Floods automatic rapid mapping through Sentinel-2 MSI multitemporal data

Floods are widespread natural disasters on Earth affecting the planet with increasing frequency and intensity. Climate changes are responsible of the increasing number of heavy and persistent rains generating these destructive events often resulting in fatalities, injuries, and extensive infrastructural damages. A near real time monitoring system able to provide timely and accurate information about location and extent of the flooded areas is crucial for the authorities to implement the right mitigation actions. Currently, the Copernicus Emergency Management Service (CEMS) supports at European level the crisis management activities in the immediate aftermath of a flood, exploiting multi-source satellite data to provide flood delineation with a release time ranging from 7 to 48 hours (from the satellite acquisition). Map characterization and relative information are retrieved through semi-automatic or manual methodologies which do not allow for a complete automation of the analysis crucial to speed up the procedure and shorten the release time.

In this study, carried out in the framework of the MITIGO project (funded by MIUR PON R&I 2014-2020 Program), results coming from a multi-temporal optical satellite technique able to quick detect and accurately map flooded areas will be presented. This technique, namely RST-Flood, exploits the statistical characterization of the satellite observed signal to retrieve accurate background information useful to promptly and automatically identify ground changes directly linked to events occurrence. RST-Flood has already been successfully implemented with mid-low spatial resolution (from 1000 to 375m) optical satellite data sensors (i.e., Advanced Very High Resolution Radiometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite), and here is for the first time exported to Sentinel-2 Multi Spectral Instrument (MSI) data at mid-high spatial resolution (20m) to study recent floods events. The achieved results demonstrated the easy implementation of RST-Flood to different sensors and geographic areas and its capability in providing fast (processing time less than 15 min from data availability) and robust mapping of flooded areas. Furthermore, its design developed to work in the Google Earth Engine (GEE) environment makes it suitable for global scale implementation without altering its performance.