Urban Flood Detection Using Sentinel1-A Images

Abstract

Synthetic Aperture Radar (SAR) imagery plays a vital role in flood mapping due to the day/night, almost all-weather, and cloud penetration capabilities. Although SAR backscatter intensity can accurately identify flooded areas on bare soil, it is still challenging to classify flooded urban areas due to the complexity of urban structures. Polarimetric SAR (PolSAR) and Interferometric SAR (InSAR) can provide us with a robust identification of backscatter patterns in urban areas, including single-bounce and double-bounce backscatters. In this study, we explore the potential of PolSAR and InSAR in urban flood mapping using a Random Forest model. The study area is located in Fredericton, New Brunswick, along the Saint John River, which has a long history of flooding. We examined various combinations of PolSAR and InSAR features, derived from Sentinel-1A images, along with four other features that are well-known to contribute to flooding, to select the best features for the model. The results showed that employing Polarimetric and Interferometric SAR (PolInSAR) features together with land-use/land-cover, altitude, slope, and aspect layers, reached an 88.6% flood classification accuracy in urban areas.