TY - JOUR
T1 - Remotely sensed sea surface salinity in the hyper-saline Arabian Gulf
T2 - Application to landsat 8 OLI data
AU - Zhao, Jun
AU - Temimi, Marouane
AU - Ghedira, Hosni
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/3/5
Y1 - 2017/3/5
N2 - 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.
AB - 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.
KW - Arabian Gulf
KW - Landsat 8
KW - Multivariable regression
KW - Ocean color
KW - Sea surface salinity
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U2 - 10.1016/j.ecss.2017.01.008
DO - 10.1016/j.ecss.2017.01.008
M3 - Article
AN - SCOPUS:85010008969
SN - 0272-7714
VL - 187
SP - 168
EP - 177
JO - Estuarine, Coastal and Shelf Science
JF - Estuarine, Coastal and Shelf Science
ER -