A Sentinel-1 Times Series-Based Exclusion Layer for Improved Flood Mapping in Arid Areas

Due to the similarity of the radar backscatter over open water and sand surfaces a reliable near real-time flood mapping based on radar sensors in arid areas is usually not possible. Within this paper an approach is presented to enhance the results of an automatic Sentinel-l flood processing chain of the German Aerospace Center (DLR) by removing overestimations of the water extent related to sand surfaces using a Sand Exclusion Layer (SEL) derived from time-series information of Sentinel-l data sets. The methodology was tested and validated on a flood event in May 2016 at Webi Shabelle River, Somalia, which has been covered by a time-series of 202 Sentinel-l scenes within the period April 2014 to May 2017. The algorithm proved capable to significantly improving the classification accuracy of the Sentinel-l flood service at this study site. Experimental results with variable lengths of the time-series have shown that the classification accuracy increased with increasing number of data sets at the cost of higher computational demand.