A model for estimating size-fractioned phytoplankton absorption coefficients in coastal and oceanic waters from satellite data

Varunan, Theenathayalan; Shanmugam, Palanisamy

doi:10.1016/j.rse.2014.11.008

Cited by 25 publications

(16 citation statements)

References 65 publications

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“…Although the functional forms in equations are similar to those used in previous studies (Brewin et al, ; Devred et al, ; Sathyendranath et al, ; Varunan & Shanmugam, ), the strategies used to realize the model are different from the equations: (1) retrieving C p and C n and then derive C m = C ‐ C p ‐ C n (Strategy 1); (2) retrieving C p and C m and then derive C n = C ‐ C p ‐ C m (Strategy 2); (3) retrieving C n and C m and then derive C p = C ‐ C n ‐ C m (Strategy 3). Furthermore, the entire spectral range of 650–750 nm was analyzed to determine the optimal wavelengths.…”

Section: Psc Model Developmentmentioning

confidence: 99%

“…In order to estimate the size‐specific chlorophyll a concentrations, this study used exponential relationships that linked the dependent variables to two independent variables, namely total chlorophyll a concentration and R rs (equations ), similar to those used in previous studies (Brewin et al, ; Devred et al, ; Sathyendranath et al, ; Varunan & Shanmugam, ).

C_{normalp} = C_{normalp}^{*} {true[1 - exp true(- C^{2} \times true(max true(R_{rs_650 \sim 750} true) true) true) true]}^{S^{normalp}},

C_{normaln} = C_{normaln}^{*} {true[1 - exp true(- C^{2} \times true(max true(R_{rs_650 \sim 750} true) true) true) true]}^{S^{normaln}},

C_{normalm} = C_{normalm}^{*} {true[1 - exp true(- C^{2} \times true(max true(R_{rs_650 \sim 750} true) true) true) true]}^{S^{normalm}},

where

C_{normalp}^{*}

C_{normaln}^{*}

, and

C_{normalm}^{*}

(dimensionless) are the concentration coefficients of picophytoplankton, nanophytoplankton, and microphytoplankton, respectively; S p , S n , and S m are their slope parameters (dimensionless).…”

Section: Psc Model Developmentmentioning

confidence: 99%

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Remote‐Sensing Estimation of Phytoplankton Size Classes FromGOCISatellite Measurements in Bohai Sea and Yellow Sea

et al. 2017

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Phytoplankton size class (PSC), a measure of different phytoplankton functional and structural groups, is a key parameter to the understanding of many marine ecological and biogeochemical processes. In turbid waters where optical properties may be influenced by terrigenous discharge and nonphytoplankton water constituents, remote estimation of PSC is still a challenging task. Here based on measurements of phytoplankton diagnostic pigments, total chlorophyll a, and spectral reflectance in turbid waters of Bohai Sea and Yellow Sea during summer 2015, a customized model is developed and validated to estimate PSC in the two semienclosed seas. Five diagnostic pigments determined through high‐performance liquid chromatography (HPLC) measurements are first used to produce weighting factors to model phytoplankton biomass (using total chlorophyll a as a surrogate) with relatively high accuracies. Then, a common method used to calculate contributions of microphytoplankton, nanophytoplankton, and picophytoplankton to the phytoplankton assemblage (i.e., Fm, Fn, and Fp) is customized using local HPLC and other data. Exponential functions are tuned to model the size‐specific chlorophyll a concentrations (Cm, Cn, and Cp for microphytoplankton, nanophytoplankton, and picophytoplankton, respectively) with remote‐sensing reflectance (Rrs) and total chlorophyll a as the model inputs. Such a PSC model shows two improvements over previous models: (1) a practical strategy (i.e., model Cp and Cn first, and then derive Cm as C‐Cp‐Cn) with an optimized spectral band (680 nm) for Rrs as the model input; (2) local parameterization, including a local chlorophyll a algorithm. The performance of the PSC model is validated using in situ data that were not used in the model development. Application of the PSC model to GOCI (Geostationary Ocean Color Imager) data leads to spatial and temporal distribution patterns of phytoplankton size classes (PSCs) that are consistent with results reported from field measurements by other researchers. While the applicability of the PSC model together with its parameterization to other optically complex regions and to other seasons is unknown, the findings of this study suggest that the approach to develop such a model may be extendable to other cases as long as local data are used to select the optimal band and to determine the model coefficients.

