Reconstructing High-Resolution Coastal Water Quality Data Based on a Deep Learning Multivariate Downscaling Approach.
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| Title: | Reconstructing High-Resolution Coastal Water Quality Data Based on a Deep Learning Multivariate Downscaling Approach. |
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| Authors: | Liu, Xiaoyu1,2 (AUTHOR), Wang, Xuan1,2 (AUTHOR) xuanwang@tju.edu.cn, Tong, Yicong1,2 (AUTHOR), Li, Wei1,2 (AUTHOR), Han, Guijun1,2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1346. 27p. |
| Subjects: | Downscaling (Climatology), Generative adversarial networks, Environmental monitoring, Deep learning, Climate change adaptation, Oceanography, Spatial data structures, Water quality |
| Geographic Terms: | Bo Hai (China) |
| Abstract: | Highlights: What are the main findings? Incorporating spatial modes as training inputs enhances downscaling performance compared to direct time- series learning by effectively capturing intrinsic spatial features. The downscaled fields exhibit fine-scale gradients and continuous structures invisible in original satellite observation. What is the implication of the main finding? Pre-trained time-invariant spatial modes can be used to reconstruct statistically consistent, high-resolution ocean fields directly from low-resolution satellite observation during any independent testing. By effectively capturing intrinsic spatial features, the proposed approach generates meaningful sub-grid information, thereby overcoming the resolution constraints in standard coastal remote sensing data and enhancing its applicability. The availability of high-resolution oceanographic data is critical for evidence-based coastal environmental management and climate resilience planning, yet it remains constrained by observational gaps and the prohibitive computational cost of fine-scale hydrodynamic modeling. While downscaling techniques provide a viable pathway, current data-driven approaches often lack statistical physical associations, overlook multivariate environmental interactions, and struggle to represent complex coastal topography. To address these limitations, we present MEOFGAN—an environmentally informed downscaling framework that integrates multivariate empirical orthogonal function (MEOF) decomposition with a generative adversarial network (GAN). The model extracts physically interpretable spatial modes of coupled ocean variables, learns their cross-scale transitions through adversarial training, and systematically incorporates high-resolution bathymetry as a static environmental constraint to enhance spatial fidelity. When applied to the Bohai Sea, MEOFGAN successfully downscales sea surface temperature (SST) and sea surface height (SSH) from 1/4° to 1/12°, achieving error reductions of 30–68% compared to benchmark methods while preserving ecologically relevant structural patterns (SSIM > 0.92). The framework demonstrates strong generalization by reconstructing 500 m resolution distributions of chlorophyll-a (Chl-a), dissolved oxygen (DO), and salinity in Bohai Bay, capturing fine-scale environmental gradients during a documented algal bloom event. This work establishes a methodological framework that can be transferred as a paradigm for generating high-resolution coastal datasets. Rather than serving as a universally transferable pre-trained model, the framework requires region-specific training and application. Data generated in this manner can directly support water quality monitoring, eutrophication assessment, habitat mapping, and regionally tailored climate adaptation strategies. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Incorporating spatial modes as training inputs enhances downscaling performance compared to direct time- series learning by effectively capturing intrinsic spatial features. The downscaled fields exhibit fine-scale gradients and continuous structures invisible in original satellite observation. What is the implication of the main finding? Pre-trained time-invariant spatial modes can be used to reconstruct statistically consistent, high-resolution ocean fields directly from low-resolution satellite observation during any independent testing. By effectively capturing intrinsic spatial features, the proposed approach generates meaningful sub-grid information, thereby overcoming the resolution constraints in standard coastal remote sensing data and enhancing its applicability. The availability of high-resolution oceanographic data is critical for evidence-based coastal environmental management and climate resilience planning, yet it remains constrained by observational gaps and the prohibitive computational cost of fine-scale hydrodynamic modeling. While downscaling techniques provide a viable pathway, current data-driven approaches often lack statistical physical associations, overlook multivariate environmental interactions, and struggle to represent complex coastal topography. To address these limitations, we present MEOFGAN—an environmentally informed downscaling framework that integrates multivariate empirical orthogonal function (MEOF) decomposition with a generative adversarial network (GAN). The model extracts physically interpretable spatial modes of coupled ocean variables, learns their cross-scale transitions through adversarial training, and systematically incorporates high-resolution bathymetry as a static environmental constraint to enhance spatial fidelity. When applied to the Bohai Sea, MEOFGAN successfully downscales sea surface temperature (SST) and sea surface height (SSH) from 1/4° to 1/12°, achieving error reductions of 30–68% compared to benchmark methods while preserving ecologically relevant structural patterns (SSIM > 0.92). The framework demonstrates strong generalization by reconstructing 500 m resolution distributions of chlorophyll-a (Chl-a), dissolved oxygen (DO), and salinity in Bohai Bay, capturing fine-scale environmental gradients during a documented algal bloom event. This work establishes a methodological framework that can be transferred as a paradigm for generating high-resolution coastal datasets. Rather than serving as a universally transferable pre-trained model, the framework requires region-specific training and application. Data generated in this manner can directly support water quality monitoring, eutrophication assessment, habitat mapping, and regionally tailored climate adaptation strategies. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091346 |
