# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 4 Results and Discussion
### Calibration Threshold Technique Results
The comparison of the histogram of the water mask obtained and the polarization VH histogram is shown in Figure 11. A significant spatial linear correlation between both histograms is shown, something that does not happen with VV polarization [56]. After studying the results, we conclude that the most representative values of the presence of the water range mostly between \(-21\) and \(-24\) dB, so \(-21\) dB was considered. The threshold taken was therefore set to \(-21\) dB. With the Raster-Band Maths tool of the SNAP program, a binary layer was generated in which, on the one hand, below \(-21\) dB values are represented, representative of the water and flooded areas, and on the other, those above it that would show the dry zones. Figure 12 shows the areas with the presence of water that are observed in part of the study area for a radar image in which no filter was used and another for which the process of Lee \(7\times 7\) filtering was used. The unfiltered image has a large number of random pixels distributed by the image that give \"false positives\". All of them are points outside the flood area and the course of the river that have backscatter intensity values below \(-21\) dB. On the other hand, the use of a filter minimizes this effect, favoring less overrepresentation errors. Figure 11: (a) Characteristic histogram of zone 1: Novillas–Pradilla de Ebro with VH polarization. (b) Representative histogram only for the water zones. Figure 12: Representation of a specific part of zone 3: Zaragoza–El Burgo de Ebro (the same represented in Figure 5) of the areas that were captured as water in the unfiltered radar image and another with the Lee \(7\times 7\) filter. The indicative layers of water presence, for the same specific area, were also generated for the other filters and different window sizes that were considered in the study (Refined Lee, Lee Sigma, and Gamma Map) and through which numerical data of the surfaces that show the presence of a water sheet were extracted (Table 4). It is quite significant that the use of one filter or another provides notable variations in the areas represented as water. Obviously, the unfiltered image is the one that generates more surface compared to any of the others that were filtered. It is quite significant that the use of one filter or another provides notable variations in the areas represented as water. Obviously, the unfiltered image is the one that generates more surface compared to any of the others that were filtered. In comparison, the unfiltered image could provide even more than twice the area flooded than, for example, the image filtered with Lee \(7\times 7\). Therefore, the application of filtering is necessary not only for speckle reduction but to avoid errors when making quantitative calculations with images. The choice of the Lee \(7\times 7\) filter, which generates the least area, minimizes the presence of individual pixels outside the flood area and thus prevents overrepresentation in these areas. The choice of the Lee \(7\times 7\) filter was verified as being the most appropriate for the results of the present study. The process represented on the PNOA results in the map of zone 1 is shown in Figure 13. It should be taken into account that factors such as the characteristics of the terrain or the angle of incidence, can produce areas of shadow (see this effect in Figure 13a in the upper right where there is a mountainous area with higher topography). These have backscatter values similar to those with surfaces with the presence of water, causing overrepresentation of flooded areas [64]. Therefore, in order to avoid delimitation errors, the threshold technique could be combined with photointerpretation to add areas not identified by default or eliminate areas identified in excess, thus improving the quality of the results obtained [47]. It should also be considered that this technique does not discriminate between bodies of surface water (lakes, lagoons, reservoirs, wetlands) flooded areas.