{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T14:25:50Z","timestamp":1763389550415,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An algorithmic approach, based on satellite-derived sea-surface (\u201cskin\u201d) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin salinities into comparable in-situ (\u201cbulk\u201d) salinities for the top-5 m of the subpolar and Arctic Oceans. In preparation for routine assimilation into operational ocean forecast models, Soil Moisture Active Passive (SMAP) satellite Level-2 SSS observations are transformed using Argo float data from the top-5 m of the ocean to address the mismatch between the skin depth of satellite L-band SSS measurements (\u223c1 cm) and the thickness of top model layers (typically at least 1 m). Separate from the challenge of Argo float availability in most of the subpolar and Arctic Oceans, satellite-derived SSS products for these regions currently are not suitable for assimilation for a myriad of other reasons, including erroneous ancillary air-sea forcing\/flux products. In the subpolar and Arctic Oceans, the root-mean-square error (RMSE) between the SMAP SSS product and several in-situ salinity observational data sets for the top-5 m is greater than 1.5 pss (Practical Salinity Scale), which can be larger than their temporal variability. Thus, we train a machine-learning algorithm (called a Generalized Additive Model) on in-situ salinities from the top-5 m and an independent air-sea forcing\/flux product to convert the SMAP SSS into bulk-salinities, correct biases, and quantify their standard errors. The RMSE between these corrected bulk-salinities and in-situ measurements is less than 1 pss in open ocean regions. Barring persistently problematic data near coasts and ice-pack edges, the corrected bulk-salinity data are in better agreement with in-situ data than their SMAP SSS equivalent.<\/jats:p>","DOI":"10.3390\/rs14061418","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T03:36:23Z","timestamp":1647401783000},"page":"1418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8222-7214","authenticated-orcid":false,"given":"David","family":"Trossman","sequence":"first","affiliation":[{"name":"Global Science & Technology, NOAA\/NESDIS Center for Satellite Applications and Research (STAR), Greenbelt, MD 20770, USA"},{"name":"Department of Oceanography & Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA"},{"name":"Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA"}]},{"given":"Eric","family":"Bayler","sequence":"additional","affiliation":[{"name":"NOAA\/NESDIS Center for Satellite Applications and Research (STAR), College Park, MD 20740, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3857","DOI":"10.1002\/2016GL068822","article-title":"Consistency of Aquarius sea surface salinity with Argo products on various spatial and temporal scales","volume":"43","author":"Lee","year":"2016","journal-title":"Geophys. 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