Synthetic Aperture Radar (SAR) backscatter serves as a key tool for tracking surface water dynamics; however, single-source data dependencies introduce systematic bias tied to the specific physical limitations of the signal. A primary challenge in SAR analysis is the backscatter ambiguity created by ‘water look-alike’ surfaces, which frequently result in false-positive water detections. We show that integrating interferometric repeat-pass coherence significantly enhances the robustness of hydrological mapping in environments where backscatter is prone to signal ambiguity. Using global-scale C-band Sentinel-1 (S1) VV-polarized one-year mosaics (December 2019 to November 2020), we first analyzed normalized backscatter and coherence signatures across major land cover and land use (LULC) classes. To benchmark the complementary value of these data streams, a tile-based minimum-error thresholding approach was applied to detect permanent water surfaces across five challenging global test sites. This evaluation was conducted without post-processing or masking to isolate the fundamental strengths of each dataset. The results indicate that coherence is an optimal complement to backscatter in arid and bare soil regions, where it vastly outperforms backscatter in mapping inland water surfaces. Crucially, since the spatial overlap of False Positives and False Negatives between datasets is minimal, the inherent complementarity of the datasets is proven here via a logical AND fusion rule, which significantly mitigates commission errors and yields substantial improvements in the aggregated F1-score and IoU performance. Analysis-ready L-band NISAR products could contribute to a more comprehensive approach for operational, large-scale surface water assessments.
David Festa, Florian Roth, Muhammed Hassaan et al.· Remote Sensing· 0 citations
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.
Florian Roth, M. Tupas, B. Bauer-Marschallinger et al.· PFG – Journal of Photogramme...· 0 citations
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