Improving Backscatter-Based Surface Water Classification in Arid Environments Through Interferometric Coherence.
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| Title: | Improving Backscatter-Based Surface Water Classification in Arid Environments Through Interferometric Coherence. |
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| Authors: | Festa, Davide1 (AUTHOR) davide.festa@tuwien.ac.at, Roth, Florian1,2 (AUTHOR), Hassaan, Muhammed1 (AUTHOR), Wagner, Wolfgang1,2 (AUTHOR) |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 17, p2966. 11p. |
| Subjects: | Backscattering, Coherence (Physics), Bodies of water, Hydrology, Synthetic aperture radar, Arid regions |
| Abstract: | Highlights: What are the main findings? Coherence Global 1-year interferometric coherence composites significantly outperform backscatter for permanent water detection in arid environments. Minimal overlap in detection failures proves strong complementarity between coherence and backscatter across all test sites. What are the implications of the main findings? Integrating coherence datasets will directly enhance the performance of global water monitoring services. Upcoming NISAR Analysis Ready Data (ARD) will drastically simplify integration into existing operational pipelines. 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Coherence Global 1-year interferometric coherence composites significantly outperform backscatter for permanent water detection in arid environments. Minimal overlap in detection failures proves strong complementarity between coherence and backscatter across all test sites. What are the implications of the main findings? Integrating coherence datasets will directly enhance the performance of global water monitoring services. Upcoming NISAR Analysis Ready Data (ARD) will drastically simplify integration into existing operational pipelines. 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18172966 |
