Variations of light absorption by suspended particles with chlorophyll <i>a</i> concentration in oceanic (case 1) waters: Analysis and implications for bio‐optical models

Bricaud, Annick; Morel, A.; Babin, Marcel; Allali, Karima; Claustre, Hervé

doi:10.1029/98jc02712

Cited by 605 publications

(636 citation statements)

References 46 publications

(38 reference statements)

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“…The latter normalization allows the spectral shape of a ϕ λ to change with an estimate of C a , but constrains its magnitude to an average value for oceanic water [3,8,14]. Our choice of default parameterizations served two purposes: to provide some consistency with previously developed SAAs and to acknowledge the emerging quality of variable, dynamically selected eigenvectors.…”

Section: B Model Configurationmentioning

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%

“…Briefly, GIOP allows construction of different IOP models at runtime by selection from a wide assortment of published absorption and backscattering eigenvectors [4,[6][7][8][14][15][16][17]. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of new SAAs, development of regionally tuned SAAs (e.g., [18]), and ensemble inversion modeling.…”

Section: Introductionmentioning

confidence: 99%

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Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

366

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: B Model Configurationmentioning

confidence: 99%

Section: Introductionmentioning

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.

366

319

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“…It follows from equation (12) that the spectral reflectance of whitecaps depends strongly on the spectral absorption coefficient a of water. We stress that values of natural water absorption coefficient can vary considerably because of the presence of dissolved and particulate matter [Bricaud et al, 1998]. This causes the variation of the foam spectral reflectance across the ocean.…”

Section: Experiments In the Surf Zone And Open Oceanmentioning

confidence: 99%

Spectral reflectance of whitecaps

Kokhanovsky

2004

J. Geophys. Res.

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[1] Spectral properties of whitecaps are of importance for color ocean remote sensing and aerosol optical thickness probing from satellite-based instruments. They also influence planetary albedo and climate. In particular, whitecaps may affect the response of the climate system to changes in greenhouse gases and other atmospheric constituents. Several experimental measurements of whitecap spectral reflectance have been performed both in the surf zone and in the open ocean, which indicate that oceanic foam cannot be considered as a gray body (e.g., for satellite remote sensing techniques). This paper is devoted to the interpretation of experiments performed in terms of the radiative transfer theory. Only the case of a semi-infinite foam is studied in detail. However, results can be easily extended to the case of finite foamed media having large optical thickness. The model introduced is capable of explaining main features observed, like a sharp decrease of the foam spectral reflectance in the infrared as compared with the visible part of the electromagnetic spectrum and a high correlation of the foam reflectance R and the water absorption coefficient a. A simple method to retrieve the spectral dependence of a from the spectral foam reflectance R is proposed.

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“…Morel, 1988Morel, , 2009). Previous studies have noted that there are, however, discrepancies with this approach (Bricaud et al, 1998;Siegel et al, 2005a, b;Brown et al, 2008). In fact, the differences in how CDOM and detrital matter absorb and scatter light has been used in algorithm development (e.g.…”

Section: Discussion and Summarymentioning

confidence: 99%

Modelling ocean-colour-derived chlorophyll <i>a</i>

2018

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Abstract. This article provides a proof of concept for using a biogeochemical/ecosystem/optical model with a radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour chlorophyll a (Chl a) product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real-world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model) water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl a (called "derived Chl a" in this paper). We compare the derived Chl a to the "actual" model Chl a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl a. The real-world algorithm is found using concurrent in situ measurement of Chl a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. Not surprisingly, we find that regionspecific algorithms provide a significant improvement, at least in the annual mean. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl a and the actual Chl a. These mismatches stem from temporal decoupling between Chl a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter). The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation and peak of the spring bloom in the derived Chl a lags the actual Chl a by days and sometimes weeks. These results indicate that care should also be taken when studying phenology through satellite-derived products of Chl a. This study also reemphasizes that ocean-colourderived Chl a is not the same as the real in situ Chl a. In fact the model derived Chl a compares better to real-world satellite-derived Chl a than the model actual Chl a. Modellers should keep this is mind when evaluating model output with ocean colour Chl a and in particular when assimilating this product. Our goal is to illustrate the use of a numerical laboratory that (a) helps users of ocean colour, particularly modellers, gain further understanding of the products they use and (b) helps the ocean colour community to explore other ocean colour products, their biases and uncertainties, as well as to aid in future algorithm development.

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Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: Analysis and implications for bio‐optical models

Cited by 605 publications

References 46 publications

Generalized ocean color inversion model for retrieving marine inherent optical properties

Generalized ocean color inversion model for retrieving marine inherent optical properties

Spectral reflectance of whitecaps

Modelling ocean-colour-derived chlorophyll <i>a</i>

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Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: Analysis and implications for bio‐optical models

Cited by 605 publications

References 46 publications

Generalized ocean color inversion model for retrieving marine inherent optical properties

Generalized ocean color inversion model for retrieving marine inherent optical properties

Spectral reflectance of whitecaps

Modelling ocean-colour-derived chlorophyll &lt;i&gt;a&lt;/i&gt;

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Product

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Modelling ocean-colour-derived chlorophyll <i>a</i>