A multi-source remote sensing-based geocommunication tool for global flood monitoring and management

Abstract

Global warming is expected to increase the frequency of extreme flooding, making rapid and accurate flood mapping crucial for effective risk assessment. Many governmental agencies and organizations are developing flood risk assessment tools; however, due to the lack of observational records, some rely on probabilistic generated data, uncalibrated simulations, or terrain-based methods, all of which are subject to various types of uncertainty. Although remote sensing provides valuable flood data with global coverage, single-source reliance is constrained by satellite revisit rates, resolution, weather conditions, and sensor limitations. To address these challenges, this study introduces a user-friendly application on the Google Earth Engine (GEE) platform that enables near-real-time global flood mapping using a multi-source remote sensing approach. By leveraging optical and SAR imagery, the App ensures improved water detection accuracy and supports all-weather and day/night monitoring. Our results show SAR and optical flood inundation maps agree up to 80 %. Beyond flood mapping, the tool leverages GEE datasets to extract multi-disciplinary information, such as population exposure and affected residential, urban, and cropland areas, to support timely decision-making. For example, during the Sylhet, Bangladesh flood, the tool identified over 300,000 people potentially affected and approximately 600 km² of cropland inundated. This research presents one of the first global-scale, rapid, multi-source flood mapping tools tailored to both expert users and non-expert decision-makers. It offers a practical solution to current data limitations and supports more informed emergency response, planning, and climate resilience efforts to foster communication across scientific, policy, management, and operational communities.