Enhanced flood water depth estimation from Sentinel-1A images

Abstract

ABSTRACT Floods, as a natural disaster, have severe consequences for all aspects of life and society, including but not limited to damaging harvests and transportation systems, and causing epidemic diseases. Estimating flood water depths has been a topic of great interest for several decades. Finding an effective solution to this problem is crucial for accurately estimating the impact of floods and making informed decisions to mitigate their consequences. The paper presents a novel approach to estimate flood water depths in Quang Binh province, Vietnam, by utilizing an improved Otsu algorithm to classify each image pixel as either water or non-water, and then applying the FwDET interpolation algorithm to compute the flood depth of each classified water pixel. The improved algorithm can improve the accuracy of water classification when compared to the traditional Otsu method by automatically dividing images into subregions of varying sizes, which are suitable for optimal Otsu computation.The estimated results for the study area in Quang Binh province were validated using ground measurement station data and demonstrated the relatively high accuracy of water classification (ranging from 91.4% to 92%) and estimated flood water depth (85.26%). Based on the positive results obtained from the investigation of the complex mountainous area mentioned above, it can be inferred that the proposed method has the potential to be applied to estimate flood depths in various types of terrains.