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To make up for the ocean interior data shortage and entirely use the abundant satellite data, we developed a data-driven deep learning model named Convformer to reconstruct ocean subsurface temperature and salinity fields from satellite-observed sea surface data. Convformer is designed by deeply optimizing Vision Transformer and ConvLSTM, consisting of alternating residual connections between multiple temporal and spatial attention blocks. The input variables consist of sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). Our results demonstrate that Convformer exhibits superior performance in estimating the temperature-salinity structure of the tropical Pacific Ocean. The all-depth average root mean square error (RMSE) of the reconstructed subsurface temperature (ST)\/subsurface salinity (SS) is 0.353 \u00b0C\/0.0695 PSU, with correlation coefficients (R\u00b2) of 0.98663\/0.99971. In the critical thermocline, although the root mean square errors of ST and SS reach 0.85 \u00b0C and 0.121 PSU, respectively, they remain smaller compared to other models. Furthermore, we assessed Convformer\u2019s performance from various perspectives. Notably, we also delved into the potential of Convformer to extract physical and dynamic information from a model mechanism perspective. Our study offers a practical approach to reconstructing the subsurface temperature and salinity fields from satellite-observed sea surface data.<\/jats:p>","DOI":"10.3390\/rs16132422","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T10:14:46Z","timestamp":1719828886000},"page":"2422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Convformer: A Model for Reconstructing Ocean Subsurface Temperature and Salinity Fields Based on Multi-Source Remote Sensing Observations"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0130-3340","authenticated-orcid":false,"given":"Tao","family":"Song","sequence":"first","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Beijing 100081, China"}]},{"given":"Guangxu","family":"Xu","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Beijing 100081, China"}]},{"given":"Kunlin","family":"Yang","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Beijing 100081, China"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Shiqiu","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"ref_1","unstructured":"Stewart, R.H. 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