This work presents a new approach for modeling sea surface salinity (SSS) from MODIS satellite data. In doing so, the least squares algorithm is used to retrieve SSS from multi MODIS bands data. Thus, the basic linear model has been solved by using least square estimators. In situ measurements are collected along the east coast of peninsular Malaysia by using hydrolab instrument. The study shows that homogenous offshore sea surface salinity as compared with onshore SSS variations. The onshore SSS are ranged between 28.5 and 29.5 psu whereas the offshore SSS variations have maximum value of 33.8 psu. The results also show a good correlation between in situ SSS measurements and the SSS that is retrieved from MODIS satellite data with high r2 of 0.97 and RMS of bias value of ±0.37 psu. It can be said that least squares method can be used to provide a new algorithm for SSS retrieval from MODIS satellite data.
Key words: Sea surface salinity, moderate-resolution imaging spectrometer satellite data, least square estimators, linear model.
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