Automatic monitoring of surface water dynamics using Sentinel-1 and Sentinel-2 data with Google Earth Engine

Abstract

Dynamic monitoring of floods is important for water resource management and disaster prevention. Obtaining multitemporal surface water distribution maps using remote sensing technology can help in elucidating the trends in water expansion so that measures can be quickly formulated. Sentinel-1 synthetic aperture radar (SAR) observation data are particularly suitable for this task because of their high spatial resolution and short revisit cycle, as well as its cloud-penetration ability. However, quickly and accurately mapping floods from a large number of SAR images remains challenging because of the enormous pressure on data acquisition and processing. Hence, in this study, we designed a new automatic SAR image flood mapping method based on the Google Earth Engine (GEE) cloud platform, which is an improvement over the Otsu method, and solves the problem of a higher segmentation threshold caused by images that do not meet the bimodal distribution hypothesis. In addition, to eliminate the omissions caused by salt-and-pepper noise and the misclassification caused mainly by low-backscattering-intensity vegetation and mountain shadows, we constructed an algorithm based on topological relationships and a DSM (Digital Surface Model) local search algorithm. The proposed method achieved an accuracy of 96.213% and 98.611% and F1 scores of 0.87254 and 0.89298 for plains and mountainous terrain, respectively. This method uses powerful computing resources and abundant datasets provided by the GEE cloud platform, and can be used for large-scale, long-term, and dynamic flood monitoring.