Remotely sensed sea surface salinity in the hyper-saline Arabian Gulf: Application to landsat 8 OLI data. (5th March 2017)

• Record Type: Journal Article
• Authors: Zhao, Jun; Temimi, Marouane; Ghedira, Hosni
• Is Part Of: Estuarine, coastal and shelf science. Volume 187(2017)
• Page Start: 168
• Page End: 177
• Publication Date: 2017-03-05
• Subjects: Landsat 8 -- Arabian Gulf -- Sea surface salinity -- Ocean color -- Multivariable regression; Estuarine oceanography -- Periodicals; Coasts -- Periodicals; Estuarine biology -- Periodicals; Seashore biology -- Periodicals; Coasts; Estuarine biology; Estuarine oceanography; Seashore biology; Periodicals; 551.461805
• DOI: 10.1016/j.ecss.2017.01.008
• Languages: English
• ISSNs: 0272-7714

Abstract

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 R 2 s 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.