Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model

Jagath Kumara Rajapaksha

Abstract


Remote sensing techniques have been widely used for monitoring and measuring of the uppermost layer of the ocean. A methodology has been developed to predict the temperature vertical profiles of the ocean using satellite derived sea surface temperature (SST) and sea surface height (SSH). SSH have shown significant linear relationships with latitudinal thermicline depths. Linear relationships were also found among the model parameters. Mixed layer temperature was obtained by a linear relationship using SST. Having all the relationships, temperature vertical profiles prediction using satellite data were discussed. Thermal structure of the oceans have wider applications in oceanography and fisheries. This model can be used to generate isothermal subsurface temperatures of depth to 500 m. On the other hand, if a particular species tuna fish swim in 21 oC, the depth of that isothermal surface can be predicted. The output of this model can support for deep sea fishing industry.

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