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Open AccessArticle

The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas

1
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany
2
Department of Civil, Geo and Environmental Engineering, Technical University of Munich, D-80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 583; https://doi.org/10.3390/rs10040583
Received: 21 March 2018 / Revised: 29 March 2018 / Accepted: 30 March 2018 / Published: 9 April 2018
(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes)
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Abstract

Due to the similarity of the radar backscatter over open water and over sand surfaces a reliable near real-time flood mapping based on satellite radar sensors is usually not possible in arid areas. Within this study, an approach is presented to enhance the results of an automatic Sentinel-1 flood processing chain by removing overestimations of the water extent related to low-backscattering sand surfaces using a Sand Exclusion Layer (SEL) derived from time-series statistics of Sentinel-1 data sets. The methodology was tested and validated on a flood event in May 2016 at Webi Shabelle River, Somalia and Ethiopia, which has been covered by a time-series of 202 Sentinel-1 scenes within the period June 2014 to May 2017. The approach proved capable of significantly improving the classification accuracy of the Sentinel-1 flood service within this study site. The Overall Accuracy increased by ~5% to a value of 98.5% and the User’s Accuracy increased by 25.2% to a value of 96.0%. Experimental results have shown that the classification accuracy is influenced by several parameters such as the lengths of the time-series used for generating the SEL. View Full-Text
Keywords: SAR (Synthetic Aperture Radar); water bodies; inundation; flood detection; Sentinel-1; time-series; sand surfaces; arid areas SAR (Synthetic Aperture Radar); water bodies; inundation; flood detection; Sentinel-1; time-series; sand surfaces; arid areas
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Martinis, S.; Plank, S.; Ćwik, K. The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas. Remote Sens. 2018, 10, 583.

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