Correction of pathlength amplification in the filter-pad technique for measurements of particulate absorption coefficient in the visible spectral region

Stramski, Dariusz; Reynolds, Rick A.; Kaczmarek, S.; Uitz, Julia; Zheng, Guangming

doi:10.1364/ao.54.006763

Cited by 103 publications

(97 citation statements)

References 62 publications

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“…The spectral absorption coefficient of particles, a p ( λ ), was determined at 1 nm intervals over the spectral range 300–850 nm for ICESCAPE cruises and 300–800 nm for the MALINA cruise in a dual‐beam spectrophotometer (Lambda 18, Perkin Elmer) equipped with a 15 cm integrating sphere (Labsphere). Filters were placed inside the integrating sphere for measurement and scanned in two different orientations to subsample different portions of the filter, whereafter results were averaged (e.g., Röttgers and Gehnke ; Stramski et al ). A correction for the pathlength amplification factor was applied to the data (Stramski et al ).…”

Section: Methodsmentioning

confidence: 99%

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Optical classification and characterization of marine particle assemblages within the western Arctic Ocean

Neukermans

Reynolds

Stramski

2016

Limnology & Oceanography

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We develop an optical classification of marine particle assemblages from an extensive dataset of particle optical properties collected in the Chukchi and Beaufort Seas. Hierarchical cluster analysis of the spectral particulate backscattering-to-absorption ratio partitioned the dataset into seven optically-distinct clusters of particle assemblages, each associated with different characteristics of particle concentration, composition, and phytoplankton taxonomic composition and size. Three phytoplankton-dominated clusters were identified. One was characterized by small-sized phytoplankton that are typically associated with regenerated production, and comprised samples from the subsurface chlorophyll-a maximum in oligotrophic waters of the Beaufort Sea. The other two clusters represented diatom-dominated particle assemblages in turbid shelf waters with differing contributions of photoprotective pigments. Such situations are generally associated with significant new production. Two clusters were dominated by organic nonalgal material, one representing clear waters off the shelf, the other representative of post-diatom bloom conditions in the Chukchi Sea. Another distinct cluster represented mineral-dominated particle assemblages that were observed in the Colville and Mackenzie River plumes and near the seafloor. Finally, samples in a cluster of mixed particle composition were scattered throughout all locations. Optical classification improved performance of predictive bio-optical relationships. These results demonstrate a capability to discriminate distinct assemblages of suspended particles associated with specific ecological conditions from hyperspectral measurements of optical properties, and the potential for identification of ecological provinces at synoptic time and space scales from optical sensors. Analogous analysis of multispectral optical data strongly reduced this capability.Particles in seawater are a ubiquitous and important component of the marine environment and comprise both living organisms and nonliving material. They directly influence optical properties of seawater that are measurable from various in situ and remote platforms, thus enabling observations of marine particle assemblages over a wide range of temporal and spatial scales. Sensors measuring the inherent optical properties (IOPs) of seawater at a given wavelength of light k (in vacuo), such as the spectral coefficients for absorption, a(k), scattering, b(k), backscattering, b b (k), and beam attenuation, c(k) 5 a(k) 1 b(k), are now routinely deployed on various in situ platforms including ships, moorings, profilers, and autonomous underwater vehicles. Such data are becoming increasingly important for ocean biogeochemical and ecosystem studies (e.g., Johnson et al. 2009;Fennel et al. 2011). Remote sensing of ocean color from satellites or aircraft, quantified as the spectral remote-sensing reflectance, R rs (k), also allow estimation of seawater IOPs as R rs (k) is, to first order, proportional to the ratio of b b (k) to a(k) (Go...

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Section: Methodsmentioning

confidence: 99%

“…Filters were placed inside the integrating sphere for measurement and scanned in two different orientations to subsample different portions of the filter, whereafter results were averaged (e.g., Röttgers and Gehnke ; Stramski et al ). A correction for the pathlength amplification factor was applied to the data (Stramski et al ).…”

Section: Methodsmentioning

confidence: 99%

Optical classification and characterization of marine particle assemblages within the western Arctic Ocean

Neukermans

Reynolds

Stramski

2016

Limnology & Oceanography

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“…Particularly, they showed how the transmittance QFT method (as used to determine the a phy (443) and Chla relation by Bricaud et al [24]) tends to overestimate a phy (443) in respect to measurements obtained inside an integrating sphere (as used herein following Röttgers et al [18]). This a problem to be considered when integrating data sets obtained from different cruises and analysed by different methods [71].…”

Section: Environmental and Bio-optical Characterizationmentioning

confidence: 99%

“…Stramski et al [71] shows how using different methods and path length amplification factors (beta correction) can cause over than 30% differences for the ap measurements. Particularly, they showed how the transmittance QFT method (as used to determine the aphy(443) and Chla relation by Bricaud et al [24]) tends to overestimate aphy(443) in respect to measurements obtained inside an integrating sphere (as used herein following Röttgers et al [18]).…”

