| Title |
Sentinel-1-based flood mapping: a fully automated processing chain |
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| Author |
André Twele, Wenxi Cao, Simon Plank, Sandro Martinis |
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| Abstract |
This article presents an automated Sentinel-1-based processing chain designed for flood detection and monitoring in near-realtime (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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| Citation |
André Twele, Wenxi Cao, Simon Plank & Sandro Martinis (2016) Sentinel-1-based flood mapping: a fully automated processing chain, International Journal of Remote Sensing, 37:13, 2990-3004, DOI: 10.1080/01431161.2016.1192304 |
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