This paper presents an automatic algorithm for mapping floods. Its main characteristic is that it can detect not only inundated bare soils, but also floodwater in urban areas. The synthetic aperture radar (SAR) observations of the flood that hit the city of Houston (Texas) following the landfall of Hurricane Harvey in 2017 are used to apply and validate the algorithm. The latter consists of a two-step approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for focusing the analysis on the most risk-prone areas. Data provided by the Sentinel-1 mission acquired in both Strip Map and Interferometric Wide Swath modes were used, with a geometric resolution of 5 m and 20 m, respectively. Furthermore, the coherence-based algorithm takes full advantage of the Sentinel-1 mission's six-day repeat cycle, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas. The results for the Houston case study have been qualitatively evaluated through very-high-resolution optical images acquired almost simultaneously with SAR, crowdsourcing points derived by photointerpretation from Digital Globe and Federal Emergency Management Agency's (FEMA) inundation model over the area. For the first time the comparison with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission
