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Carbon Cycle & Ecosystems

The proposed research focuses on utilizing remote sensing to assess the diversity of phytoplankton communities within the open ocean. Our work will integrate (i) SeaWiFS-derived ocean color data, an in situ pigment database, and bio-optical and primary production modeling for estimating biomass and primary productivity associated with three phytoplankton classes, and (ii) combine proposed field measurements in the Atlantic Ocean with an existing comprehensive in situ dataset to develop hyperspectral algorithms for estimation of the phytoplankton community composition. Our proposal also involves a significant component of international collaboration with French and German scientists from the Laboratoire d'Oceanographie de Villefranche and the Alfred Wegener Institute for Polar and Marine Research. The proposed work is comprised of two major components. The first component extends an empirical approach to inferring the phytoplankton community composition in terms of three major pigment-based size classes from surface chlorophyll a concentration. Statistical relationships, derived from a large in situ data set of phytoplankton pigments collected from various oceanic regions, are used to estimate class-specific chlorophyll biomass from satellite-derived total surface chlorophyll a concentration. This information will be combined with a primary production model and class-specific physiological parameters to compute class-specific rates of primary production. By using the available times series of SeaWiFS ocean color data, we will assess, for the first time, the spatial patterns and temporal trends in class-specific biomass and class-specific primary production of phytoplankton communities within the global open ocean over the past decade. The second component of the proposed research will focus on the development of algorithms for assessing the composition of phytoplankton communities from present and future in situ, airborne, and satellite hyperspectral sensors. Two extensive sets of data will be analyzed; an existing data set containing a comprehensive suite of hyperspectral optical measurements along with biodiversity and biogeochemical data from the eastern South Pacific (BIOSOPE), and a similarly comprehensive new data set which we propose to acquire during a German expedition across the Atlantic. We will use various classification techniques to analyze the variability in hyperspectral data of ocean reflectance and inherent optical properties, and perform a detailed analysis of the sources of optical variability associated with both phytoplankton community composition and non-phytoplankton constituents of seawater. Our research objectives address Subgoal 3A of NASA's strategic goals, and also specifically address all elements of the Biodiversity solicitation. We anticipate that the results concerning satellite-derived class-specific biomass and primary production will have important implications for our understanding of pelagic ecosystems and ocean biogeochemistry. The analysis and development of hyperspectral algorithms addresses a need to explore the full potential of remote sensing for assessing phytoplankton diversity. Such research is strongly needed for marine systems which, compared to terrestrial systems, have been undersampled and underrepresented in terms of ecological and biodiversity data.

Project PI: Dariusz Stramski/University of California San Diego

Marine Physical Laboratory Scripps Institution of Oceanography University of California, San Diego La Jolla, CA 92093-0238, U.S.A.

Phone:  (858) 534 3353

Email: dstramski@ucsd.edu

http://www.mpl.ucsd.edu/people/dstramski/

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Started: Aug 09, 2010

Last Activity: Dec 10, 2010

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