Evaluating Copernicus Global Flood Monitoring (GFM) Service trade-offs in near-real-time flood mapping

Near-real-time satellite-based flood maps support disaster risk management and emergency response. One widely used service is the Global Flood Monitoring (GFM) product of the Copernicus Emergency Management Service, launched in 2021 and based on Sentinel-1 Synthetic Aperture Radar (SAR) data. The GFM service combines three flood-mapping algorithms: pixel-based thresholding, region-based approaches, and change-detection techniques, merged using a majority-voting scheme to generate the final flood extent product. Another key strength of the GFM service is its rapid analysis, providing flood maps within approximately five hours of satellite image acquisition through a fully automated processing chain. As the product is increasingly relied upon by practitioners and decision-makers, there is a growing need to assess its accuracy and robustness. Understanding false alarms and missed detections is critical for improving the reliability and usability of the service.In this study, we systematically compare GFM flood maps across twenty real-world flood events using high-resolution reference datasets. To ensure temporal consistency, the GFM-derived flood maps are generated using Sentinel-1 acquisitions from the same day as the reference observations. Spatial agreement between datasets is quantified using the Intersection-over-Union metric.Our results suggest that the GFM service performs well for large, extensive flood events but degrades for smaller, localized ones. Many of the observed errors come not from flood detection itself, but from inaccuracies in the reference water layer - while surface water is correctly identified, misclassification of permanent or seasonal water bodies leads to false alarms and missed floods. We evaluate the three-underlying flood-mapping algorithms individually for consistent patterns of misdetection or false alarms. In addition, we develop an automated framework to rapidly compare any external flood map with the GFM outputs, enabling near-instant evaluation of agreement and error patterns. This framework provides practical insights into where and why the GFM services achieve successes and failures and offers continuous validation and iterative improvement of global flood mapping services.