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- W4322006084 abstract "Flood events are becoming increasingly common with the increase in the frequency of extreme weather driven by climate change. The present state of the technologies for flood risk mapping is typically tested on small geographical regions due to limitation of flood inundation observations, which hinders the implementation of flood risk management activities. Synthetic aperture radar (SAR) measurements represent an indispensable data source for flood disaster planners and managers, given their ability to scan the Earth's surface nearly independently of weather conditions and the time of day. The decision by the European Space Agency (ESA) Copernicus program to open data from its Sentinel-1 SAR satellites to the public marks the first time of global, operational SAR data freely available. Combined with the emergence of cloud computing platforms like the Google Earth Engine (GEE), this development presents a tremendous opportunity to the disaster response community, for whom rapid access to analysis-ready data is needed to inform effective flood disaster response interventions and management plans. Here, we present an algorithm that exploits available Sentinel-1 SAR images in combination with historical Landsat and other auxiliary data sources hosted on the GEE to rapidly map surface inundation during flood events. Our algorithm relies on multi-temporal SAR statistics to identify historical floods. Additionally, historical Landsat-based surface water class probabilities are used to distinguish floods from permanent or seasonally occurring surface water. Using this algorithm, we can get a flood inundation map of the region of interest in less than 10 seconds. We tested the algorithm over Houston, Texas following the Hurricane Harvey in late August 2017 and the results showed an accuracy of 89.9%. The flexibility of our algorithm will allow for the rapid processing of future open-access SAR data, including data from future Sentinel-1 missions." @default.
- W4322006084 created "2023-02-26" @default.
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- W4322006084 date "2023-05-15" @default.
- W4322006084 modified "2023-10-16" @default.
- W4322006084 title "Rapid Flood inundation mapping using SAR data with Google Earth Engine cloud platform" @default.
- W4322006084 doi "https://doi.org/10.5194/egusphere-egu23-7558" @default.
- W4322006084 hasPublicationYear "2023" @default.
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