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This article presents an automated Sentinel-1-based processing chain designed for flood detection and monitoring in near-real-time (NRT). Since no user intervention is required at any stage of the flood mapping procedure, the processing chain allows deriving time-critical disaster information in less than 45 min after a new data set is available on the Sentinel Data Hub of the European Space Agency (ESA). Due to the systematic acquisition strategy and high repetition rate of Sentinel-1, the processing chain can be set up as a web-based service that regularly informs users about the current flood conditions in a given area of interest. The thematic accuracy of the thematic processor has been assessed for two test sites of a flood situation at the border between Greece and Turkey with encouraging overall accuracies between 94.0% and 96.1% and Cohen’s kappa coefficients (κ) ranging from 0.879 to 0.910. The accuracy assessment, which was performed separately for the standard polarizations (VV/VH) of the interferometric wide swath (IW) mode of Sentinel-1, further indicates that under calm wind conditions, slightly higher thematic accuracies can be achieved by using VV instead of VH polarization data.
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André Twele
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
Wenxi Cao
Shenzhen Institute of Information Technology
Simon Plank
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
International Journal of Remote Sensing
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
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Twele et al. (Tue,) studied this question.
synapsesocial.com/papers/69dc34a01eeef32283c0fa64 — DOI: https://doi.org/10.1080/01431161.2016.1192304