The use of Neural Networks for the estimation of oceanic constituents based on the MERIS instrument

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Abstract
Artificial Neural Networks (NNs) are used in estimations of oceanic constituents from simulated data for the Mechron Resolution Imaging Spectrometer (MERIS) instrument system for Case II water applications. The simulation includes the effects of oceanic substances such as algal related chlorophyll, non-chlorophyllous suspended matter and DOM (dissolved organic matter). It is shown here that NNs can be used to estimate oceanic constituents based on simulated data which include the effects of realistic noise and variability models. The advantage of NNs is that they not only achieve higher retrieval accuracy than more traditional techniques such as band ratio algorithms, but they also allow the inclusion of usually superfluous or unused information, such as geometric parameters and atmospheric visibility.

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