Fast Flood Extent Monitoring With SAR Change Detection Using Google Earth Engine

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; pp. 1 - 19
• Main Authors: Hamidi, Ebrahim, Peter, Brad G., Munoz, David F., Moftakhari, Hamed, Moradkhani, Hamid
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alliances; Automation; Change detection; Change detection and thresholding; Cloud computing; Compounding effects; Data acquisition; Decision making; Detection; Earth; Emergency response; Engines; Extreme weather; flood extent monitoring; Flood mapping; Flooding; Floods; Google Earth Engine (GEE); Hurricanes; Imagery; Landsat; Monitoring; multisource remote sensing data; Optical imaging; Optical sensors; Remote sensing; River discharge; River flow; Runoff; SAR (radar); Scaling; Sensitivity analysis; Storm surges; Storms; Streamlines; Synthetic aperture radar; United States > US
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3240097

Flooding is one of the most frequent and disastrous natural hazards triggered by extreme precipitation, high river runoff, hurricane storm surges, and the compounding effects of various flood drivers. This study introduces a new multisource remote sensing approach that leverages both multispectral optical imagery and the weather- and illumination-independent characteristics of synthetic aperture radar (SAR) data to streamline, automate, and map geographically reliable flood inundation extents. Utilizing the near real-time and cloud computing capabilities of Google Earth Engine (GEE), this process facilitates data acquisition and enables large-scale flood monitoring in an expeditious manner. Two major hurricanes along the U.S. Gulf Coast were evaluated: 1) the 2021 Hurricane Ida to the south of New Orleans, LA, USA, and 2) the 2017 Hurricane Harvey to the east of Houston, TX, USA. We devised a change detection and thresholding framework using multitemporal SAR imagery and validated the results with flood extent maps derived from Landsat 8 and Sentinel-2 optical imagery. We demonstrate that constant threshold values for flood extraction from SAR change detection indices are not ubiquitously suitable for all geographies; thus, we outline a heuristic that can be used to select thresholds suitable for specific sites through a fully automated sensitivity analysis. The results indicated high agreement between the SAR and optical imagery (77%-80%), with SAR providing the benefit of under-cloud detection. Furthermore, our results contribute to scaling the SAR approach to produce rapid and accurate information for decision-makers and emergency responders during time-sensitive flood events.