Section: Environmental and Bio-optical Characterizationmentioning

confidence: 99%

Bio-Optical Characterization and Ocean Colour Inversion in the Eastern Lagoon of New Caledonia, South Tropical Pacific

et al. 2018

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Abstract:The Eastern Lagoon of New Caledonia (ELNC) is a semi-enclosed system surrounded by an extensive coral reef barrier. The system has been suffering impacts from climate variability and anthropogenic activities, including mining exploitation. Satellite monitoring is thus an essential tool to detect such changes. The present study aimed to assess the bio-optical variability of the ELNC and examine the applicability of ocean colour algorithms, using in situ bio-optical and radiometric data, collected during the March 2014 CALIOPE 2 cruise. The chlorophyll a concentration (Chla) varied from 0.13-0.72 mg·m −3 , and the coastal stations were spectrally dominated by non-algal particles (NAP) and coloured dissolved organic matter (CDOM) (>80% of the total non-water absorption at 443 nm), due to the contribution of allochthonous sources. The phytoplankton specific absorption was generally lower (mean, 0.049 m 2 ·mg Chla −1 ) than typical values observed for the corresponding Chla range, as well as the spectral slopes of the absorption of CDOM plus NAP (a dg ) (mean, 0.016 nm −1 ) and of the particle backscattering coefficient (b bp ) (mean, 0.07 nm −1 ). The remote sensing reflectance obtained using two in-water approaches and modelled from Inherent Optical Properties (IOPs) showed less than 20% relative percent differences (RPD). Chla estimates were highly biased for the empirical (OC4 and OC3) and semi-analytical (GSM, QAA, GIOP, LMI) algorithms, especially at the coastal stations. Excluding these stations, the GSM01 yielded the best retrievals with 35-40% RPD. a dg (443) was well retrieved by all algorithms with~18% RPD, and b bp (443) with~40% RPD. Turbidity algorithms also performed reasonably well (30% RPD), showing the capacity and usefulness of the derived products to monitor the water quality of the ELNC, provided accurate atmospheric correction of the satellite data. Regionally tuned algorithms may potentially improve the Chla retrievals, but better parameterization schemes that consider the spatiotemporal variability of the specific IOPs are still needed.

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“…For instance, in DS2 the spectral nonwater absorption coefficient, a nw ( λ ), was obtained from in situ measurements with an ac‐s instrument (WET Labs) as described in Neukermans et al (). The absorption data in DS3 were obtained from spectrophotometric measurements made on discrete water samples, which provided the component absorption coefficients, a CDOM ( λ ) and

a_{p} (|, λ) = a_{normalp normalh} (|, λ) + a_{normalN normalA normalP} (|, λ)

, and hence

, a_{normaln normalw} (|, λ) = a_{normalC normalD normalO normalM} (|, λ) + a_{p} (|, λ)

(e.g., Mitchell et al, ; Stramski et al, ). With regard to the backscattering coefficient, the ECO BB‐9 and ECO‐VSF sensors (WET Labs) have been used to measure b b ( λ ) in DS2, and Hydroscat‐6 sensors (HOBI Labs) have been used in DS3.…”

Section: Model Evaluation and Validationmentioning

confidence: 99%

An Inverse Model for Estimating the Optical Absorption and Backscattering Coefficients of Seawater From Remote‐Sensing Reflectance Over a Broad Range of Oceanic and Coastal Marine Environments

et al. 2018

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We present an inverse model (referred to as LS2) for estimating the inherent optical properties (IOPs) of seawater, specifically the spectral absorption, a(λ), and backscattering, bb(λ), coefficients within the ocean surface layer, from measurements of ocean remote‐sensing reflectance, Rrs(λ). The nonwater absorption, anw(λ), and particulate backscattering, bbp(λ), coefficients can be derived after subtracting pure seawater contributions. The LS2 requires no spectral assumptions about IOPs and provides solutions at arbitrary light wavelengths in the visible spectrum independently of one another. As the LS2 can operate with the inputs of Rrs(λ) and solar zenith angle it is applicable to satellite ocean color remote sensing. The model can also operate with additional input of the diffuse attenuation coefficient of downward irradiance, which provides somewhat improved model performance for applications using in situ radiometric measurements as inputs. The evaluation of LS2 with a synthetic data set that is free of measurement errors indicates good performance for IOPs in the visible spectrum, except for anw(λ) in the long‐wavelength portion of the spectrum where anw(λ) contributes only a few percent to a(λ) under typical open ocean conditions. The good performance is characterized by a median absolute percent difference between the model‐derived and true values of IOPs, which is generally <20%, and the median ratio of model‐derived to true values <10%. The satisfactory model performance is also demonstrated through validation analysis based on extensive data sets comprising coincident in situ measurements of Rrs(λ) and IOPs as well as a match‐up data set comprising satellite‐derived Rrs(λ) and in situ IOP measurements.

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Correction of pathlength amplification in the filter-pad technique for measurements of particulate absorption coefficient in the visible spectral region

Cited by 103 publications

References 62 publications

Optical classification and characterization of marine particle assemblages within the western Arctic Ocean

Optical classification and characterization of marine particle assemblages within the western Arctic Ocean

Bio-Optical Characterization and Ocean Colour Inversion in the Eastern Lagoon of New Caledonia, South Tropical Pacific

An Inverse Model for Estimating the Optical Absorption and Backscattering Coefficients of Seawater From Remote‐Sensing Reflectance Over a Broad Range of Oceanic and Coastal Marine Environments

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