Optical River Ice Spectral Subclassification on the Tibetan Plateau: A Landsat 5–9 and Sentinel-2 Benchmark with Interpretable Machine Learning.
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| Title: | Optical River Ice Spectral Subclassification on the Tibetan Plateau: A Landsat 5–9 and Sentinel-2 Benchmark with Interpretable Machine Learning. |
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| Authors: | Zhang, Hanwen1 (AUTHOR), Li, Hongyi2,3,4 (AUTHOR) lihongyi@lzb.ac.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1437. 24p. |
| Subjects: | Ice on rivers, lakes, etc., Multispectral imaging, Hydrological research, Landsat satellites, Machine learning |
| Geographic Terms: | Tibet (China) |
| Abstract: | Highlights: What are the main findings? Four reproducible optical river ice subclasses (thin-snow-covered ice, thick ice cover, thin ice, and frazil ice) are consistently identified across Landsat 5–9 and Sentinel-2 datasets on the Tibetan Plateau. River ice subclass separability is primarily controlled by visible-band brightness, spectral turnover near ~0.7 μm, and SWIR sensitivity to ice thickness and surface wetness. What are the implications of the main findings? This study establishes a sensor-aware and interpretable benchmark for optical river ice subclassification, moving the field beyond traditional binary ice–water discrimination. The identified spectral drivers and robust model performance enable improved monitoring of river ice heterogeneity and support future hydrological and hazard-related applications. River ice products from optical satellites are still dominated by binary ice–water or ice–snow discrimination, leaving within-ice spectral heterogeneity largely unresolved. This study benchmarks how far river ice can be subclassified from multispectral reflectance alone on the Tibetan Plateau using Landsat 5/7, Landsat 8/9, and Sentinel-2 surface-reflectance imagery. We compiled 356 winter scenes acquired between 2000 and 2024 across eight Tibetan Plateau basins, delineated river ice using NDSI and RDRI, and extracted 24,674 pixel-level spectra. To define reproducible subclasses, we applied K-means clustering guided by the Silhouette Coefficient, Davies–Bouldin index, Calinski–Harabasz index, and Gap Statistic. Combined with stratified visual interpretation, this approach consistently supported four optical spectral subclasses: thin-snow-covered ice, thick ice cover, thin ice, and frazil ice. Within-sensor classification accuracy remained extremely high (overall accuracy ≥ 0.948; kappa ≥ 0.929), with the Backpropagation Neural Network (BPNN) and tree ensembles performing best. Crucially, evaluating the optimal BPNN architecture revealed exceptional multi-dimensional generalizability: a Leave-One-Basin-Out spatial cross-validation yielded a stable average OA > 99% with an average Kappa > 0.98, while a unified multi-sensor model achieved a robust OA of 90.14% and a Kappa of 0.86. The most stable discriminative cues were visible-band brightness, reflectance turnover near ~0.7 μm, and shortwave-infrared sensitivity to effective thickness and surface wetness. These results provide a sensor-aware benchmark for practical optical river ice spectral subclassification and clarify which multispectral bands most strongly constrain subclass separability. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Four reproducible optical river ice subclasses (thin-snow-covered ice, thick ice cover, thin ice, and frazil ice) are consistently identified across Landsat 5–9 and Sentinel-2 datasets on the Tibetan Plateau. River ice subclass separability is primarily controlled by visible-band brightness, spectral turnover near ~0.7 μm, and SWIR sensitivity to ice thickness and surface wetness. What are the implications of the main findings? This study establishes a sensor-aware and interpretable benchmark for optical river ice subclassification, moving the field beyond traditional binary ice–water discrimination. The identified spectral drivers and robust model performance enable improved monitoring of river ice heterogeneity and support future hydrological and hazard-related applications. River ice products from optical satellites are still dominated by binary ice–water or ice–snow discrimination, leaving within-ice spectral heterogeneity largely unresolved. This study benchmarks how far river ice can be subclassified from multispectral reflectance alone on the Tibetan Plateau using Landsat 5/7, Landsat 8/9, and Sentinel-2 surface-reflectance imagery. We compiled 356 winter scenes acquired between 2000 and 2024 across eight Tibetan Plateau basins, delineated river ice using NDSI and RDRI, and extracted 24,674 pixel-level spectra. To define reproducible subclasses, we applied K-means clustering guided by the Silhouette Coefficient, Davies–Bouldin index, Calinski–Harabasz index, and Gap Statistic. Combined with stratified visual interpretation, this approach consistently supported four optical spectral subclasses: thin-snow-covered ice, thick ice cover, thin ice, and frazil ice. Within-sensor classification accuracy remained extremely high (overall accuracy ≥ 0.948; kappa ≥ 0.929), with the Backpropagation Neural Network (BPNN) and tree ensembles performing best. Crucially, evaluating the optimal BPNN architecture revealed exceptional multi-dimensional generalizability: a Leave-One-Basin-Out spatial cross-validation yielded a stable average OA > 99% with an average Kappa > 0.98, while a unified multi-sensor model achieved a robust OA of 90.14% and a Kappa of 0.86. The most stable discriminative cues were visible-band brightness, reflectance turnover near ~0.7 μm, and shortwave-infrared sensitivity to effective thickness and surface wetness. These results provide a sensor-aware benchmark for practical optical river ice spectral subclassification and clarify which multispectral bands most strongly constrain subclass separability. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091437 |
