Effect of pigment composition on absorption properties of phytoplankton

Nicolas, Hoepffner; Sathyendranath, Shubha

doi:10.3354/meps073011

Cited by 207 publications

(172 citation statements)

References 35 publications

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“…The values of pm,,(12), representing absorption by chl a at 674 nm, [ranging from 0.021 m-' (mg chl a m-')-' for Group A to 0.037 m-' (mg chl a m-3)-' for Group B] are higher than those previously reported for the in vivo specific coefficient of chl a at that wavelength [-0.020 m-' (mg chl a m-3)-'; Bricaud et al 1983, Bidigare et al 1987, 1990, Hoepffner & Sathyendranath 1991. These higher values of pa,(12) might arise from various causes: (1) the estimation of p,(i) depends on the low values of ab~n:ptlc:?, 2nd in ssme C S S~C , (~~~~~.…”

Section: Factors Influencing the Absorption Coefficientcontrasting

confidence: 57%

“…Several new methods have been proposed to estimate the absorption coefficient of phytoplankton based on the concentrations of all major pigments instead of just chl a (Bidigare et al 1987, Bidigare et al 1989, Bidigare et al 1990. Hoepffner & Sathyendranath 1991.…”

Section: Introductionmentioning

confidence: 99%

“…In the regional approach, the absorption spectra are calculated using parameters specific for each region, whereas in the general approach a single set of general parameters is used to estimate the shape and magnitude of the absorption spectra of all the samples. Two methods of estimating the absorption coefficient are tested for each approach: (1) a 'single-pigment' method based on the use of a fixed shape of the spectrum which is scaled according to the concentration of chl a (Prieur & Sathyendranath 1981); and (2) a 'multi-pigment' method based on the use of the relationship between the concentrations of major pigments (chlorophylls a, b, c and carotenoids) and absorption at different wavelengths (Hoepffner & Sathyendranath 1991).…”

Section: Introductionmentioning

confidence: 99%

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Absorption coefficient of phytoplankton:regional variations in the North Atlantic

Lutz¹,

Sathyendranath²,

Head³

1996

Mar. Ecol. Prog. Ser.

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The effect of major pigments on the absorption characteristics of phytoplankton were evaluated by comparing the in vjvo absorption spectra of phytoplankton with pigment data obtained using high-performance liqu~d chromatography (HPLC) for samples of phytoplankton collected in the northwestern Atlantic Ocean, during spring. Four groups of spectra having distinct absorption characteristics and well-defined regional distributions were identified. The result of using general parameters to estimate the absorption coefficient of phytoplankton from any region, the 'general approach', was compared with the result of using specific parameters for each region, or the 'regional approach' Two methods of estlrnating the absorption coefficient were tested for each approach. These were: a 'slnglepigment' method (Prieur & Sathyendranath 1981, Limnol Oceanogr 26:671-689), and a 'multi-pigment' method (Hoepffner & Sathyendranath 1991, Mar Ecol Prog Ser 73:ll-23). It was shown that the regional approach performed better (<10% average error) than the general approach (~2 0 % average error). Nevertheless, the results given by the general approach could be acceptable for some applications. The multi-pigment and the single-pigment methods performed equally well in the context of the reglonal approach, whereas in the general approach, the s~ngle-pigment method gave better results than the multi-pigment method. Overall, it appears that part~tioning the oceans into regions according to their absorption characteristics should improve regional models of light transmission and the algorithms for detection of pigments by remote sensing.

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Section: Factors Influencing the Absorption Coefficientcontrasting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Absorption coefficient of phytoplankton:regional variations in the North Atlantic

Lutz¹,

Sathyendranath²,

Head³

1996

Mar. Ecol. Prog. Ser.

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“…The shape of phytoplankton light absorption spectra results from absorption of individual pigments contained in the cell, where the pigment composition is genetically fixed and the concentrations in the cell are influenced by photoacclimation [29]. Due to the genetically fixed pigment composition, taxonomically different algal groups of phytoplankton can be differentiated by analyzing spectral absorption properties.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Differentiation of Phytoplankton Taxonomic Groups: A Comparison between Using Remote Sensing Reflectance and Absorption Spectra

Hieronymi

Röttgers

et al. 2015

Remote Sensing

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Abstract:The emergence of hyperspectral optical satellite sensors for ocean observation provides potential for more detailed information from aquatic ecosystems. The German hyperspectral satellite mission EnMAP (enmap.org) currently in the production phase is supported by a project to explore the capability of using EnMAP data and other future hyperspectral data from space. One task is to identify phytoplankton taxonomic groups. To fulfill this objective, on the basis of laboratory-measured absorption coefficients of phytoplankton cultures (aph(λ)) and corresponding simulated remote sensing reflectance spectra (Rrs(λ)), we examined the performance of spectral fourth-derivative analysis and clustering techniques to differentiate six taxonomic groups. We compared different sources of input data, namely aph(λ), Rrs(λ), and the absorption of water compounds obtained from inversion of the Rrs(λ)) spectra using a quasi-analytical algorithm (QAA). Rrs(λ) was tested as it can be directly obtained from hyperspectral sensors. The last one was tested as expected influences of the spectral features of pure water absorption on Rrs(λ) could be avoided after subtracting it from the inverted total absorption. Results showed that derivative analysis of measured aph(λ) spectra performed best with only a few misclassified cultures. Based on Rrs(λ) spectra, the accuracy of this differentiation decreased but the OPEN ACCESSRemote Sens. 2015, 7 14782 performance was partly restored if wavelengths of strong water absorption were excluded and chlorophyll concentrations were higher than 1 mg•m −3 . When based on QAA-inverted absorption spectra, the differentiation was less precise due to loss of information at longer wavelengths. This analysis showed that, compared to inverted absorption spectra from restricted inversion models, hyperspectral Rrs(λ) is potentially suitable input data for the differentiation of phytoplankton taxonomic groups in prospective EnMAP applications, though still a challenge at low algal concentrations.

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“…SeaWiFS, to determine Chl a concentrations). The methodology used is currently applicable for highlight subtropical waters of optical classification oceanic I-III (Jerlov 1976), where absorption of light by photosynthetic accessory pigments is presumably very small (Bidigare et al 1990a;Hoepffner and Sathyendranath 1991). Because high-light-adapted phytoplankton generally contain blue wavelength-absorbing photoprotective accessory pigments, use of this methodology is limited to the red absorption peak.…”

mentioning

confidence: 99%