# Potential and Limitations of Open Satellite Data for Flood Mapping
## 4 Results
### Semi-Automatic Mapping of the Flooded Area
#### 4.2.1 SAR Data Analysis  
In this section, we show the results of the SAR data-based analysis performed by using SAR amplitude classifications (SAR_AC) (Figure 5) and \(\Delta\sigma^{\text{o}}\) backscattering variation in Figure 6:  
* **SAR_AC**. In the case of the Ebro River flood, the classification of the Sentinel-1 image acquired on 5 March 2015, i.e., two-three days after the flood peak (when most of the areas remain inundated according to MODIS data), allowed good detection of the flooded areas (Figure 5A). It is also possible to observe false positives, i.e., areas wrongly classified as flooded, most likely related to topographic artefacts or flat surfaces (e.g., airport ramp). Figure 5B shows the SAR_AC based on the Sentinel-1 image acquired on 28 November 2016 over the areas inundated by the Tanaro and Bormida Rivers near Alessandria. The flooded areas were detectable more easily for the Tanaro River, where the soil remained submerged two days after the flood peak, than for the Bormida River. The analysis performed over the area flooded by the Po River did not show significant results. In the Arahal area, the flooded area was partially detected with images acquired in the early morning of 29 November 2017, a few hours before the flood peak (Figure 5C). * \(\Delta\sigma^{\text{o}}\) backscattering variation-based analysis. Compared with the SAR_AR-based analysis, such an approach allows achieving more-accurate results by exploiting calibrated Sentinel-1 data and image log ratios between the post- and pre-flood images (i.e., \(\Delta\sigma^{\text{o}}=\text{log}\ \sigma^{\text{o}}\) post-log \(\sigma^{\text{o}}\) pre). In addition, the comparison with pre-flood data allowed discriminating flooded areas from permanent water bodies. For most of the cases, we used VH polarization and \(\Delta\sigma^{\text{o}}>0.6\) as a threshold. In the case of Zaragoza (Figure 6A), we used the post-flood image of 5 March 2015 (acquired three days after the discharge peak), and the pre-flood image of 9 February 2015. The flooded area was partially detected with the semi-automatic methodology. However, this result allowed mapping of the entire inundated area with a visual interpretation. The results are also in agreement with MODIS data acquired on the same date. The comparison with a water-depth model showed that the areas remaining classified as flooded on the Sentinel-1 image approximately correspond to regions in which the water depth during the flood peak was higher than 1.5 m. In the case of Alessandria and Turin, we used the post-flood image of 28 November 2016 and the pre-flood image of 22 November 2016. Results show residual inundated areas. In particular, as already mentioned, the flooding pattern remains visible in the case of Tanaro River but not for the southern region of Turin, in which the remaining flooded areas are too small to be detected with Sentinel-1 data. In the case of Arahal (Figure 6D), we used VV polarization, which showed better performance concerning VH. We compared pre-flood (23 November 2017) with near co-flood images of 29 November 2017. In this case, we found that the threshold of 0.3 was the best value to detect flooded areas. The Sentinel-1 image was acquired in the early morning of 29 November 2017, some hours before the flood peak; most likely, the inundated areas are underestimated. Figure 5: Detection of flooded areas based on synthetic aperture radar (SAR) amplitude classification of Sentinel-1 images. (**A**) VH Image acquired on 5 March 2015 over Zaragoza; (**B**) VH Image acquired on 28 November 2016 over the Alessandria area; (**C**) VV image acquired on 29 November 2017 over the Arahal area. Figure 6: Detection of flooded areas based on the difference of SAR backscattering (\(\Delta\)\(\alpha^{\rm o}\)) with Sentinel-1 data: 5 March 2015 Ebro flood (**A**); 28 November 2016 Po basin flood in South Turin area (**B**) and Alessandria area (**C**); 29 November 2017 Arahal flood (**D**).