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• W4280494923 abstract "Satellite-based flood mapping has become an important part of disaster response. In order to accurately distinguish flood inundation from normally present conditions, up-to-date, high-resolution information on the seasonal water cover is crucial. This information is usually neglected in disaster management, which may result in a non-reliable representation of the flood extent, mainly in regions with highly dynamic hydrological conditions. In this study, we present a fully automated method to generate a global reference water product specifically designed for the use in global flood mapping applications based on high resolution Earth Observation data. The proposed methodology combines existing processing pipelines for flood detection based on Sentinel-1/2 data and aggregates permanent as well as seasonal water masks over an adjustable reference time period. The water masks are primarily based on the analysis of Sentinel-2 data and are complemented by Sentinel-1-based information in optical data scarce regions. First results are demonstrated in five selected study areas (Australia, Germany, India, Mozambique, and Sudan), distributed across different climate zones and are systematically compared with external products. Further, the proposed product is exemplary applied to three real flood events in order to evaluate the impact of the used reference water mask on the derived flood extent. Results show, that it is possible to generate a consistent reference water product at 10–20 m spatial resolution, that is more suitable for the use in rapid disaster response than previous masks. The proposed multi-sensor approach is capable of producing reasonable results, even if only few or no information from optical data is available. Further it becomes clear, that the consideration of seasonality of water bodies, especially in regions with highly dynamic hydrological and climatic conditions, reduces potential over-estimation of the inundation extent and gives a more reliable picture on flood-affected areas. • Automatic large-scale water segementation based on Sentinel-1 and Sentinel-2 data. • Fusing Sentinel-1 and Sentinel-2 time-series data for global water mask generation. • Computation of a permanent and seasonal reference water product at 10 m resolution. • Extensive comparison to external permanent and seasonal reference water products. • The use of seasonal reference water masks improves flood mapping." @default.
• W4280494923 created "2022-05-22" @default.
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• W4280494923 date "2022-09-01" @default.
• W4280494923 modified "2023-10-03" @default.
• W4280494923 title "Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping" @default.
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• W4280494923 doi "https://doi.org/10.1016/j.rse.2022.113077" @default.
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