SAR Flood Anomaly Mapping Through Statistical Time-Series Feature Classification and Pixel-Wise TCEV Modeling

Flood mapping using Synthetic Aperture Radar (SAR) observations is essential for rapid disaster response, yet existing approaches often struggle to characterize rare extreme flood events due to limited temporal context and insufficient representation of flood anomalies. This study proposes a statistical time-series feature classification and pixel-wise TCEV modeling method for SAR flood anomaly mapping by integrating extreme value theory. Long-term Sentinel-1 SAR time series were used to construct nine statistical features for flood classification using a Tabular Prior-data Fitted Network (TabPFN), while a separately fitted Two-Component Extreme Value (TCEV) model was used to calculate event-period anomaly probabilities. The resulting spatial classification mask and TCEV-based anomaly probabilities were then combined to produce Temporal Flood Anomaly Maps (TFAMs). The proposed method was evaluated using four extreme flood events under diverse environmental conditions. The generated TFAMs characterized flood-related temporal anomalies, with the highest ROC-AUC 0.87 and average precision 0.81 achieved for New South Wales. The corresponding TFAM-derived binary flood extent map achieved the highest F1-score of 0.81, IoU of 0.69, and Kappa coefficient of 0.72.