Articles | Volume 21, issue 2
https://doi.org/10.5194/os-21-787-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-21-787-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF)
AGO-GHER, University of Liège, Allée du Six Aout 17, Sart Tilman, 4000 Liège, Belgium
Dimitry Van der Zande
Royal Belgian Institute of Natural Sciences (RBINS), Direction Natural Environment Rue Vautier 29, 1000 Brussels, Belgium
Alexander Barth
AGO-GHER, University of Liège, Allée du Six Aout 17, Sart Tilman, 4000 Liège, Belgium
Antoine Dille
Royal Belgian Institute of Natural Sciences (RBINS), Direction Natural Environment Rue Vautier 29, 1000 Brussels, Belgium
Joppe Massant
Royal Belgian Institute of Natural Sciences (RBINS), Direction Natural Environment Rue Vautier 29, 1000 Brussels, Belgium
Jean-Marie Beckers
AGO-GHER, University of Liège, Allée du Six Aout 17, Sart Tilman, 4000 Liège, Belgium
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Pamela Linford, Iván Pérez-Santos, Paulina Montero, Patricio A. Díaz, Claudia Aracena, Elías Pinilla, Facundo Barrera, Manuel Castillo, Aida Alvera-Azcárate, Mónica Alvarado, Gabriel Soto, Cécile Pujol, Camila Schwerter, Sara Arenas-Uribe, Pilar Navarro, Guido Mancilla-Gutiérrez, Robinson Altamirano, Javiera San Martín, and Camila Soto-Riquelme
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Francesca Doglioni, Robert Ricker, Benjamin Rabe, Alexander Barth, Charles Troupin, and Torsten Kanzow
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This paper presents a new satellite-derived gridded dataset, including 10 years of sea surface height and geostrophic velocity at monthly resolution, over the Arctic ice-covered and ice-free regions, up to 88° N. We assess the dataset by comparison to independent satellite and mooring data. Results correlate well with independent satellite data at monthly timescales, and the geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km.
Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers
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Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.
Malek Belgacem, Katrin Schroeder, Alexander Barth, Charles Troupin, Bruno Pavoni, Patrick Raimbault, Nicole Garcia, Mireno Borghini, and Jacopo Chiggiato
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The Mediterranean Sea exhibits an anti-estuarine circulation, responsible for its low productivity. Understanding this peculiar character is still a challenge since there is no exact quantification of nutrient sinks and sources. Because nutrient in situ observations are generally infrequent and scattered in space and time, climatological mapping is often applied to sparse data in order to understand the biogeochemical state of the ocean. The dataset presented here partly addresses these issues.
Estrella Olmedo, Verónica González-Gambau, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Marilaure Gregoire, Aida Álvera-Azcárate, Luminita Buga, and Marie-Hélène Rio
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-364, https://doi.org/10.5194/essd-2021-364, 2021
Revised manuscript not accepted
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We present the first dedicated satellite salinity product in the Black Sea. We use the measurements provided by the European Soil Moisture and Ocean Salinity mission. We introduce enhanced algorithms for dealing with the contamination produced by the Radio Frequency Interferences that strongly affect this basin. We also provide a complete quality assessment of the new product and give an estimated accuracy of it.
Sylvain Watelet, Øystein Skagseth, Vidar S. Lien, Helge Sagen, Øivind Østensen, Viktor Ivshin, and Jean-Marie Beckers
Earth Syst. Sci. Data, 12, 2447–2457, https://doi.org/10.5194/essd-12-2447-2020, https://doi.org/10.5194/essd-12-2447-2020, 2020
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We present here a seasonal atlas of the Barents Sea including both temperature and salinity for the period 1965–2016. This atlas is curated using several in situ data sources interpolated thanks to the tool DIVA minimizing the expected errors. The results show a recent "Atlantification" of the Barents Sea, i.e., a general increase in both temperature and salinity, while its density remains stable. The atlas is made freely accessible (https://doi.org/10.21335/NMDC-2058021735).
Cited articles
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Short summary
This work presents an approach for increasing the spatial resolution of satellite data and interpolating gaps due to cloud cover, using a method called DINEOF (data-interpolating empirical orthogonal functions). The method is tested on turbidity and chlorophyll-a concentration data in the Belgian coastal zone and the North Sea. The results show that we are able to improve the spatial resolution of these data in order to perform analyses of spatial and temporal variability in coastal regions.
This work presents an approach for increasing the spatial resolution of satellite data and...