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    <identifier>10.57760/sciencedb.02053</identifier>
    <datestamp>2022-07-26T09:46:07Z</datestamp>
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  <dc:date>2022-07-26</dc:date>
  <dc:title>Datasets of reconstructed sea surface salinity in the South China Sea</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.02053</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Salinity is one of the basic physical properties of seawater that can be used to trace different water masses and to infer chemical behaviors in the ocean, especially in marginal seas. Limited by the coverage of direct observations, however, available salinity data are lacking both spatially and temporally high resolutions. To supplement the salinity database, this paper reconstructed sea surface salinity (SSS) with a reasonably high spatial resolution (0.05&amp;ordm;&amp;times;0.05&amp;ordm;) over 2003-2020 in the South China Sea (SCS) with a machine learning algorithm using MODIS-Aqua remote sensing data and observational SSS data.</dc:description>
  <dc:subject>South china Sea; Salinity; Machine learning</dc:subject>
  <dc:creator>Zhixuan Wang</dc:creator>
  <dc:creator>Guizhi Wang</dc:creator>
  <dc:creator>Minhan Dai</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.3390/rs14236147</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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