A multi-sensor approach for increased measurements of floods and their societal impacts from space

Abstract

Merging observations from multiple satellites is necessary to ensure that extreme hydrological events are consistently observed. Here, we evaluate the potential improvements to flood detectability afforded by combining data collected globally by Landsat, Sentinel-2, and Sentinel-1. The enhanced temporal sampling increased the number of floods with at least 1 useful image (≤20% clouds) from 7% for single sensors to up to 66% for a potential multi-sensor product. As dramatic as the increased coverage is, the socioeconomic impacts are even more tangible. In the pre-Sentinel era, only 22% of the total population displaced by flood events benefitted from having high-resolution images, whereas a potential multi-sensor product would serve 75% of the displaced population. Additionally, the merged dataset could observe up to 100% of floods caused by challenging drivers, e.g., tropical cyclones, tidal surges, including those rarely seen by single sensors, and thereby enable insights into governing mechanisms of these events.