# Potential and Limitations of Open Satellite Data for Flood Mapping
## 4 Results
### Flood Map Validation and Statistics  
The last step of the presented methodology consists of validating the automatic flood maps. We applied the validation to the flood maps of Ebro valley and the Po basin using the official flood maps of the Ebro River authority (CHE) and ARPA Piemonte. The validation process, as described earlier, is based on the definition of a raster validation map. In these raster maps, each pixel can assume four values: (0) True negative (TN) case represented in brown; (1) false positive (FP) case represented in cyan; (2) false negative (FN) case represented in yellow; and (3) true positive (TP) case represented in blue. To make a more homogeneous comparison, the area covered by a permanent water body was not calculated in FR or NFR. In Figure 12, examples of validation maps for semi-automatic flooded area maps are reported. For the area of Zaragoza (Figure 12A), the MNDWI\({}_{\text{var}}\) derived from co-flood Landsat-8 images allowed  
Figure 11: Arahal flood November 2017. (**A**) Semi-automatic map of the flooded area based on a combination of NDMI\({}_{\text{var}}\) made with Landsat-8, MDNWI\({}_{\text{var}}\) made with Sentinel-2, and \(\Delta\sigma\) Sentinel-1 data; (**B**) water depth model based on DEM; (**C**) semi-automatic flooded area corrected with WD model and our manual interpretation. (1–5) Ground truth geolocated photos found on the web [91]. Photo 3 shows the railway affected by the flood, in which a train derailed. correctly classifying both flooded and not-flooded areas (>90%); the \(\Delta\sigma^{\rm o}\) Sentinel-1-based analysis (Figure 13B) also shows satisfactory performance (FR 50%; NFR > 90%). For the area of Alessandria, the NDVIvar computed from the MODIS-Terra data shows better results (FR 70%, Figure 12C) compared with the \(\Delta\sigma^{\rm o}\)-based analysis (FR of 30%, Figure 12D). In the area at the south of Turin, the MNDWIvar (Figure 13E) made with a post-flood image (1 December 2016) from Sentinel-2 data detected approximately 45% of the flooded area. We also applied the validation to the final refined maps of flooded areas. The results show that our maps mostly coincide (FR > 90%) with official maps of ARPA Piemonte and the Ebro River authority in the case of Zaragoza and Alessandria, but more differences can be found in the maps flooded by the Po (FR 70%). Figure 13 shows a correlation between the river levels for the Ebro (Figure 13A), Tanaro (Figure 13B) and Po (Figure 13C) with the flood ratio (FR) for semi-automatic flood mapping based on different methodologies and satellites. The results give an idea of the various parameters that influence flooded area detection. In general, the quality of semi-automatic detection of flooded areas decreases with the time after the peak of the flood for all of the cases considered. The SAR data show a much more rapid decrease in the capacity to detect flooded areas. Figure 12: Example of flooded area validation raster. Ebro flood 2015: (**A**) 3 March 2015, Landsat-8 MNDWIvar; (**B**) 5 March 2015, Sentinel-1 (\(\Delta\sigma^{\rm o}\)). Alessandria area: (**C**) 30 November 2016, NDVIvar using MODIS-Terra; (**D**) 28 November 2016, Sentinel-1 (\(\Delta\sigma^{\rm o}\)). Turin area: (**E**) 1 December 2016, MNDWIvar using Sentinel-2. For each area, the primary results are as follows:  
* The area flooded by the Ebro River near Zaragoza in 2015 (Figure 13A). In this case, co-flood data at low resolution (MODIS-Terra/Aqua and Proba-V) and medium-high resolution (Landsat-8) allowed mapping the entire flooded area (FR \(>\) 95%) with few false positive values. In this case, spatial resolution has little influence on the accuracy of flooded area detection. The \(\Delta\)\(\sigma\)\({}^{\rm o}\) map made with Sentinel-1 data acquired two days after the maximum flood shows that approximately 50% of the area is detectable with an NFR (\(\approx\)98%). With the map based on Sentinel-1 data, it was nonetheless possible to detect a clear pattern that identifies entire flooded areas.