Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF)
<p>In this work we present a super-resolution approach for deriving high-spatial-resolution and high-temporal-resolution ocean colour satellite datasets. The technique is based on DINEOF (data-interpolating empirical orthogonal functions), a data-driven method that uses the spatio-temporal coh...
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Copernicus Publications
2025-04-01
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| Series: | Ocean Science |
| Online Access: | https://os.copernicus.org/articles/21/787/2025/os-21-787-2025.pdf |
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| author | A. Alvera-Azcárate D. Van der Zande A. Barth A. Dille J. Massant J.-M. Beckers |
| author_facet | A. Alvera-Azcárate D. Van der Zande A. Barth A. Dille J. Massant J.-M. Beckers |
| author_sort | A. Alvera-Azcárate |
| collection | DOAJ |
| description | <p>In this work we present a super-resolution approach for deriving high-spatial-resolution and high-temporal-resolution ocean colour satellite datasets. The technique is based on DINEOF (data-interpolating empirical orthogonal functions), a data-driven method that uses the spatio-temporal coherence of analysed datasets to infer missing information. DINEOF is used here to effectively increase the spatial resolution of satellite data and is applied to a combination of Sentinel-2 and Sentinel-3 datasets. The results show that DINEOF is able to infer the spatial variability observed in the Sentinel-2 data to the Sentinel-3 data while reconstructing missing information due to clouds and reducing the amount of noise in the initial dataset. In order to achieve this, the Sentinel-2 and Sentinel-3 datasets have undergone the same pre-processing, including a comprehensive, region-independent, and pixel-based automatic switching scheme for choosing the most appropriate atmospheric correction and ocean colour algorithm to derive in-water products. The super-resolution DINEOF has been applied to two different variables (turbidity and chlorophyll) and two different domains (Belgian coastal zone and the whole of the North Sea), and the sub-mesoscale variability of the turbidity along the Belgian coastal zone has been studied.</p> |
| format | Article |
| id | doaj-art-b7735741e72245df935f07f350cefdd1 |
| institution | OA Journals |
| issn | 1812-0784 1812-0792 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Ocean Science |
| spelling | doaj-art-b7735741e72245df935f07f350cefdd12025-08-20T02:26:23ZengCopernicus PublicationsOcean Science1812-07841812-07922025-04-012178780510.5194/os-21-787-2025Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF)A. Alvera-Azcárate0D. Van der Zande1A. Barth2A. Dille3J. Massant4J.-M. Beckers5AGO-GHER, University of Liège, Allée du Six Aout 17, Sart Tilman, 4000 Liège, BelgiumRoyal Belgian Institute of Natural Sciences (RBINS), Direction Natural Environment Rue Vautier 29, 1000 Brussels, BelgiumAGO-GHER, University of Liège, Allée du Six Aout 17, Sart Tilman, 4000 Liège, BelgiumRoyal Belgian Institute of Natural Sciences (RBINS), Direction Natural Environment Rue Vautier 29, 1000 Brussels, BelgiumRoyal Belgian Institute of Natural Sciences (RBINS), Direction Natural Environment Rue Vautier 29, 1000 Brussels, BelgiumAGO-GHER, University of Liège, Allée du Six Aout 17, Sart Tilman, 4000 Liège, Belgium<p>In this work we present a super-resolution approach for deriving high-spatial-resolution and high-temporal-resolution ocean colour satellite datasets. The technique is based on DINEOF (data-interpolating empirical orthogonal functions), a data-driven method that uses the spatio-temporal coherence of analysed datasets to infer missing information. DINEOF is used here to effectively increase the spatial resolution of satellite data and is applied to a combination of Sentinel-2 and Sentinel-3 datasets. The results show that DINEOF is able to infer the spatial variability observed in the Sentinel-2 data to the Sentinel-3 data while reconstructing missing information due to clouds and reducing the amount of noise in the initial dataset. In order to achieve this, the Sentinel-2 and Sentinel-3 datasets have undergone the same pre-processing, including a comprehensive, region-independent, and pixel-based automatic switching scheme for choosing the most appropriate atmospheric correction and ocean colour algorithm to derive in-water products. The super-resolution DINEOF has been applied to two different variables (turbidity and chlorophyll) and two different domains (Belgian coastal zone and the whole of the North Sea), and the sub-mesoscale variability of the turbidity along the Belgian coastal zone has been studied.</p>https://os.copernicus.org/articles/21/787/2025/os-21-787-2025.pdf |
| spellingShingle | A. Alvera-Azcárate D. Van der Zande A. Barth A. Dille J. Massant J.-M. Beckers Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF) Ocean Science |
| title | Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF) |
| title_full | Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF) |
| title_fullStr | Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF) |
| title_full_unstemmed | Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF) |
| title_short | Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF) |
| title_sort | generation of super resolution gap free ocean colour satellite products using data interpolating empirical orthogonal functions dineof |
| url | https://os.copernicus.org/articles/21/787/2025/os-21-787-2025.pdf |
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