Light backscattering properties of marine phytoplankton: relationships to cell size, chemical composition and taxonomy

Vaillancourt, Robert D.; Brown, Christopher W.; Guillard, Robert R. L.; Balch, William M.

doi:10.1093/plankt/fbh012

Cited by 173 publications

(153 citation statements)

References 62 publications

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“…In case 1 waters, 90% of the Q bbe values range between 0.004 and 0.034 with a median value of 0.010 (see Table 2). Vaillancourt et al (2004) report Q bbe factors at l 5 620 nm between 0.001 and 0.068, with a mean of 0.011, for 28 phytoplankton cultures with diameters from 1.4 to 35 mm. This is in good agreement with our case 1 observations.…”

Section: Resultsmentioning

confidence: 99%

“…Such relationships were derived from in situ measurements, whereas our present understanding of the effect of particle characteristics on IOPs is mainly based on laboratory measurements and theoretical calculations. For instance, studies investigating optical variability of phytoplankton cells with cell size and refractive index (Morel and Bricaud 1986;Ahn et al 1992;Vaillancourt et al 2004) or induced by environmental factors (Stramski and Morel 1990;Stramski et al 2002) were performed in laboratories. Other laboratory studies focused on optical variability of mineral particles (Volten et al 1998;Stramski et al 2007).…”

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confidence: 99%

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In situ variability of mass‐specific beam attenuation and backscattering of marine particles with respect to particle size, density, and composition

Neukermans

Loisel

Mériaux

et al. 2011

Limnology & Oceanography

181

179

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This study analyzes relationships between concentration of suspended particles represented by dry mass, [SPM], or area, [AC], and optical properties including particulate beam attenuation (c p ), side scattering (b s ), and backscattering (b bp ), obtained from an intensive sampling program in coastal and offshore waters around Europe and French Guyana. First-order optical properties are driven by particle concentration with best predictions of [SPM] by b bp and b s , and of [AC] by c p . Second-order variability is investigated with respect to particle size, apparent density (dry weight-to-wet-volume ratio), and composition. Overall, the mass-specific particulate backscattering coefficient, b bp remains unexplained. Possible causes are the limitation of the measured size distributions to the 2-302-mm range and effects of particle shape and internal structure that affect b bp more than c p and were not accounted for.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

In situ variability of mass‐specific beam attenuation and backscattering of marine particles with respect to particle size, density, and composition

Neukermans

Loisel

Mériaux

et al. 2011

Limnology & Oceanography

181

179

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“…The use of b bp (λ) makes the approach less sensitive to physiological variability than other approaches. However, the particle size classes include all particles, not just phytoplankton and the relationship between b bp (λ) and phytoplankton cell size is still a matter of active debate (Stramski et al, 2004;Vaillancourt et al, 2004;Dall'Olmo et al, 2009;Whitmire et al, 2010). In addition, the sources of backscattering are still uncertain (Stramski et al, 2004) and applicability of Mie theory to particles and/or phytoplankton assemblages in seawater has its limitations (e.g., Dall'Olmo et al, 2009).…”

Section: Algorithm Selectionmentioning

confidence: 99%

A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean

et al. 2017

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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.

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“…On the other hand, the spectral shape of the particle backscattering coefficient b bp (λ) is known to vary with the mean size of suspended particles in the water (e.g. Bricaud and Morel, 1986;Stramski et al, 2001;Vaillancourt et al, 2004). Based on the characterisitic, Loisel et al (2006) used remotely sensed spectral slope of b bp (λ), γ , as a particle size index and mapped global distribution of γ using SeaWiFS imagery.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of size structure of phytoplankton communities using optical properties of the Chukchi and Bering Sea shelf region

et al. 2011

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Abstract. Recent ocean warming and subsequent sea ice decline resulting from climate change could affect the northward shift of the ecosystem structure in the Chukchi Sea and Bering Sea shelf region (Grebmeier et al., 2006b). The size structure of phytoplankton communities provides an index of trophic levels that is crucial to understanding the mechanisms underlying such ecosystem changes and their implications for the future. This study proposes a new ocean color algorithm for deriving this characteristic by using the region's optical properties. The size derivation model (SDM) estimates the phytoplankton size index F L on the basis of sizefractionated chlorophyll-a (chl-a) using the light absorption coefficient of phytoplankton, a ph (λ), and the backscattering coefficient of suspended particles including algae, b bp (λ). F L was defined as the ratio of algal biomass attributed to cells larger than 5 µm to the total. It was expressed by a multiple regression model using the a ph (λ) ratio, a ph (488)/a ph (555), which varies with phytoplankton pigment composition, and the spectral slope of b bp (λ), γ , which is an index of the mean suspended particle size. A validation study demonstrated that 69 % of unknown data are correctly derived within F L range of ±20 %. The spatial distributions of F L for the cold August of 2006 and the warm August of 2007 were compared to examine application of the SDM to satellite remote sensing. The results suggested that phytoplankton size was responsive to changes in sea surface temperature. Further analysis of satellite-derived F L values and other environmental factors can advance our understanding of ecosystem structure changes in the shelf region of the Chukchi and Bering Seas.

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Light backscattering properties of marine phytoplankton: relationships to cell size, chemical composition and taxonomy

Cited by 173 publications

References 62 publications

In situ variability of mass‐specific beam attenuation and backscattering of marine particles with respect to particle size, density, and composition

In situ variability of mass‐specific beam attenuation and backscattering of marine particles with respect to particle size, density, and composition

A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean

Remote sensing of size structure of phytoplankton communities using optical properties of the Chukchi and Bering Sea shelf region

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