Combining Time-Series Variation Modeling and Fuzzy Spatiotemporal Feature Fusion: A Novel Approach for Unsupervised Flood Mapping Using Dual-Polarized Sentinel-1 SAR Images

Due to the impact of climate change, the frequency of flood events has increased in recent years, which puts forward an urgent need for timely and accurate flood mapping for emergency response. As the synthetic aperture radar (SAR) enables all-time monitoring regardless of bad weather conditions, it fits far better than passive optical sensors to delineate submerged areas during flood events. However, the universal, rapid, and accurate detection of flood extent remains a challenge. Drawing inspiration from the analysis of time-series variation in representative ground objects caused by flood events, as observed in a dual-polarized SAR time series over a hydrological year, we construct a novel window-based variation model. This model can be used to capture both long-term trends and short-term fluctuations of flood features across different polarization modes. Subsequently, we introduce an unsupervised flood-mapping framework that integrates spatiotemporal flood features extracted by fuzzy-based methods. Given the distinct backscatter value of short vegetation, a flooded short vegetation (FSV) activation model is designed and performed to enhance flood-mapping accuracy in complex regions. The proposed method, tested on the 2020 East Dongting Lake flood in China, surpasses three unsupervised flood-mapping methods and two deep-learning methods in terms of quantitative evaluation and visual performance. The uncertainty of our proposed framework is tested through parameter sensitivity analyses, comparisons with flood-mapping results from other sensor images, and extensive experiments on floods at different locations and times, thereby demonstrating its effectiveness, stability, and universality.

Automated summary

This study proposes a novel flood detection model based on time-series variation analysis and integrates it with fuzzy-based methods to develop an unsupervised flood-mapping framework. The framework also includes a flooded short vegetation activation model to improve accuracy in complex regions. Experimental results show that the proposed method outperforms other methods in terms of quantitative evaluation and visual performance, demonstrating its effectiveness, stability, and universality.