Modeling the optical properties of mineral particles suspended in seawater and their influence on ocean reflectance and chlorophyll estimation from remote sensing algorithms

Woźniak, Sławomir B.; Stramski, Dariusz

doi:10.1364/ao.43.003489

Cited by 171 publications

(136 citation statements)

References 42 publications

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“…Theoritical and experimental relationship between γ and particle size or size distribution have been reported (e.g. Raynolds et al, 2001;Green and Sosik, 2004;Wozniak and Stramski, 2004;Kostadinov et al, 2009). As we ll as these study we obtained a good correlation between F L and γ , which is higher than between F L and the a ph (λ) ratios ( Table 3).…”

Section: Sdm Validations and Applicationmentioning

confidence: 98%

Remote sensing of size structure of phytoplankton communities using optical properties of the Chukchi and Bering Sea shelf region

et al. 2011

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Abstract. Recent ocean warming and subsequent sea ice decline resulting from climate change could affect the northward shift of the ecosystem structure in the Chukchi Sea and Bering Sea shelf region (Grebmeier et al., 2006b). The size structure of phytoplankton communities provides an index of trophic levels that is crucial to understanding the mechanisms underlying such ecosystem changes and their implications for the future. This study proposes a new ocean color algorithm for deriving this characteristic by using the region's optical properties. The size derivation model (SDM) estimates the phytoplankton size index F L on the basis of sizefractionated chlorophyll-a (chl-a) using the light absorption coefficient of phytoplankton, a ph (λ), and the backscattering coefficient of suspended particles including algae, b bp (λ). F L was defined as the ratio of algal biomass attributed to cells larger than 5 µm to the total. It was expressed by a multiple regression model using the a ph (λ) ratio, a ph (488)/a ph (555), which varies with phytoplankton pigment composition, and the spectral slope of b bp (λ), γ , which is an index of the mean suspended particle size. A validation study demonstrated that 69 % of unknown data are correctly derived within F L range of ±20 %. The spatial distributions of F L for the cold August of 2006 and the warm August of 2007 were compared to examine application of the SDM to satellite remote sensing. The results suggested that phytoplankton size was responsive to changes in sea surface temperature. Further analysis of satellite-derived F L values and other environmental factors can advance our understanding of ecosystem structure changes in the shelf region of the Chukchi and Bering Seas.

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Section: Sdm Validations and Applicationmentioning

confidence: 98%

Remote sensing of size structure of phytoplankton communities using optical properties of the Chukchi and Bering Sea shelf region

et al. 2011

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“…In comparison, retrievals of ocean IOP data in this study are basically analytical because they are based on the spectral features of the seawater constituents in the NIR wavelengths. This analytical feature of IOP modeling with the NIR ocean reflectance indicates that it can address the large variation of g values (Reynolds et al 2001;Wozniak and Stramski 2004), which otherwise are expected to lead to large errors with empirical or semi-analytical approaches.…”

Section: Some Satellite Ocean-color Remote-sensing Implicationsmentioning

confidence: 99%

Ocean reflectance spectra at the red, near‐infrared, and shortwave infrared from highly turbid waters: A study in the Bohai Sea, Yellow Sea, and East China Sea

