Performance of Dual-polarization Sentinel-1 Flood Monitoring in Austria

Abstract

Floods have caused severe damage in Austria in recent years, and climate change is expected to increase flood risks in the future. While Austria has an advanced hydrological measurement network for flood monitoring and prediction, Synthetic Aperture Radar (SAR) data from satellites can provide valuable additional information. This study presents a well-established Bayesian flood mapping approach that automatically retrieves flood extents using Sentinel‑1 SAR data. By combining VV and VH polarizations, the algorithm aims to improve sensitivity for flood mapping. However, Austria’s complex topography and land cover, as well as flood dynamics present significant challenges for SAR-based flood mapping. To assess the suitability of SAR-based flood mapping and specifically our algorithm for Austria, we introduce two novel evaluation methods: (1) assessing temporal coverage through hydrological measurements and (2) evaluating sensitivity using flood risk zones. Additionally, we validate the results using independent reference data from local helicopter surveys and high-resolution optical satellite imagery. Our findings show that the approach can map flood extents up to 60.64% of Austria’s flood-prone areas. Despite limitations in capturing rapid changes of flooding, our results demonstrate that Sentinel‑1 represents a breakthrough in its ability to document the progression of flood events. Furthermore, a stratified-sampled average overall accuracy (OA) of 74.67% and Normalized Matthews Correlation Coefficient (MCC) of 78.73% demonstrate a strong classification performance. This study confirms that despite existing challenges SAR-based flood mapping can effectively support flood monitoring and management in Austria.