Retrieving absorption coefficients of multiple phytoplankton pigments from hyperspectral remote sensing reflectance measured over cyanobacteria bloom waters

Wang, Guoqing; Lee, Zhongping; Mishra, Deepak R.; Ma, Ronghua

doi:10.1002/lom3.10102

Cited by 44 publications

(44 citation statements)

References 72 publications

(136 reference statements)

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“…All other inversion models require information on water inherent optical properties and the retrieval accuracy can be various (e.g., Werdell et al, 2014;Wang et al, 2016). In our previous study, we have made a performance comparison between using R rs (λ) directly and using QAA-inverted absorption spectra from R rs (λ) for phytoplankton group differentiation (Xi et al, 2015).…”

Section: Identification Approach and Its Skillmentioning

confidence: 99%

Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

Hieronymi

Krasemann

et al. 2017

Front. Mar. Sci.

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In the present study we investigate the bio-geo-optical boundaries for the possibility to identify dominant phytoplankton groups from hyperspectral ocean color data. A large dataset of simulated remote sensing reflectance spectra, R rs (λ), was used. The simulation was based on measured inherent optical properties of natural water and measurements of five phytoplankton light absorption spectra representing five major phytoplankton spectral groups. These simulated data, named as C2X data, contain more than 10 5 different water cases, including cases typical for clearest natural waters as well as for extreme absorbing and extreme scattering waters. For the simulation the used concentrations of chlorophyll a (representing phytoplankton abundance), Chl, are ranging from 0 to 200 mg m −3 , concentrations of non-algal particles, NAP, from 0 to 1,500 g m −3 , and absorption coefficients of chromophoric dissolved organic matter (CDOM) at 440 nm from 0 to 20 m −1 . A second, independent, smaller dataset of simulated R rs (λ) used light absorption spectra of 128 cultures from six phytoplankton taxonomic groups to represent natural variability. Spectra of this test dataset are compared with spectra from the C2X data in order to evaluate to which extent the five spectral groups can be correctly identified as dominant under different optical conditions. The results showed that the identification accuracy is highly subject to the water optical conditions, i.e., contribution of and covariance in Chl, NAP, and CDOM. The identification in the simulated data is generally effective, except for waters with very low contribution by phytoplankton and for waters dominated by NAP, whereas contribution by CDOM plays only a minor role. To verify the applicability of the presented approach for natural waters, a test using in situ R rs (λ) dataset collected during a cyanobacterial bloom in Lake Taihu (China) is carried out and the approach predicts blue cyanobacteria to be dominant. This fits well with observation of the blue cyanobacteria Microcystis sp. in the lake. This study provides an efficient approach, which can be promisingly applied to hyperspectral sensors, for identifying dominant phytoplankton spectral groups purely based on R rs (λ) spectra.

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Section: Identification Approach and Its Skillmentioning

confidence: 99%

Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

Hieronymi

Krasemann

et al. 2017

Front. Mar. Sci.

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“…Absorption is often utilized in algorithm development because of its dominant role in regulating spectral variability of remote sensing reflectance due to changes in pigmentation (Moisan et al, 2011;Chase et al, 2013;Wang et al, 2016). In addition, sophisticated algorithms focus on ecological patterns of the phytoplankton community in relation to many factors such as climate change and meteorological conditions (Sathyendranath et al, 2004;Alvain et al, 2005;Hardman-Mountford et al, 2008;Raitsos et al, 2008;D'Ortenzio and Ribera d'Alcalà, 2009).…”

Section: Introductionmentioning

confidence: 99%

Satellite Observations of Phytoplankton Functional Type Spatial Distributions, Phenology, Diversity, and Ecotones

