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Open AccessArticle

Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case

1
Environmental Research and Innovation Department (ERIN), Luxembourg Institute of Science and Technology (LIST), L-4422 Belvaux, Luxembourg
2
CIMA Research Foundation, I-17100 Savona, Italy
3
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, I-00184 Rome, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(2), 107; https://doi.org/10.3390/rs11020107
Received: 21 September 2018 / Revised: 25 December 2018 / Accepted: 30 December 2018 / Published: 9 January 2019
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
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. View Full-Text
Keywords: SAR; floodwater mapping; InSAR coherence; urban areas; Sentinel-1 SAR; floodwater mapping; InSAR coherence; urban areas; Sentinel-1
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Chini, M.; Pelich, R.; Pulvirenti, L.; Pierdicca, N.; Hostache, R.; Matgen, P. Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case. Remote Sens. 2019, 11, 107.

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