Continuous flood monitoring using on-demand SAR data acquired with different geometries: Methodology and test on COSMO-SkyMed images

When large floods occur, satellite data are useful for providing emergency managers with frequent and synoptic maps of affected areas, even on a daily basis. Only the use of on-demand SAR data enables the high-resolution monitoring of flood events through acquisitions performed day and night, and regardless of cloud cover, over different areas. However, continuous flood mapping generally requires combining images acquired with different sensor parameters. In turn, this makes data interpretation and processing quite challenging and might require a time-consuming visual analysis activity, which contrasts with the requirement of fast daily delivery of flood maps to end-users.

This paper presents a new methodology designed to perform continuous flood monitoring in near real-time using on-demand SAR data. It implements a complete workflow, ranging from satellite tasking and pre-flood reference image collection to flood map generation. The core of the methodology is a new automated algorithm based on change detection that can work with data captured with different imaging geometries. The algorithm is designed to discriminate the change due to the change in the scenario from that due to possible differences in the acquisition parameters of the images. It applies different image processing techniques, such as clustering, histogram equalization, fuzzy logic, and region growing, and implements two electromagnetic models. The algorithm is complemented by a post processing step whose objective is to make the daily flood maps consistent with each other. The methodology was tested on a major flood that hit Italy (Emilia-Romagna region) in May 2023, using COSMO-SkyMed data and benchmark flood maps derived from optical data and from the Rapid Mapping component of the Copernicus Emergency Management Service (CEMS). Additionally, it was applied to another flood event that occurred in Italy (Tuscany region) in November 2023, for which benchmark CEMS products were also available, to further assess its reliability. Across these case studies, the algorithm achieved F1-scores ranging from 76% to 90%, demonstrating that, even when using data acquired with geometries that are non-optimal for flood mapping, the methodology produces reliable results. These results are consistent with those reported in the literature for change detection methods applied to acquisitions from the same orbit and for semi-automated supervised workflows such as those used by CEMS.

The pseudocode of the algorithm is available at: https://github.com/LucaP-CIMA/AUTOWADE2.0-pseudocode.