{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:33:36Z","timestamp":1783132416126,"version":"3.54.6"},"reference-count":53,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2018YFB0504905"],"award-info":[{"award-number":["2018YFB0504905"]}]},{"name":"the National Key R&amp;D Program of China","award":["42276203"],"award-info":[{"award-number":["42276203"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2018YFB0504905"],"award-info":[{"award-number":["2018YFB0504905"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42276203"],"award-info":[{"award-number":["42276203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea surface temperature (SST) is a key parameter in ocean hydrology. Currently, existing SST prediction methods fail to fully utilize the potential spatial correlation between variables. To address this challenge, we propose a spatiotenporal UNet (ST-UNet) model based on the UNet model. In particular, in the encoding phase of ST-UNet, we use parallel convolution with different kernel sizes to efficiently extract spatial features, and use ConvLSTM to capture temporal features based on the utilization of spatial features. Atrous Spatial Pyramid Pooling (ASPP) module is placed at the bottleneck of the network to further incorporate the multi-scale features, allowing the spatial features to be fully utilized. The final prediction is then generated in the decoding stage using parallel convolution with different kernel sizes similar to the encoding stage. We conducted a series of experiments on the Bohai Sea and Yellow Sea SST data set, as well as the South China Sea SST data set, using SST data from the past 35 days to predict SST data for 1, 3, and 7 days in the future. The model was trained using data spanning from 2010 to 2021, with data from 2022 being utilized to assess the model\u2019s predictive performance. The experimental results show that the model proposed in this research paper achieves excellent results at different prediction scales in both sea areas, and the model consistently outperforms other methods. Specifically, in the Bohai Sea and Yellow Sea sea areas, when the prediction scales are 1, 3, and 7 days, the MAE of ST-UNet outperforms the best results of the other three compared models by 17%, 12%, and 2%, and the MSE by 16%, 18%, and 9%, respectively. In the South China Sea, when the prediction ranges are 1, 3, and 7 days, the MAE of ST-UNet is 27%, 18%, and 3% higher than the best of the other three compared models, and the MSE is 46%, 39%, and 16% higher, respectively. Our results highlight the effectiveness of the ST-UNet model in capturing spatial correlations and accurately predicting SST. The proposed model is expected to improve marine hydrographic studies.<\/jats:p>","DOI":"10.3390\/rs16071205","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T06:33:16Z","timestamp":1711693996000},"page":"1205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Prediction of Sea Surface Temperature Using U-Net Based Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2505-7061","authenticated-orcid":false,"given":"Jing","family":"Ren","sequence":"first","affiliation":[{"name":"Colleage of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changying","family":"Wang","sequence":"additional","affiliation":[{"name":"Colleage of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Sun","sequence":"additional","affiliation":[{"name":"National Satellite Meteorological Center (National Centre for Space Weather), Beijing 100081, China"},{"name":"Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China"},{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, CMA, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoxiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Colleage of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Colleage of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiadong","family":"Mu","sequence":"additional","affiliation":[{"name":"Colleage of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Colleage of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"ref_1","first-page":"125","article-title":"The characteristics of the seasonal variability of the sea surface temperature field in the Bohai Sea, the Huanghai Sea and the East China Sea from AVHRR data","volume":"24","author":"Bao","year":"2002","journal-title":"Acta Oceanol. 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