Satellite remote sensing is a powerful tool to map flooded areas. In recent years,
the availability of free satellite data significantly increased in terms of type and frequency, allowing
the production of flood maps at low cost around the world. In this work, we propose a
semi-automatic method for flood mapping, based only on free satellite images and open-source
software. The proposed methods are suitable to be applied by the community involved in flood hazard
management, not necessarily experts in remote sensing processing. As case studies, we selected three
flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS,
Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were
used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference
Water Index, SAR backscattering variation, and supervised classification). Then, we improved and
manually refined the automatic mapping using free ancillary data such as the digital elevation
model-based water depth model and available ground truth data. We calculated flood detection
performance (flood ratio) for the different datasets by comparing with flood maps made by official
river authorities. The results show that it is necessary to consider different factors when selecting
the best satellite data. Among these factors, the time of the satellite pass with respect to the flood
peak is the most important. With co-flood multispectral images, more than 90% of the flooded area
was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood
ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy),
but it remained useful to map the inundated pattern. The SAR could detect flooding only at the
co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River
inundation. Another result of the research was the creation of geomorphology-based inundation
maps that matched up to 95% with official flood maps