Shi

Wang

2014

Limnology & Oceanography

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Normalized water-leaving radiance spectra nL w (l) at the red, near-infrared (NIR), and shortwave infrared (SWIR) are quantified and characterized in highly turbid waters of the western Pacific using 3 yr (2009)(2010)(2011) observations from the Moderate Resolution Imaging Spectroradiometer on the satellite Aqua. nL w (645; red), nL w (859; NIR), and nL w (1240; SWIR) were higher in the coastal region and river estuaries, with SWIR nL w (1240) reaching up to , 0.2 mW cm 22 mm 21 sr 21 in Hangzhou Bay during winter. The NIR ocean-reflectance spectral shape represented by the ratio of the normalized water-leaving reflectance r wN (l) at the two NIR bands r wN (748) : r wN (869) is highly dynamic and region-dependent. The NIR spectral feature associated with the sediment source from the Yellow River and Ancient Yellow River is noticeably different from that of the Yangtze River. There are non-negligible SWIR nL w (1240) contributions for waters with the NIR nL w (859) . , 2.5 mW cm 22 mm 21 sr 21 . Estimation of the NIR ocean reflectance with iterative approaches might only be accurate for turbid waters with nL w (859) , , 1.5 mW cm 22 mm 21 sr 21 . Thus, the SWIR atmospherics correction algorithm for satellite ocean-color data processing is indispensable to derive accurate nL w (l) for highly turbid waters. Current existing satellite algorithms for chlorophyll a, diffuse attenuation coefficient at the wavelength of 490 nm (K d (490)), total suspended matter, and inherent optical properties (IOPs) using nL w (l) at the red band for coastal waters are limited and can only be applied to turbid waters with nL w (859) , , 1.5 mW cm 22 mm 21 sr 21 . Thus, the NIR nL w (l) measurements are required to characterize water properties for highly turbid waters. Based on the fact that pure water absorption is significantly larger than other absorption components in the NIR wavelengths, we show that it is feasible to analytically derive accurate IOP data for turbid waters with combined satellite-measured visible-NIR nL w (l) spectra data.

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“…Similar problems also exist with the retrieval of the water constituents' concentrations from satellite observations in these waters (O'Reilly et al, 1998;O'Reilly et al, 2000;Shanmugam, 2011a). The errors of more than 10 % in retrieval of IOPs (Stramski, 2001) and even much higher (20 times higher than measurements) in retrieval of chlorophyll are reported (Wozniak and Stramski, 2004).…”

Section: Introductionmentioning

confidence: 71%

Modelling of underwater light fields in turbid and eutrophic waters: application and validation with experimental data

Sundarabalan

Shanmugam

2015

Ocean Sci.

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Abstract.A reliable radiative transfer (RT) model is an essential and indispensable tool for understanding the radiative transfer processes in homogenous and layered waters, analyzing measurements made by radiance sensors and developing remote-sensing algorithms to derive meaningful physical quantities and biogeochemical variables in turbid and productive coastal waters. Existing radiative transfer models have been designed to be applicable to either homogenous waters or inhomogeneous waters. To overcome such constraints associated with these models, this study presents a radiative transfer model that treats a homogenous layer as a diffuse part and an inhomogeneous layer as a direct part in the water column and combines these two parts appropriately in order to generate more reliable underwater lightfield data such as upwelling radiance (L u ), downwelling irradiance (E d ) and upwelling irradiance (E u ). The diffuse model assumes the inherent optical properties (IOPs) to be vertically continuous and the light fields to exponentially decrease with depth, whereas the direct part considers the water column to be vertically inhomogeneous (layer-by-layer phenomena) with the vertically varying phase function. The surface and bottom boundary conditions, source function due to chlorophyll and solar incident geometry are also included in the present RT model. The performance of this model is assessed in a variety of waters (clear, turbid and eutrophic) using the measured radiometric data. The present model shows an advantage in terms of producing accurate L u , E d and E u profiles (in spatial domain) in different waters determined by both homogenous and inhomogeneous conditions. The feasibility of predicting these underwater light fields based on the remotely estimated IOP data is also examined using the present RT model. For this application, vertical profiles of the water constituents and IOPs are estimated by empirical models based on our in situ data. The present RT model generates L u , E d and E u spectra closely consistent with the measured data. These results lead to a conclusion that the present RT model is a viable alternative to existing RT models and has an important implication for remote sensing of optically complex waters.

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Modeling the optical properties of mineral particles suspended in seawater and their influence on ocean reflectance and chlorophyll estimation from remote sensing algorithms

Cited by 171 publications

References 42 publications

Remote sensing of size structure of phytoplankton communities using optical properties of the Chukchi and Bering Sea shelf region

Remote sensing of size structure of phytoplankton communities using optical properties of the Chukchi and Bering Sea shelf region

Ocean reflectance spectra at the red, near‐infrared, and shortwave infrared from highly turbid waters: A study in the Bohai Sea, Yellow Sea, and East China Sea

Modelling of underwater light fields in turbid and eutrophic waters: application and validation with experimental data

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