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Section: Psc Model Developmentmentioning

confidence: 99%

C_{normalp} = C_{normalp}^{*} {true[1 - exp true(- C^{2} \times true(max true(R_{rs_650 \sim 750} true) true) true) true]}^{S^{normalp}},

C_{normaln} = C_{normaln}^{*} {true[1 - exp true(- C^{2} \times true(max true(R_{rs_650 \sim 750} true) true) true) true]}^{S^{normaln}},

C_{normalm} = C_{normalm}^{*} {true[1 - exp true(- C^{2} \times true(max true(R_{rs_650 \sim 750} true) true) true) true]}^{S^{normalm}},

where

C_{normalp}^{*}

C_{normaln}^{*}

, and

C_{normalm}^{*}

(dimensionless) are the concentration coefficients of picophytoplankton, nanophytoplankton, and microphytoplankton, respectively; S p , S n , and S m are their slope parameters (dimensionless).…”

Section: Psc Model Developmentmentioning

confidence: 99%

Remote‐Sensing Estimation of Phytoplankton Size Classes FromGOCISatellite Measurements in Bohai Sea and Yellow Sea

et al. 2017

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“…This bio‐optical algorithm was demonstrated to be very effective in terms of accurate estimation of chlorophyll and discriminating algal bloom patches in coastal waters rather than using global algorithms (e.g., OC3 and OC4v4). Inherent optical properties such as absorption ( a ( λ ) = a w ( λ )+ a p ( λ )+ a cdom ( λ )) and backscattering ( b b ( λ ) = b w ( λ )+ b p ( λ )) were derived from the existing models [ Shanmugam et al ., ; Gokul et al ., ; Varunan and Shanmugam , ; Sundarabalan and Shanmugam , ]. The pure water absorption and backscattering coefficients were added with these particulate terms to obtain the respective total absorption and backscattering coefficients.…”

Section: Methodsmentioning

confidence: 99%

“…The existing models [ Bricaud et al ., ; Devred et al ., ; Brewin et al ., ] to estimate the absorption coefficient of phytoplankton ( a ph ) were inadequate or observed to produce large errors in sediment‐laden and algal bloom waters. To overcome these issues, Varunan and Shanmugam [] proposed a model that takes chlorophyll and reflectance values 678 and 748 nm as inputs to accurately estimate a ph across the entire visible wavelengths. This method is valid for both marine and highly productive inland waters.…”

Section: Approachmentioning

confidence: 99%

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An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India

Gokul

Shanmugam

2016

JGR Oceans

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An optical system is developed with the aim to detect and monitor three major algal blooms (including harmful algal blooms ''HABs'') over ecologically relevant scales around India and to strengthen algal forecasting system. This system is designed to be capable of utilizing remote sensing, in situ, and radiative transfer techniques to provide species-specific data necessary for increasing capabilities of an algal forecasting system. With the ability to measure in-water optical properties by means of remote sensing and in situ observations, the optical system developed infers the desired phytoplankton signal from spectral distributions and utilize these data in a numerical classification technique to generate species-specific maps at given spatial and temporal scales. A simple radiative transfer model is adopted for this system to provide a means to optimally interpolate to regions with sparse in situ observation data and to provide a central component to generate in-water optical properties from remotely sensed data. For a given set of inherent optical properties along with surface and bottom boundary conditions, the optical system potentially provides researchers and managers coverage at different locations and depths for tracking algal blooms in the water column. Three major algal blooms focused here include Noctiluca scintillans/miliaris, Trichodesmium erythraeum, and Cochlodinium polykrikoides, which are recurring events in coastal and oceanic waters around India. Because satellite sensors provide a synoptic view of the ocean, both spatially and temporally, our initial efforts tested this optical system using several MODIS-Aqua images and ancillary data. Validation of the results with coincident in situ data obtained from either surface samples or depth samples demonstrated the robustness and potential utility of this approach, with an accuracy of 80-90% for delineating the presence of all three blooms in a heterogeneous phytoplankton community. Despite its limitation in detecting specific species during their prebloom phases and indicating whether a particular bloom is toxic or harmful, the proposed optical system will provide managers with the specific phytoplankton bloom maps to structure monitoring efforts and a powerful tool for studying the dynamics of algal blooms at various temporal and spatial scales.

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Satellite-Based Marine Ecological Services for the Indian Ocean Region

Baliarsingh

Samanta

Lotliker

et al. 2022

Social and Economic Impact of Earth Sciences

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A model for estimating size-fractioned phytoplankton absorption coefficients in coastal and oceanic waters from satellite data

Cited by 25 publications

References 65 publications

Remote‐Sensing Estimation of Phytoplankton Size Classes FromGOCISatellite Measurements in Bohai Sea and Yellow Sea

Remote‐Sensing Estimation of Phytoplankton Size Classes FromGOCISatellite Measurements in Bohai Sea and Yellow Sea

An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India

Satellite-Based Marine Ecological Services for the Indian Ocean Region

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