{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T05:08:36Z","timestamp":1768453716647,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences","award":["2020LDE006"],"award-info":[{"award-number":["2020LDE006"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["421291282"],"award-info":[{"award-number":["421291282"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA 19060103"],"award-info":[{"award-number":["XDA 19060103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Meso- and fine-scale sea surface temperature (SST) is an essential parameter in oceanographic research. Remote sensing is an efficient way to acquire global SST. However, single infrared-based and microwave-based satellite-derived SST cannot obtain complete coverage and high-resolution SST simultaneously. Deep learning super-resolution (SR) techniques have exhibited the ability to enhance spatial resolution, offering the potential to reconstruct the details of SST fields. Current SR research focuses mainly on improving the structure of the SR model instead of training dataset selection. Different from generating the low-resolution images by downscaling the corresponding high-resolution images, the high- and low-resolution SST are derived from different sensors. Hence, the structure similarity of training patches may affect the SR model training and, consequently, the SST reconstruction. In this study, we first discuss the influence of training dataset selection on SST SR performance, showing that the training dataset determined by the structure similarity index (SSIM) of 0.6 can result in higher reconstruction accuracy and better image quality. In addition, in the practical stage, the spatial similarity between the low-resolution input and the objective high-resolution output is a key factor for SST SR. Moreover, the training dataset obtained from the actual AMSR2 and MODIS SST images is more suitable for SST SR because of the skin and sub-skin temperature difference. Finally, the SST reconstruction accuracies obtained from different SR models are relatively consistent, yet the differences in reconstructed image quality are rather significant.<\/jats:p>","DOI":"10.3390\/rs13183568","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T10:12:03Z","timestamp":1631095923000},"page":"3568","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution?"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2854-2261","authenticated-orcid":false,"given":"Bo","family":"Ping","sequence":"first","affiliation":[{"name":"School of Earth System Science, Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China"}]},{"given":"Yunshan","family":"Meng","sequence":"additional","affiliation":[{"name":"National Marine Data and Information Service, Tianjin 300171, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3605-6578","authenticated-orcid":false,"given":"Cunjin","family":"Xue","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, University of the Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4972-3595","authenticated-orcid":false,"given":"Fenzhen","family":"Su","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, University of the Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1175\/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2","article-title":"Improved global sea surface temperature analyses using optimum interpolation","volume":"7","author":"Reynolds","year":"1994","journal-title":"J. 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