Articles | Volume 16, issue 7
https://doi.org/10.5194/essd-16-3193-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3193-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Gap-filling techniques applied to the GOCI-derived daily sea surface salinity product for the Changjiang diluted water front in the East China Sea
Jisun Shin
Marine Research Institute, Pusan National University, Busan, 46241, South Korea
Dae-Won Kim
Center for Climate Physics, Institute for Basic Science, Busan, 46241, South Korea
Pusan National University, Busan, 46241, South Korea
So-Hyun Kim
Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea
Gi Seop Lee
Marine Bigdata AI Center, Korea Institute of Ocean Science and Technology, Busan, 49111, South Korea
Boo-Keun Khim
Marine Research Institute, Pusan National University, Busan, 46241, South Korea
Department of Oceanography, Pusan National University, Busan, 46241, South Korea
Marine Research Institute, Pusan National University, Busan, 46241, South Korea
Department of Oceanography, Pusan National University, Busan, 46241, South Korea
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Short summary
We overcame the limitations of satellite and reanalysis sea surface salinity (SSS) datasets and produced a gap-free gridded SSS product with reasonable accuracy and a spatial resolution of 1 km using a machine learning model. Our data enabled the recognition of SSS distribution and movement patterns of the Changjiang diluted water (CDW) front in the East China Sea (ECS) during summer. These results will further advance our understanding and monitoring of long-term SSS variations in the ECS.
We overcame the limitations of satellite and reanalysis sea surface salinity (SSS) datasets and...
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