# Potential and Limitations of Open Satellite Data for Flood Mapping
## 6 Conclusions
For the area of Alessandria, quasi co-flood Proba-V data permitted achieving excellent results, with a FR of nearly 100% for the area flooded by the Tanaro. The analysis that relied on medium-high resolution Landsat-8 and Sentinel-2 data shows the same flood ratio performance as the medium-low resolution satellites (FR \(>\) 90% for co-flood Landsat images in Zaragoza areas) but with a spatial precision several times greater. These data indeed also allowed the detection of small, flooded areas (up to \(\approx\)500 m\({}^{2}\)). In particular, the high-resolution was very useful for the Arahal flash-flood case study. In this case, the availability and the combination of high-resolution Sentinel-2, Sentinel-1 and Landsat-8 data joined with ancillary data and geomorphological interpretation was fundamental to producing a reliable flood map. The study points out some drawbacks of flood mapping based on the use of SAR and multispectral satellite data. In the latter case, the main limitation is represented by cloud cover affecting co-flood multispectral acquisitions. The presence of clouds has indeed limited the flooded areas detection in the case of the Po flood, while has not affected the Ebro study because the inundation occurred several days after the rainfall event. The presence of dense urban areas and forests affects both SAR and multispectral based flood mapping and requires a more-complex data processing which is not straightforward to accomplish with a user-friendly approach. High spatial resolution is also a key factor when mapping floods in dense urban areas, and it is one of the limitations of the free of charge satellite data approach. Based on our experience, on-demand high costs, high resolution data and field surveys are often necessary [57]. However, despite such drawbacks the integration of freely available satellite data with ancillary data allowed us to map about 450 km\({}^{2}\) of inundated areas with high accuracy for all studied areas. Our methodology proves to be a good trade-off between emergency real-time automatic flood mapping and the traditional flood mapping performed with on-demand data. Real-time automatic flood mapping (e.g., Copernicus EMS) are usually fast, but not very accurate. In contrast, traditional flood mapping performed with on-demand data provides very accurate results, but it is a more resource-intensive process. Furthermore, we propose a user-friendly approach for satellite data processing that can also be exploited by users with no remote sensing expertise, contributing to hydrological studies, urban planning or flood hazard/damage assessment. In such a context, having a network of operational satellites providing free- and open-access data with continuity is of great importance for flood mapping and hazard/risk management in floodplain areas and disadvantaged regions worldwide. The following are available online at [[http://www.mdpi.com/2072-4292/10/11/1673/s1](http://www.mdpi.com/2072-4292/10/11/1673/s1)]([http://www.mdpi.com/2072-4292/10/11/1673/s1](http://www.mdpi.com/2072-4292/10/11/1673/s1)). The authors wish to acknowledge the volunteers that provided ground photos of the 2016 flood. We thank the three anonymous referees that revised the paper. The authors declare no conflict of interest.