Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine

Abstract

Abstract Synthetic aperture radar (SAR) sensors represent an indispensable data source for flood disaster planners and responders, given their ability to image the Earth's surface nearly independently of weather conditions and time of day. The decision by the European Space Agency (ESA) Copernicus program to open data from its Sentinel-1 SAR satellites to the public marks the first time global, operational SAR data have been made freely available. Combined with the emergence of cloud computing platforms like the Google Earth Engine (GEE), this development presents a tremendous opportunity to the disaster response community, for whom rapid access to analysis-ready data is needed to inform effective flood disaster response interventions and management plans. Here, we present an algorithm that exploits all available Sentinel-1 SAR images in combination with historical Landsat and other auxiliary data sources hosted on the GEE to rapidly map surface inundation during flood events. Our algorithm relies on multi-temporal SAR statistics to identify unexpected floods in near real-time. Additionally, historical Landsat-based surface water class probabilities are used to distinguish unexpected floods from permanent or seasonally occurring surface water. We assessed our algorithm over three recent flood events using coincident very high- spatial resolution imagery and operational flood maps. Using very high resolution optical imagery, we estimated an area-normalized accuracy of 89.8 ± 2.8% (95% c.i.) over Houston, Texas following Hurricane Harvey in late August 2017, representing an improvement of between 1.6% and 9.8% over flood maps derived from a simple backscatter threshold. Additionally, comparison of our results with SAR-derived Copernicus Emergency Management Service (EMS) maps following devastating floods in Thessaly, Greece and Eastern Madagascar in January and March 2018, respectively, yielded overall agreement rates of 98.5% in both cases. Importantly, our algorithm was able to ingest hundreds of SAR and optical images served on the GEE to produce flood maps over affected areas within minutes, circumventing the need for time-consuming data download and pre-processing steps. The flexibility of our algorithm will allow for the rapid processing of future open-access SAR data, including data from future Sentinel-1 missions.