Dynamic flood mapping by a normalized probabilistic classification method using satellite radar amplitude time series

Abstract

Owing to the vast development of Synthetic Aperture Radar (SAR), especially the improvement of spatio-temporal resolution, observing and quantifying the complex and dynamic flood process becomes increasingly feasible. Utilizing the Sentinel-1 Ground Range Detected (GRD) dataset, we proposed an improved probabilistic flood mapping method combining image Pareto Scaling (PS) normalization and Bayesian probability estimation. We validated our method during a flood event in Xinjiang County, Shaanxi Province of China in October 2021 using a high spatial resolution (0.1 m) Unmanned Aerial Vehicle (UAV) image. The overall reliability of the new method agrees 95% to the UAV measurements and achieves the highest accuracy (85.2%) when compared to the Sentinel-1 dual-polarized water index (SDWI) threshold method and the Z-score method. Our results distinguished four flood stages: flood emergence, peak, receding, and disappearance, which provide valuable insights into the dynamic change process of floods. Notably, we observed that pixels with different flood probabilities exhibited distinct temporal characteristics. The extremely high probability pixel experienced rapid fluctuations, while the medium probability pixel showed more gradual changes over time. We believe our proposed method can enhance our understanding of flood-prone areas and their dynamics so that decision-makers can develop targeted mitigation measures and response plans.