Ahmed El-Habashi received his MS and PhD degrees in electrical engineering from the City University of New York. His thesis explored ocean color algorithm development using machine learning techniques, as well as the light-polarization properties of oceanic coastal waters and their relationship to fluorescence. He currently holds an NRC Research Associateship award at the U.S. Naval Research Laboratory in Washington, DC. His research area focuses on retrievals of seawater optical properties using measured polarized light filed and vector radiative transfer codes.
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We then compare these algorithms with a multiband retrieval algorithm that was developed using neural networks (NN) and which was trained on simulated data generated through bio-optical modeling typical for a broad range of coastal water parameters, including those known for the Chesapeake Bay. This NN algorithm was then applied to our field measurements and used to retrieve the phytoplankton absorption at 443nm which was then related to [Chl-a]. In this process, special attention was paid to field data consistency in terms of both measured reflectance and [Chl-a] values, to avoid undesirable biases and trends. All algorithm retrievals were finally evaluated by several statistical indicators to arrive at their relative merits and potential for further improvements and application to satellite data.
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