Abstract
In this study, we investigate the relative ability of least square algorithm to retrieve sea surface salinity (SSS) from MODIS satellite data. We also examine with comprehensive comparison of the root mean square of bias the difference between second polynomial order algorithm and least square algorithm. Both the least squares algorithm and second polynomial order algorithm are used to retrieve the sea surface salinity (SSS) from multi MODIS bands data. Thus, the basic linear model has been solved by using second polynomial order algorithm and least square estimators. The accuracy of this work has been examined using the root mean square of bias of sea surface salinity retrieved from MODIS satellite data and the in situ measurements that are collected along the east coast of Peninsular Malaysia by using hydrolab instrument. The study shows comprehensive relationship between least square method and in situ SSS measurements with high r2 of 0.96 and RMS of bias value of ±0.37 psu. The second polynomial order algorithm, however, has lower performance as compared to least square algorithm. Thus, RMS of bias value of ± 7.34 psu has performed with second polynomial order algorithm. In conclusions, the least square algorithm can be used to retrieve SSS from MODIS satellite data.
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Marghany, M., Hashim, M., Cracknell, A.P. (2010). Modelling Sea Surface Salinity from MODIS Satellite Data. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2010. ICCSA 2010. Lecture Notes in Computer Science, vol 6016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12156-2_41
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DOI: https://doi.org/10.1007/978-3-642-12156-2_41
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