# Potential and Limitations of Open Satellite Data for Flood Mapping
## 4 Results
### Flood, Mapping Refinement Using Water Depth DEM and Ancillary Data  
As already mentioned, the second step for the flood map creation is the refinement and improvement based on supporting data (i.e., DEM and ancillary data), the result of which is particularly useful when we have flood maps based only on post-flood images. The final result was the production of flooded area manually drawn polygons. Figure 10 shows an example of the integration of remote  
Figure 9: The 2016 Po basin flood, semi-automatic detection of the inundated areas. NDVI\({}_{\text{var}}\) based on Sentinel-2 data for Alessandria (**A**) 8 December 2016 and South Turin (**C**) 1 December 2017; NDVI\({}_{\text{var}}\) based on MODIS-Aqua (30 November 2016) data for Alessandria (**D**) and South Turin (**E**); VIS\({}_{\text{var}}\) based on Sentinel-2 data for Alessandria area 8 December 2016 (**B**).  
sensing data, DEM modelling and ancillary data for the mapping of the areas flooded by the Tanaro River near Alessandria. The map in Figure 10A represents the inundation area derived from the semi-automatic classification of remote sensing images based, in this case, on VIS\({}_{\text{var}}\) of Sentinel-2 data.  
Figure 10B shows the inundation maps based on a water depth (WD) model in which the pixel is classified as inundated when WD > 0 m,  
Figure 10C represents the synthesis of the two presented maps, in which a pixel is classified as inundated if flooded in both satellite data and the WD model. The WD model allowed reducing pixels incorrectly classified as flooded (false positive). Furthermore, the geolocated photos (available on websites or from volunteers) allowed us to have some ground truth points.  
Figure 10D represents the final version of the flooded area that was manually drawn considering the remote sensing data, the WD model, and the ancillary data.  
We also used the same methods to create the final maps of the area flooded by the Po and Ebro Rivers. The refinement step was handy for the case of the Po River, whereas for the Ebro, for which the high-resolution co-flood map is available, the support data do not provide significant improvement.  
For the Arahal case study, in which the flooded area was more difficult to detect, we made a more in-depth effort to combine satellite and support data. Figure 11 shows the steps used to create the refined flood map.  
Figure 10: Example of semi-automatic flooded area corrected with digital elevation model (DEM) and ancillary data. Alessandria area: (**A**) Flood maps based on VIS\({}_{\text{var}}\); (**B**) flooded area based on water depth model (WD); (**C**) WD-Corrected remote sensing inundation map; (**D**) refined flooded area map. Ancillary data: (1) Aerial oblique photo of a flooded area in the morning of 26 November 2016 near Alessandria (credit [PERSON]); (2) ground photo of a building showing the water level trace (approximately 3 m) on the facade.  
We initially created a semi-automatic map of the flooded area (Figure 11A) by crossing all of the available remote sensing data (i.e., Landsat-8, Sentinel-2 and Sentinel-1). Then, we created a WD model map for the two small streams affected by the flood, around the intersection with the Seville-Malaga railway (Figure 11B). The combination of such a model with remote sensing data allowed us to produce the map of the estimated flooded area (Figure 11C). To assess the reliability of the flood map, we exploited geolocated photos and news about the flood. Available images allowed an evaluation of the reliability of the inundation map locally, e.g., it was possible to locate the effect of embankment erosion on a railway line (photo 3).