Mapping Global Floods with 10 Years of Satellite Radar Data
This provides a comprehensive global flood monitoring tool for researchers and disaster response practitioners, though it is incremental in improving existing satellite-based methods.
The study tackled the scarcity of long-term global flood datasets by developing a deep learning model using Sentinel-1 SAR satellite imagery to map flood extents consistently over 10 years, unaffected by clouds, and applied it to identify flood-prone areas in Ethiopia and respond to floods in Kenya.
Floods cause extensive global damage annually, making effective monitoring essential. While satellite observations have proven invaluable for flood detection and tracking, comprehensive global flood datasets spanning extended time periods remain scarce. In this study, we introduce a novel deep learning flood detection model that leverages the cloud-penetrating capabilities of Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery, enabling consistent flood extent mapping in through cloud cover and in both day and night conditions. By applying this model to 10 years of SAR data, we create a unique, longitudinal global flood extent dataset with predictions unaffected by cloud coverage, offering comprehensive and consistent insights into historically flood-prone areas over the past decade. We use our model predictions to identify historically flood-prone areas in Ethiopia and demonstrate real-time disaster response capabilities during the May 2024 floods in Kenya. Additionally, our longitudinal analysis reveals potential increasing trends in global flood extent over time, although further validation is required to explore links to climate change. To maximize impact, we provide public access to both our model predictions and a code repository, empowering researchers and practitioners worldwide to advance flood monitoring and enhance disaster response strategies.