Phytoplankton Absorption Spectra Research Articles

Light Absorption by Optically Active Components in the Arctic Region (August 2020) and the Possibility of Application to Satellite Products for Water Quality Assessment

In August 2020, during the 80th cruise of the R/V “Akademik Mstislav Keldysh”, the chlorophyll a concentration (Chl-a) and spectral coefficients of light absorption by phytoplankton pigments, non-algal particles (NAP) and colored dissolved organic matter (CDOM) were measured in the Norwegian Sea, the Barents Sea and the adjacent area of the Arctic Ocean. It was shown that the spatial distribution of the three light-absorbing components in the explored Arctic region was non-homogenous. It was revealed that CDOM contributed largely to the total non-water light absorption (atot(λ) = aph(λ) + aNAP(λ) + aCDOM(λ)) in the blue spectral range in the Arctic Ocean and the Barents Sea. The fraction of NAP in the total non-water absorption was low (less than 20%). The depth of the euphotic zone depended on atot(λ) in the surface water layer, which was described by a power equation. The Arctic Ocean, the Norwegian Sea and the Barents Sea did not differ in the Chl-a-specific light absorption coefficients of phytoplankton. In the blue maximum of phytoplankton absorption spectra, Chl-a-specific light absorption coefficients of phytoplankton in the upper mixed layer (UML) were higher than those below the UML. Relationships between phytoplankton absorption coefficients and Chl-a were derived by least squares fitting to power functions for the whole visible domain with a 1 nm interval. The OCI, OC3 and GIOP algorithms were validated using a database of co-located results (day-to-day) of in situ measurements (n = 63) and the ocean color scanner data: the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra (EOS AM) and Aqua (EOS PM) satellites, the Visible and Infrared Imager/Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) and JPSS-1 satellites (also known as NOAA-20), and the Ocean and the Land Color Imager (OLCI) onboard the Sentinel-3A and Sentinel-3B satellites. The comparison showed that despite the technological progress in optical scanners and the algorithms refinement, the considered standard products (chlor_a, chl_ocx, aph_443, adg_443) carried little information about inherent optical properties in Arctic waters. Based on the statistic metrics (Bias, MdAD, MAE and RMSE), it was concluded that refinement of the algorithm for retrieval of water bio-optical properties based on remote sensing data was required for the Arctic region.

Method for extracting pigment characteristic spectra from the phytoplankton absorption spectrum.

The extraction of pigment characteristic spectra from the phytoplankton absorption spectrum has high application value in phytoplankton identification and classification and in quantitative extraction of pigment concentrations. Derivative analysis, which has been widely used in this field, is easily interfered with by noisy signals and the selection of the derivative step, resulting in the loss and distortion of the pigment characteristic spectra. In this study, a method based on the one-dimensional discrete wavelet transform (DWT) was proposed to extract the pigment characteristic spectra of phytoplankton. DWT and derivative analysis were applied simultaneously to the phytoplankton absorption spectra of 6 phyla (Dinophyta, Bacillariophyta, Haptophyta, Chlorophyta, Cyanophyta, and Prochlorophyta) to verify the effectiveness of DWT in the extraction of pigment characteristic spectra.

Influence of the phytoplankton community composition on the in situ fluorescence signal: Implication for an improved estimation of the chlorophyll-a concentration from BioGeoChemical-Argo profiling floats

