{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:50:03Z","timestamp":1771548603917,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41976188"],"award-info":[{"award-number":["41976188"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2018YFC1406202"],"award-info":[{"award-number":["2018YFC1406202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The algorithms based on Surface Quasi-Geostrophic (SQG) dynamics have been developed and validated by many researchers through model products, however it is still doubtful whether these SQG-based algorithms are worth using in terms of observed data. This paper analyzes the factors impeding the practical application of SQG and makes amends by a simple \u201cfirst-guess (FG) framework\u201d. The proposed framework includes the correction of satellite salinity and the estimation of the FG background, making the SQG-based algorithms applicable in realistic circumstances. The dynamical-statistical method SQG-mEOF-R is thereafter applied to satellite data for the first time. The results are compared with two dynamical algorithms, SQG and isQG, and three empirical algorithms, multivariate linear regression (MLR), random forest (RF), and mEOF-R. The validation against Argo profiles showed that the SQG-mEOF-R presents a robust performance in mesoscale reconstruction and outperforms the other five algorithms in the upper layers. It is promising that the SQG-mEOF-R and the FG framework are applicable to operational reconstruction.<\/jats:p>","DOI":"10.3390\/rs13245085","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T22:06:10Z","timestamp":1639519570000},"page":"5085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Practical Dynamical-Statistical Reconstruction of Ocean\u2019s Interior from Satellite Observations"],"prefix":"10.3390","volume":"13","author":[{"given":"Hengqian","family":"Yan","sequence":"first","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Hunan 410073, China"}]},{"given":"Ren","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Hunan 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4334-3882","authenticated-orcid":false,"given":"Huizan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Hunan 410073, China"}]},{"given":"Senliang","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Hunan 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6058-666X","authenticated-orcid":false,"given":"Chengzu","family":"Bai","sequence":"additional","affiliation":[{"name":"Beijing Institute of Applied Meteorology, Beijing 100029, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pocean.2013.11.010","article-title":"Subsurface and Deeper Ocean Remote Sensing from Satellites: An Overview and New Result","volume":"122","author":"Klemas","year":"2014","journal-title":"Prog. 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