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This article presents a methodology to improve the flood classification results of an automatic Sentinel-1 Flood Service (S-1FS) in arid areas, where reliable SAR-based water detection is usually not possible. Statistical information of Sentinel-1 (S-1) backscatter time series data were used to remove water look-alikes related to sand surfaces which generally lead to significant overestimations of the flood extent as these are characterized by similar backscatter values as open water areas. The approach was demonstrated and evaluated on a flood event in May 2016 at Webi Shabelle River, Somalia, where a time series of 15 Sentinel-1 archive data were available. The algorithm proved capable to significantly improving the classification accuracy of the S-1 flood processing chain and increases the Overall Accuracy by approx. 5% and the User's Accuracy of the class flood by approx. 26% within the study area.