Spectral variations of light scattering by marine particles in coastal waters, from the visible to the near infrared

Doxaran, David; Ruddick, Kevin; McKee, David; Gentili, Bernard; Tailliez, Dominique; Chami, Malik; Babin, Marcel

doi:10.4319/lo.2009.54.4.1257

Cited by 78 publications

(60 citation statements)

References 46 publications

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“…a SPM * measurements in the visible and NIR range had a large variability that was comparable to the range of values reported in the literature for temperate coastal waters (e.g., Mobile Bay, Elbe Estuary, Gironde Estuary) (Snyder et al, 2008;Doxaran et al, 2009;Röttgers et al, 2014) (Table 4). Also, our values in the blue spectral range are within the interval of variation (e.g., 0.024-0.031 m 2 g −1 , λ = 440 nm) of the linear regression slope between NAP absorption coefficient and SPM concentration values measured in mineralrich waters (i.e., a SPM * ∼ = a NAP * ) (Bowers et al, 1996;Babin et al, 2003).…”

Section: Spatial Variability Of Mass-normalized Optical Coefficientssupporting

confidence: 58%

“…This is remarkable given the large diversity of methodologies used by different research teams for estimating a SPM * values (e.g., pad technique, ac-s, integrating sphere). In general, the lowest a SPM * values (i.e., 0.01-0.02 m 2 g −1 at λ = 440 nm) commonly corresponded with samples obtained in very turbid environments (i.e., > 100 g m −3 , Mississippi River and Delta, Gironde River) (Bowers and Binding, 2006;D'Sa et al, 2006;Doxaran et al, 2009). These low values could be overestimated due to an increase on particulate absorption associated with an incomplete removal of multiple scattering effects.…”

Section: Spatial Variability Of Mass-normalized Optical Coefficientsmentioning

confidence: 99%

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Optical properties of size fractions of suspended particulate matter in littoral waters of Québec

Mohammadpour

Gagné

Larouche³

et al. 2017

Biogeosciences

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Abstract. Mass-specific absorption (a i * (λ)) and scattering (b i * (λ)) coefficients were derived for four size fractions (i = 0.2-0.4, 0.4-0.7, 0.7-10, and > 10 µm, λ = wavelength in nm) of suspended particulate matter (SPM) and with samples obtained from surface waters (i.e., 0-2 m depth) of the Saint Lawrence Estuary and Saguenay Fjord (SLE-SF) during June of 2013. For the visible-near-infrared spectral range (i.e., λ = 400-710 nm), mass-specific absorption coefficients of total SPM (i.e., particulates > 0.2 µm) (hereafter a SPM * ) had low values (e.g., < 0.01 m 2 g −1 at λ = 440 nm) in areas of the lower estuary dominated by particle assemblages with relatively large mean grain size and high particulate organic carbon and chlorophyll a per unit of mass of SPM. Conversely, largest a SPM * values (i.e., > 0.05 m 2 g −1 at λ = 440 nm) corresponded with locations of the upper estuary and SF where particulates were mineral-rich and/or their mean diameter was relatively small. The variability of two optical proxies (the spectral slope of particulate beam attenuation coefficient and the mass-specific particulate absorption coefficient, hereafter γ and Svis, respectively) with respect to changes in particle size distribution (PSD) and chemical composition was also examined. The slope of the PSD was correlated with b i * (550) (Spearman rank correlation coefficient ρ s up to 0.37) and a i * (440) estimates (ρ s up to 0.32) in a comparable way. Conversely, the contribution of particulate inorganic matter to total mass of SPM (F SPM PIM ) had a stronger correlation with a i * coefficients at a wavelength of 440 nm (ρ s up to 0.50). The magnitude of γ was positively related to F SPM i or the contribution of size fraction i to the total mass of SPM (ρ s up to 0.53 for i = 0.2-0.4 µm). Also, the relation between γ and F SPM PIM variability was secondary (ρ s = −0.34, P > 0.05). Lastly, the magnitude of Svis was inversely correlated with a SPM * (440) (ρ s = −0.55, P = 0.04) and F SPM PIM (ρ s = −0.62, P = 0.018) in sampling locations with a larger marine influence (i.e., lower estuary).

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Section: Spatial Variability Of Mass-normalized Optical Coefficientssupporting

confidence: 58%

Section: Spatial Variability Of Mass-normalized Optical Coefficientsmentioning

confidence: 99%

Optical properties of size fractions of suspended particulate matter in littoral waters of Québec

Mohammadpour

Gagné

Larouche³

et al. 2017

Biogeosciences

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“…While commonly employed in the remote-sensing paradigm, we acknowledge the validity of the power function for b bp λ remains debatable [25][26][27]. Both a w λ and b bw λ are known [28,29].…”

Section: A Model Developmentmentioning

confidence: 99%

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

365

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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“…These changing features modify the specific inherent optical properties (specific absorption and backscattering) of water. Particularly, the particle size figure) and West Gabbard (blue, right figure) locations, from dataset G. The solid line is the regression line between in situ and MODIS TSM products, the dotted line is the 1:1 distribution affects the specific scattering of particles (Doxaran et al 2009) which consequently varies seasonally, daily or at lower time scales (i.e. with phytoplankton blooms, tides, wind and currents effects).…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data

et al. 2011

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In situ measurements of total suspended matter (TSM) over the period [2003][2004][2005][2006], collected with two autonomous platforms from the Centre for Environment, Fisheries and Aquatic Sciences (Cefas) measuring the optical backscatter (OBS) in the southern North Sea, are used to assess the accuracy of TSM time series extracted from satellite data. Since there are gaps in the remote sensing (RS) data, due mainly to cloud cover, the Data Interpolating Empirical Orthogonal Functions (DINEOF) is used to fill in the TSM time series and build a continuous daily "recoloured" dataset. The RS datasets consist of TSM maps derived from MODIS imagery using the bio-optical model of Nechad et al. (Rem Sens Environ 114: 854-866, 2010). In this study, the DINEOF time series are compared to the in situ OBS measured in moderately to very turbid waters respectively in West Gabbard and Warp Anchorage, in the southern North Sea. The discrepancies between instantaneous RS, DINEOFfilled RS data and Cefas data are analysed in terms of TSM algorithm uncertainties, space-time variability and DINEOF reconstruction uncertainty.

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Spectral variations of light scattering by marine particles in coastal waters, from the visible to the near infrared

Cited by 78 publications

References 46 publications

Optical properties of size fractions of suspended particulate matter in littoral waters of Québec

Optical properties of size fractions of suspended particulate matter in littoral waters of Québec

Generalized ocean color inversion model for retrieving marine inherent optical properties

Reconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data

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