# Potential and Limitations of Open Satellite Data for Flood Mapping
## 6 Conclusions  
In this study, we presented a methodology for low-cost and user-friendly flood mapping based on free satellite data jointly exploited with free ancillary data. In particular, we used data from Sentinel-1, Sentinel-2, Landsat-8, Proba-V and MODIS Terra/Aqua satellites and processed them with open-source software based-methods easily carrying out also by non-remote sensing expert community. We tested our methodology over three areas recently hit by floods: (i) The Ebro valley near Zaragoza, Spain, flooded in spring 2015; (ii) two areas near Alessandria and Turin (Piemonte region, NW Italy)inundated by Po, Tanaro, and their tributaries in November 2016; and (iii) a small area around the Arahal village, southern Spain, affected by a flash flood in November 2017. The proposed flood mapping strategy is composed of progressive steps. Firstly, the detection of flooded area is performed, through semi-automatic extraction or by visual interpretation of satellite images. The flood map is later manually refined with the help of ancillary data such as digital elevation models (DEMs), water depth (WD) models or ground photos and web-based information. The final outcome is an accurate and morphologically based flood map in vector format. In the presented case studies, we validate the methodology results with available flooded area maps provided by local authorities. For the case of the Ebro and Po basin flood, we considered a performance assessment of the different automatic maps, and we calculated the flood ratio (FR), which is the percentage of the flooded area detected by satellites with respect to official maps. The study points out that the capability of spaceborne sensors to map a flood event depends upon several factors: (a) The time of the satellite pass with respect to the time of the flood peak, that is the most important factor; (b) the spatial resolution of satellite; (c) the sensors type (e.g., SAR or multispectral); (d) the sky conditions; (e) the land-use and morphology of the flooded area; (f) the type of data processing. The results show that SAR-based flood mapping is more efficient when co-flood acquisitions are available, allowing the extraction of information on areas fully covered by water also in cloudy or night conditions. However, the only SAR system currently providing free and open data is Sentinel-1; for this reason, the availability of co-flood data is limited. In our study, a co-flood Sentinel-1 image was available only for the 2015 Ebro valley flood, allowing a satisfactory detection of the flooded area (\(\approx\)50% FR). In the other study areas, only post-flood SAR data were available; these data, although acquired a few hours or days after the water withdrawal, were not suitable for flood mapping: FR showed values of about 20% for the area flooded by Tanaro to less than 5% for the area inundated by the Po River. In the case of multispectral data-based mapping instead, the analysis shows that post-flood acquisitions can be profitably exploited, allowing to detect changes inside the flooded areas within a few weeks after water withdrawal. The results show that the FR decreases with the time elapsed from the flood peak but slower respect to SAR: For instance, the computed \(\mathrm{MNDWI_{var}}\) decreases from values \(>\)90% with co-flood imagery to less than 50% if the available imagery is acquired two weeks later in all the flooded areas. It is worth noting that the local conditions, such as the depositions of silt sediments, contribute to mapping the flooded areas for a more extended period: e.g., \(\mathrm{VI_{SVar}}\) showed a FR\(\approx\) 70% in the area flooded by the Tanaro using Sentinel-2 data acquired 15 days after the floods. The MODIS and Proba-V satellite sensors, with a daily revisit time, increase the probability of acquiring co-flood data and allow achievement of very good results despite the medium-low spatial resolution. In the Zaragoza area, the MODIS and Proba-V co-flood images allowed a very accurate mapping (FR \(>\)90%).