre-flood image, while AUTOWADE labels as permanent water the pixels classified as open water in the flood image and in the pre-flood one. On the other hand, the misclassification is also due to differences between the hydrography data included in the CEMS products and the reference water mask. This has nothing to do with the accuracy of the AUTOWADE-derived maps. For what concerns permanent water, the high value of \(\epsilon_{\text{om}}\) obtained for the EMSR492 case study (30.5%, Table 6) is mostly due to a small water course whose delineation with S1 was partially hampered by the presence of many mixed land-water pixels (data with higher spatial resolution would have been necessary for a more precise delineation). The high value of \(\epsilon_{\text{om}}\) obtained for the EMSR501 case study (32%, Table 6) is mostly due to a water body (Drin River) whose detection was missed because of unexpected high values of backscattering likely due to the presence of wind blowing above the water surface. The extent of the objects labelled as open floodwater is the major cause of misclassification between open floodwater and non-water. An example of this kind of misclassification is shown in Figure 15 for the Springfield Clonara test site (EMSR429). For the class of open floodwater, \(\epsilon_{\text{comm}}\) and \(\epsilon_{\text{om}}\) are equal to about 20%, in this case. Looking at Figure 15, it can be seen that, although there is a substantial agreement between CEMS and AUTOWADE classifications, the size of the floodwater objects is slightly different. This is due to the differences in the flood detection methods; in particular, the BFE approach uses RG and uncertainties in the determination of the tolerance of the RG may imply uncertainties in the extent of the floodwater objects. The red pixels in the lower left part of Figure 15 correspond to omission errors due to small differences between the \(\sigma^{0}\) values measured by S1 under non-flooded and flooded conditions, considering that the BFE approach applies change detection. Note that for this test case commission and omission errors are of the same order of magnitude (around 20%); thus, the difference between the total flooded surface estimated by CEMS and that estimated by AUTOWADE is quite small (7 ha, Table 8). Thanks to the availability of some aerial photos, it has been verified that the quite high value of \(\epsilon_{\text{comm}}\) obtained for the EMSR496 case study (24.4%, class of open floodwater), is due to the presence of ponding water not detected by CEMS. This can be inferred by looking at Figure 16 that compares the maps with two photos of the area affected by the flood. In the areas highlighted by the ellipses, CEMS seems to underestimate water, while in the AUTOWADE-derived map, the underestimation is significantly reduced (especially in the magenta ellipse). The photos were taken on 26 January 2021 at 11:00 UTC, i.e., about 17 h before the S1 acquisition, but, reportedly, water did not recede. Hence, the disagreement with respect to the reference data in terms of ratio between the extent of the flooded area and the size of the AOI highlighted above for this case study (2.2%) is probably not due to an overestimation of the flooded area done by AUTOWADE. It was not possible to robustly validate the method designed to map flooded vegetation, because CEMS products do not map this target when they are derived from SAR data.