The fully-automatic Sentinel-1 Global Flood Monitoring service: Scientific challenges and future directions
Wolfgang Wagner, Bernhard Bauer-Marschallinger, Florian Roth, Tobias Raiger-Stachl, Christoph Reimer, Niall McCormick, Patrick Matgen, Marco Chini, Yu Li, Sandro Martinis, Marc Wieland, Franziska Kraft, Davide Festa, Muhammed Hassaan, Mark Edwin Tupas, Jie Zhao, Michaela Seewald, Michael Riffler, Luca Molini, Richard Kidd, Christian Briese, Peter Salamon
Abstract
Sentinel-1 is a unique resource for global flood monitoring, providing systematic, weather-independent Synthetic Aperture Radar (SAR) imagery with unprecedented coverage. To overcome limitations of on-demand flood mapping services that depend on human operators to collect and interpret satellite images, a fundamentally new approach was adopted by the Global Flood Monitoring (GFM) service. This service, which was launched in 2021 as part of the Copernicus Emergency Management Service (CEMS), processes all Sentinel-1 land images acquired in VV polarisation fully automatically in near-real time. This article presents the first comprehensive analysis of GFM’s scientific achievements and challenges during its initial years of operation. To map floods reliably under diverse environmental conditions, GFM combines three complementary flood-mapping algorithms with reference water datasets to differentiate flooded areas from permanent and seasonal water bodies. The service also offers a novel flood-likelihood layer and contextual information to highlight areas where flood mapping is unreliable or not feasible. These data layers were derived from a global 20 m backscatter datacube containing approximately 379 billion land surface pixels. This datacube also made it possible to generate the first global Sentinel-1 flood archive (2015 to present). Our performance analysis shows that GFM typically delivers flood maps within five hours of image acquisition. However, a significant percentage of floods may go undetected due to coverage gaps. Initial evaluation results show that good accuracies are achieved for larger-scale floods and regions in the temperate and tropical zones, while accuracies are lower for smaller-scale floods and arid environments. The GFM service will continue to improve service quality by enhancing flood detection capabilities using improved algorithms and additional data, such as the VH channel from Sentinel-1 or L-band data from the upcoming ROSE-L mission. • We set up a fully-automatic global monitoring service for flood mapping: GFM. • Flood and likelihood maps from three SAR-algorithms are combined in an ensemble. • Models are based on geophysical considerations on SAR’s sensitivity to flood. • A global multi-year SAR datacube allows robust and up-to-date models and parameters. • GFM output is joined by context-layers and is freely available in near-real-time.