Reconstruction of Three-Dimensional Temperature and Salinity in the Equatorial Ocean with Deep-Learning
Ocean temperature and salinity are core elements influencing ocean dynamics and biogeochemical cycles, critical to climate change and ocean process studies. In recent years, Argo floats and satellite remote sensing data have provided key support for observing and reconstructing three-dimensional (3D...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/12/2005 |
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| Summary: | Ocean temperature and salinity are core elements influencing ocean dynamics and biogeochemical cycles, critical to climate change and ocean process studies. In recent years, Argo floats and satellite remote sensing data have provided key support for observing and reconstructing three-dimensional (3D) ocean temperature and salinity. However, due to the challenges and high costs of in situ observations and the limitation of satellite measurements to surface data, effectively combining multi-source data to enhance the reconstruction accuracy of 3D temperature and salinity remains a significant challenge. In this study, we propose a VI-UNet model that incorporates a Vision Transformer module into UNet model and apply it to reconstruct 3D temperature and salinity in the equatorial oceans (20°S–20°N, 20°E–60°W) at depths from 1 to 6000 m using sea surface data acquired by satellites. In addition, we also investigate the impact of incorporating significant wave height (SWH) on the reconstruction of temperature and salinity. The results demonstrate that the VI-UNet model performs remarkably well in reconstructing temperature and salinity, achieving maximum reductions in root mean square error (RMSE) of up to 40% and 100%, respectively. Additionally, incorporating SWH enhances model accuracy, particularly in the upper 1000 m. |
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| ISSN: | 2072-4292 |