Articles | Volume 14, issue 11
https://doi.org/10.5194/essd-14-5037-2022
© Author(s) 2022. 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-14-5037-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
18 Nov 2022
Data description paper |
| 18 Nov 2022
| 18 Nov 2022
Reconstructing ocean subsurface salinity at high resolution using a machine learning approach
Tian Tian
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410073, China
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao,
266071, China
Gongjie Wang
National Climate Center, Chinese Meteorological Administration,
Beijing, 100081, China
John Abraham
School of Engineering, University of St. Thomas, St. Paul,
MN 55105, USA
Wangxu Wei
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Shihe Ren
Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, 100081, China
Jiang Zhu
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Junqiang Song
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410073, China
Hongze Leng
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410073, China
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Cited
18 citations as recorded by crossref.
- Reconstructing ocean subsurface salinity at high resolution using a machine learning approach T. Tian et al. 10.5194/essd-14-5037-2022
- A Method for Predicting High-Resolution 3D Variations in Temperature and Salinity Fields Using Multi-Source Ocean Data X. Cao et al. 10.3390/jmse12081396
- Deep learning-driven 3D marine nitrate estimation: uncertainty mitigation through underwater signal exploitation and label augmentation X. Yu et al. 10.3389/fmars.2025.1576558
- An Updated Estimate of the Indonesian Throughflow Geostrophic Transport: Interannual Variability and Salinity Effect Y. Guo et al. 10.1029/2023GL103748
- Attention-enhanced deep learning model for reconstruction and downscaling of thermocline depth in the tropical Indian Ocean Z. Feng et al. 10.1016/j.ocemod.2025.102537
- Assessment of wetland ecosystem health in Rarh Region, India through P-S-R (pressure-state-response) model R. Khatun & S. Das 10.1016/j.scitotenv.2024.175700
- Reconstructing high-resolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations H. Su et al. 10.1016/j.isprsjprs.2024.09.022
- Super-resolution reconstruction of subsurface temperature field in South China Sea using satellite observations H. Wu et al. 10.1063/5.0253807
- Estimation of subsurface salinity and analysis of Changjiang diluted water volume in the East China Sea S. Kim et al. 10.3389/fmars.2023.1247462
- A deep learning approach to estimate ocean salinity with data sampled with expendable bathythermographs E. Campos et al. 10.1016/j.apor.2024.103997
- Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach J. Qi et al. 10.1016/j.dynatmoce.2023.101403
- Forecasting Vertical Profiles of Ocean Currents from Surface Characteristics: A Multivariate Multi-Head Convolutional Neural Network–Long Short-Term Memory Approach S. Kar et al. 10.3390/jmse11101964
- Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data Z. Gong et al. 10.1080/07038992.2023.2298575
- Reconstructing 3D ocean subsurface salinity (OSS) from T–S mapping via a data-driven deep learning model J. Zhang et al. 10.1016/j.ocemod.2023.102232
- Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation A. Giganti et al. 10.3390/rs16213963
- Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations A. Wang et al. 10.1109/TGRS.2024.3509616
- OceanVP: A HYCOM based benchmark dataset and a relational spatiotemporal predictive network for oceanic variable prediction Z. Shi et al. 10.1016/j.oceaneng.2024.117748
- Reconstructing ocean subsurface salinity at high resolution using a machine learning approach T. Tian et al. 10.5194/essd-14-5037-2022
17 citations as recorded by crossref.
- Reconstructing ocean subsurface salinity at high resolution using a machine learning approach T. Tian et al. 10.5194/essd-14-5037-2022
- A Method for Predicting High-Resolution 3D Variations in Temperature and Salinity Fields Using Multi-Source Ocean Data X. Cao et al. 10.3390/jmse12081396
- Deep learning-driven 3D marine nitrate estimation: uncertainty mitigation through underwater signal exploitation and label augmentation X. Yu et al. 10.3389/fmars.2025.1576558
- An Updated Estimate of the Indonesian Throughflow Geostrophic Transport: Interannual Variability and Salinity Effect Y. Guo et al. 10.1029/2023GL103748
- Attention-enhanced deep learning model for reconstruction and downscaling of thermocline depth in the tropical Indian Ocean Z. Feng et al. 10.1016/j.ocemod.2025.102537
- Assessment of wetland ecosystem health in Rarh Region, India through P-S-R (pressure-state-response) model R. Khatun & S. Das 10.1016/j.scitotenv.2024.175700
- Reconstructing high-resolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations H. Su et al. 10.1016/j.isprsjprs.2024.09.022
- Super-resolution reconstruction of subsurface temperature field in South China Sea using satellite observations H. Wu et al. 10.1063/5.0253807
- Estimation of subsurface salinity and analysis of Changjiang diluted water volume in the East China Sea S. Kim et al. 10.3389/fmars.2023.1247462
- A deep learning approach to estimate ocean salinity with data sampled with expendable bathythermographs E. Campos et al. 10.1016/j.apor.2024.103997
- Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach J. Qi et al. 10.1016/j.dynatmoce.2023.101403
- Forecasting Vertical Profiles of Ocean Currents from Surface Characteristics: A Multivariate Multi-Head Convolutional Neural Network–Long Short-Term Memory Approach S. Kar et al. 10.3390/jmse11101964
- Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data Z. Gong et al. 10.1080/07038992.2023.2298575
- Reconstructing 3D ocean subsurface salinity (OSS) from T–S mapping via a data-driven deep learning model J. Zhang et al. 10.1016/j.ocemod.2023.102232
- Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation A. Giganti et al. 10.3390/rs16213963
- Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations A. Wang et al. 10.1109/TGRS.2024.3509616
- OceanVP: A HYCOM based benchmark dataset and a relational spatiotemporal predictive network for oceanic variable prediction Z. Shi et al. 10.1016/j.oceaneng.2024.117748
1 citations as recorded by crossref.
Latest update: 23 Apr 2025
Short summary
A high-resolution gridded dataset is crucial for understanding ocean processes at various spatiotemporal scales. Here we used a machine learning approach and successfully reconstructed a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 (monthly) by merging in situ salinity profile observations with high-resolution satellite remote-sensing data. This new product could be useful in various applications in ocean and climate fields.
A high-resolution gridded dataset is crucial for understanding ocean processes at various...
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