Remotely sensed sea surface salinity in the hyper-saline Arabian Gulf: Application to landsat 8 OLI data

Jun Zhao, Marouane Temimi, Hosni Ghedira

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

In this study, a multivariable linear algorithm was developed to derive sea surface salinity (SSS) from remote sensing reflectance (Rrs) in the hyper-saline Arabian Gulf. In situ measured Rrs at Operational Land Imager (OLI) bands 1–4 were involved in the algorithm development. Comparisons between estimated and in situ measured SSS produced R2s reaching 0.74 and RMSEs <2%. The proposed algorithm was applied to OLI scenes collected in November 2013 and March 2016 to demonstrate SSS changes from normal conditions when extreme events were encountered. The good agreement between satellite-derived and in situ Rrs suggested that the algorithm uncertainties were primarily attributed to the algorithm parameterization and more measurements were required for performance improving. Compared with OLI-derived products, numerical simulations overestimated SSS by 3.4%. Our findings demonstrate the potential of high resolution satellite products to study short-lasting events and capture fine-scale features in the marine environment.

Original languageEnglish
Pages (from-to)168-177
Number of pages10
JournalEstuarine, Coastal and Shelf Science
Volume187
DOIs
StatePublished - 5 Mar 2017

Keywords

  • Arabian Gulf
  • Landsat 8
  • Multivariable regression
  • Ocean color
  • Sea surface salinity

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