Abstract
Phytoplankton are composed of diverse taxonomical groups, which are manifested as distinct morphology, size and pigment composition. These characteristics, modulated by their physiological state, impact their light absorption and scattering, allowing them to be detected with ocean color satellite radiometry. There is a growing volume of literature describing satellite algorithms to retrieve information on phytoplankton composition in the ocean. This synthesis provides a review of current methods and a simplified comparison of approaches. The aim is to provide an easily comprehensible resource for non-algorithm developers, who desire to use these products, thereby raising the level of awareness and use of these products and reducing the boundary of expert knowledge needed to make a pragmatic selection of output products with confidence. The satellite input and output products, their associated validation metrics, as well as assumptions, strengths and limitations of the various algorithm types are described, providing a framework for algorithm organization to assist users and inspire new aspects of algorithm development capable of exploiting the higher spectral, spatial and temporal resolutions from the next generation of ocean color satellites.
Highlights
The determination of phytoplankton community structure using satellite remote sensing has evolved from an aspiration to a highly active area of research, with numerous published approaches available over the past decade
phytoplankton size classes (PSC), phytoplankton taxonomic composition (PTC), and particle size distribution (PSD) serve as a further refinement of phytoplankton functional types” (PFT), where the choice of the considered functional type depends on the question at hand
While there are a variety of algorithm approaches, all agree on broad understanding of PFT distribution at large spatial-temporal scales, that are forced mainly by bathymetry and climatic regions
Summary
The determination of phytoplankton community structure using satellite remote sensing has evolved from an aspiration to a highly active area of research, with numerous published approaches available over the past decade. Hirata et al (2008) do not use the Ciotti et al (2002) construct (i.e., Equation 4) that utilizes multiple wavelengths to characterize the spectral shape of aph(λ) Instead, they identify a tight relationship between the magnitude of phytoplankton absorption at a single wavelength [aph(443)], related to [Chl], and the slope of aph(443) to aph(510), which is influenced by pigment packaging and composition. They identify a tight relationship between the magnitude of phytoplankton absorption at a single wavelength [aph(443)], related to [Chl], and the slope of aph(443) to aph(510), which is influenced by pigment packaging and composition When this approach is applied to satellite data, it only uses aph(443), and determines dominate size class using boundaries in aph(443). PSD and bbp have a power-law shape Relative proportions of biovolume to total particulate volume are roughly constant across classes
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