et al. 2017

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Phytoplankton functional diversity plays a key role in structuring the ocean carbon cycle and can be estimated using measurements of phytoplankton functional type (PFT) groupings. Concentrations of 18 phytoplankton pigments were calculated using a linear matrix inversion algorithm, with an average r 2 value of 0.70 for all pigments with p-values below the statistical threshold of 0.05. The inversion algorithm was then used with a chlorophyll-based absorption spectra model and Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) chlorophyll observations to calculate phytoplankton pigment concentrations in an area of the Atlantic Ocean off the United States east coast. Pigment distributions were analyzed to assess the distribution of PFTs. Five unique PFTs were found and delineated into three distinct offshore, transition, and open ocean groups. Group 1 (Diatoms) had highest abundance along the coast. Group 2 (prymnesiophytes, prokaryotes, and green algae) was a year-round stable offshore community that extended at reduced levels into the coast. Group 3 (dinoflagellates) dominated offshore between the Groups 1 and 2. Phytoplankton communities were delineated into coastal and offshore populations, with Group 2 having a dampened seasonal cycle, relative to the coastal populations. Shannon Diversity Indices (H) for the PFTs showed both spatial and temporal variability and had a clear non-linear relationship with chlorophyll. Diversity levels varied seasonally with changes in chlorophyll a levels. Peak PFT H was observed on the shelf where frontal features dominate, with diversity levels declining nearshore and offshore. This region marks an ecotone for phytoplankton in the study domain, and is associated with the coastal-side boundary of dinoflagellate dominance. Highest levels of diversity were observed in the tidally well-mixed regions of the Gulf of Maine and along a band that ran along the shelf region of the study area that was narrowest in the summer periods and broadened during the winter. These peak diversity zones were associated with moderate levels (∼0.8 mg m −3 ) of chlorophyll a. While the sign in the linear trends in chlorophyll between 2002 and 2016 varied depending on the region, the trends in the PFT H values were nearly all negative due to the non-linear relationship between chlorophyll levels and H.

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“…Phytoplankton and coloured detrital matter (CDM), which includes yellow substances (CDOM) and detritus, play an important role in global carbon cycling and climate, e.g., [1][2][3][4]. Phytoplankton are primary producers, which regulate the photosynthetic efficiency of carbon fixation [5][6][7], transfer primary production to higher trophic levels, e.g., [8], and export carbon to the deep oceans, e.g., [3,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, algorithms for estimating chlorophyll a, a primary photosynthetic pigment have been studied for decades [19][20][21][22][23]. Some pigments that occur in particular phytoplankton groups or taxa have been recognised as signatures for those groups or taxa, and their concentrations are used as indicators for the presence of these organisms in the water column [2,[24][25][26]. The summertime blooms of filamentous cyanobacteria in the Baltic Sea are known to contain large quantities of phycocyanin that may provide a more specific indicator of cyanobacterial biomass than chlorophyll a, which is present in all phytoplankton species [27,28].…”

Section: Introductionmentioning

confidence: 99%

A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters

et al. 2017

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Abstract:Eutrophication is an increasing problem in coastal waters of the Baltic Sea. Moreover, algal blooms, which occur every summer in the Gulf of Gdansk can deleteriously impact human health, the aquatic environment, and economically important fisheries, tourism, and recreation industries. Traditional laboratory-based techniques for water monitoring are expensive and time consuming, which usually results in limited numbers of observations and discontinuity in space and time. The use of hyperspectral radiometers for coastal water observation provides the potential for more detailed remote optical monitoring. A statistical approach to develop local models for the estimation of optically significant components from in situ measured hyperspectral remote sensing reflectance in case 2 waters is presented in this study. The models, which are based on empirical orthogonal function (EOF) analysis and stepwise multilinear regression, allow for the estimation of parameters strongly correlated with phytoplankton (pigment concentration, absorption coefficient) and coloured detrital matter abundance (absorption coefficient) directly from reflectance spectra measured in situ. Chlorophyll a concentration, which is commonly used as a proxy for phytoplankton biomass, was retrieved with low error (median percent difference, MPD = 17%, root mean square error RMSE = 0.14 in log 10 space) and showed a high correlation with chlorophyll a measured in situ (R = 0.84). Furthermore, phycocyanin and phycoerythrin, both characteristic pigments for cyanobacteria species, were also retrieved reliably from reflectance with MPD = 23%, RMSE = 0.23, R 2 = 0.77 and MPD = 24%, RMSE = 0.15, R 2 = 0.74, respectively. The EOF technique proved to be accurate in the derivation of the absorption spectra of phytoplankton and coloured detrital matter (CDM), with R 2 (λ) above 0.83 and RMSE around 0.10. The approach was also applied to satellite multispectral remote sensing reflectance data, thus allowing for improved temporal and spatial resolution compared with the in situ measurements. The EOF method tested on simulated Medium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI) data resulted in RMSE = 0.16 for chl-a and RMSE = 0.29 for phycocyanin. The presented methods, applied to both in situ and satellite data, provide a powerful tool for coastal monitoring and management.

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Retrieving absorption coefficients of multiple phytoplankton pigments from hyperspectral remote sensing reflectance measured over cyanobacteria bloom waters

Cited by 44 publications

References 72 publications

Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

Satellite Observations of Phytoplankton Functional Type Spatial Distributions, Phenology, Diversity, and Ecotones

A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters

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