Improvements to PhytoDOAS method for identification of major phytoplankton groups using high spectrally resolved data.

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Sadeghi, A. , Dinter, T. , Vountas, M. , Taylor, B. and Bracher, A. (2010): Improvements to PhytoDOAS method for identification of major phytoplankton groups using high spectrally resolved data. , COSPAR 38th COSPAR Scientific Assembly, Bremen, GermanyJuly 2010. .
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The goal of this study is to improve the retrieval method of identification of major Phytoplankton Functional Types (PFTs) using ocean-color data provided by a high spectrally-resolved satellite sensor, SCIAMACHY(Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) on board ENVISAT. The method under improvement, so-called Phyto-DOAS, is an extended exploitation of DOAS (Differential Optical Absorption Spectroscopy), originally developed to retrieve atmospheric trace gases, into remote identification of oceanic phytoplankton.The main challenge for retrieving a specific Phytoplankton target by Phyto-DOAS is to overcome the overlapping effects of different PFTs absorption spectra which are living -in different ratios- together with that target in ocean waters. Phytoplankton absorption spectra are specified by the types and concentrations of pigment assemblages they are composed of and the mentioned correlation effect is due to common pigments among different PFTs, e.g., Chl-a. So far Phyto-DOAS has been successfully exploited to identify Cyanobacteria and Diatom over global ocean (Bracher et al. 2009).In this study two ideas have been implemented for the purpose of PFTs retrieval improvement: firstly using the method of fourth-derivative spectroscopy (Aguirre-Gomez et al. 1995) some spectral analysis are done to reach the peak positions of main pigment components in each absorption spectrum. Then comparing the results of major PFTs the optimized fit-window for DOAS-retrieval of each PFT is determined. Secondly the simultaneous fitting of different PFTs have been implemented (over the year 2008) to include the real oceanic situation more into the retrieval. In this step the provided optimized fit-windows have been tested for better results. Validation of the global ocean results have been followed using several in-situ data sets obtained through several cruises of Polarstern research vessel in the year 2008.

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