Modeling in situ phytoplankton absorption from total absorption spectra in productive inland marine waters

Roesler, Collin S.; Perry, Mary Jane; Carder, Kendall L.

doi:10.4319/lo.1989.34.8.1510

Cited by 401 publications

(310 citation statements)

References 31 publications

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“…In particular, we adopted G 1 and G 2 from Gordon et al [21] to be consistent with previously published work [3][4][5][7][8][9] and variable S bp and a ϕ λ to better represent heterogeneous natural environments than would be possible with fixed values. Our choice of a fixed S dg represents a compromise between multiple published values [8,24], although we expect GIOP-DC to ultimately adopt a variable S dg as methods for its dynamic calculation improve. Note, we did not make these choices based on comparisons with in situ or synthetic data sets, nor do we suggest that these parameterizations represent all natural oceanic conditions at all times.…”

Section: B Model Configurationmentioning

confidence: 99%

“…where S d and S g typically vary between 0.01 and 0.02 nm −1 in natural waters [24]. As the spectral shapes of NAP and CDOM absorption differ only in their exponential slopes, the two components are typically combined for satellite applications and Eq.…”

Section: A Model Developmentmentioning

confidence: 99%

“…(8) and S bp from Lee et al [7] • a dg λ from Eq. (5) and S dg 0.018 nm −1 [24] • a ϕ λ from Bricaud et al [14], estimated using OC-derived C a [31] and normalized such that a ϕ 443 0.055 m 2 mg −1 .…”

Section: B Model Configurationmentioning

confidence: 99%

See 2 more Smart Citations

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

367

319

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Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

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Section: B Model Configurationmentioning

confidence: 99%

Section: A Model Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

367

319

View full text Add to dashboard Cite

show abstract

“…In order to invert reliably the remotely sensed ocean color to obtain the concentration of optically significant material, it is of major importance to determine CDOM distribution and its effect on ocean color. The spectral absorption by CDOM (here denoted by a a) at the visible wavelengths can be described, to a high degree of accuracy, by a single exponential or a sum of two exponentials that decrease with increasing wavelength Jerlov, 1968; Roesler et al, 1989]. As in previous studies, we use the term CDOM to denote colored material that passes through a 0.2 /•m filter, regardless of its origin and chemical composition.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variability of absorption by dissolved material at a continental shelf

Boss¹,

Pegau²,

Zaneveld³

et al. 2001

J. Geophys. Res.

120

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Abstract. Optical properties of dissolved (colored dissolved organic material (CDOM)) and particulate matter and hydrographic measurements were obtained at the Mid-Atlantic Bight during the fall of 1996 and the spring of 1997 as part of the Coastal Mixing and Optics experiment. To assess the temporal and spatial variability, time series were obtained at one location and cross-shelf transects were carried out. On short timescales, variability in the vertical distribution of the dissolved fraction was mostly due to highfrequency internal waves. This variability was conservative, resulting in no changes on isopycnals. Over longer periods and episodically, CDOM variability was dominated by storms. The storms were associated with sediment resuspension events and were accompanied by an increase in the absorption by the dissolved materials. Data from spatial transects show that near the bottom, over the shelf, and in both spring and fall, increased particulate absorption and increased CDOM absorption co-occur. These data support the hypothesis that bottom sediments can act as a source of dissolved organic carbon during sediment resuspension events.

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“…Corrections were made for detrital absorption using the theoretical approach proposed by Hoepffner & Sathyendranath (1993), which assumes an exponential shape for detrital absorption (Roesler et al 1989, Bricaud & Stramski 1990). According to this method, the total absorption by particles can be defined as:…”

Section: Methodsmentioning

confidence: 99%

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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Modeling in situ phytoplankton absorption from total absorption spectra in productive inland marine waters

Cited by 401 publications

References 31 publications

Generalized ocean color inversion model for retrieving marine inherent optical properties

Generalized ocean color inversion model for retrieving marine inherent optical properties

Spatial and temporal variability of absorption by dissolved material at a continental shelf

Absorption coefficient of phytoplankton:regional variations in the North Atlantic

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