The Copernicus EMS Validation service as a vector for improving the emergency mapping based on Sentinel data

The Copernicus Emergency Management Service (CEMS) is coordinated by the European Commission and “provides all actors involved in the management of natural disasters, man-made emergency situations, and humanitarian crises with timely and accurate geo-spatial information derived from satellite remote sensing and complemented by available in situ or open data sources”. It includes two components, Early Warning and Monitoring and Mapping. The latter provides on demand geo-spatial information derived from satellite imagery during all phases of the disaster management cycle. It includes 3 systems, Rapid Mapping (RM), Risk and Recovery Mapping (RRM), and a Validation Service. RM provides geospatial information immediately after a disaster to assess its impact; RRM in the prevention, preparation and reconstruction phases; and the Validation Service is in charge of validating and verifying the products generated by both, and of collecting and analyzing users’ feedback. The wide spectrum of activities framed in the Validation Service has allowed it to become a vector to improve the Mapping component through the testing of new methodologies, data input type, or approach for the creation of emergency cartography in the frame of the CEMS. The present paper introduces the main investigation lines based on Sentinel-1 and 2 for flood and fire monitoring that could be implemented in the CEMS services taking into consideration the characteristics of the Mapping component in terms of products to create and time constraints. The applicability of Sentinel-1 for flood monitoring based on the backscattering, the MultiTemporal Coherence (MTC), and dual polarization; and for burnt area delineation based on MTC was studied, while Sentinel-2 was used for burnt area delineation based on vegetation indices. Results indicate that proposed methodologies might be appropriate for the creation of crisis information products in large areas, due to the relative easy and fast implementation compared to classic photo interpretation, although further applicability analyses should be carried out.