In-situ fluorescence is a widely used method to estimate the chlorophyll-a (Chla) concentration, a proxy of the phytoplankton biomass. With the emergence of autonomous platforms such as BioGeoChemical-Argo (BGC-Argo) profiling floats, its use has expanded to global scale observations. However, the relationship between in-situ fluorescence and Chla may vary significantly, leading to major discrepancies between oceanic regions. This study aims to investigate the main sources of the natural variability in the in-situ fluorescence signal in the global open ocean, specifically the influence of the phytoplankton community composition. In this view, we analyzed a combination of three datasets comprising concomitant measurements of in-situ fluorescence, pigment concentrations and phytoplankton absorption spectra. Two datasets cover several contrasted bioregions of the global ocean whereas the third one consists of a regional time series in the northwestern Mediterranean Sea, which allows to examine the effect of phytoplankton community composition on the fluorescence signal on the global, seasonal and vertical scales. We studied the variability of the two major drivers of the natural variability of the fluorescence process, i.e. the light absorption and the fluorescence quantum yield of phytoplankton, in regards of the variability of the pigment composition of the communities. The community composition correlates substantially with the Chla-to-fluorescence ratio, with high fluorescence values associated with phytoplankton communities dominated by large cells. This trend may be explained by the combined effects of the community composition on the phytoplankton absorption coefficient and the fluorescence quantum yield, and is consistently observed globally, seasonally and vertically. Non-photosynthetic pigments also appear to play a critical role in oligotrophic surface waters, leading to a reduction of the quantum yield of fluorescence. The results indicate that the phytoplankton community composition plays a key role in the relationship between the in-situ fluorescence signal and Chla concentration. Therefore, we suggest that taking into account the composition of phytoplankton communities in the retrieval of the Chla concentration from current in-situ fluorometers, those mounted on BGC-Argo floats in particular, would lead to a better estimation of the phytoplankton biomass on a wide range of spatial and temporal scales.

Estimation of the Global Distribution of Phytoplankton Light Absorption From Pigment Concentrations

AbstractLight absorption by phytoplankton drives marine primary production and determines ocean color. Phytoplankton absorption is a function of the pigment composition, wavelength, intracellular pigment concentration, and the cells' type. This paper presents phytoplankton absorption spectra reconstructed from in situ pigment concentration and a library of pigment‐specific absorption coefficients from 32 individual pigment standards, including chlorophylls, caretonoids and phycobilins. The samples dominated by small phytoplankton show no significant difference between calculated absorption and that measured by a spectrophotometer. The component of absorption due to large cells, determined by diagnostic pigments analysis, required correction for the package effect. For the global ocean, the reconstructed phytoplankton absorption was overestimated by 16% at 443 nm and underestimated by 13% over the range between 400 and 700 nm. Following our reconstruction protocol, this approach allows the estimation of phytoplankton absorption spectra from many locations where pigment concentration has been measured, but no directly observed phytoplankton absorption measurements are available.

Light absorption spectra of naturally mixed phytoplankton assemblages for retrieval of phytoplankton group composition in coastal oceans

AbstractPhytoplankton group composition is complex and highly variable in coastal waters. Given that different taxonomic groups have different pigment signatures, which in turn impact the light absorption spectra of phytoplankton, the absorption spectral‐based approach has the potential for distinguishing phytoplankton groups. Using a large dataset of in situ surface observations of concurrent HPLC (high‐performance liquid chromatography) pigments and phytoplankton absorption spectra collected from 2015 to 2018 in Chinese coastal oceans, in situ phytoplankton group composition was obtained from chemotaxonomic analysis (CHEMTAX). By using the linear additive principle on phytoplankton absorption spectra and CHEMTAX results, the chlorophyll‐specific absorption spectra of eight phytoplankton groups were reconstructed, including prasinophytes, dinoflagellates, cryptophytes, chlorophytes, cyanobacteria, diatoms, chrysophytes, and prymnesiophytes. These chlorophyll‐specific absorption spectra were subsequently used as inputs to a spectral‐based inversion model for estimating phytoplankton group composition from the phytoplankton absorption coefficient. The optimal band selection and initial guesses of the phytoplankton group composition, derived from correlation and HCA (hierarchical cluster analysis) analyses, were included in the model inversion to improve the accuracy of retrievals. The performance of the proposed model was validated using an independent dataset, showing accurate estimates of chlorophyll a (Chl a) concentrations for seven phytoplankton groups (0.371 ≤ r ≤ 0.721, p < 0.05), apart from chrysophytes. Our results suggest that the absorption spectral‐based approach is able to discriminate phytoplankton group composition quantitatively, which has implications for retrieving Chl a concentrations of phytoplankton groups from hyperspectral platforms and satellites.

Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra

Algal pigment composition is an indicator of phytoplankton community structure that can be estimated from optical observations. Assessing the potential capability to retrieve different types of pigments from phytoplankton absorption is critical for further applications. This study investigated the performance of three models and the utility of hyperspectral in vivo phytoplankton absorption spectra for retrieving pigment composition using a large database (n = 1392). Models based on chlorophyll-a (Chl-a model), Gaussian decomposition (Gaussian model), and partial least squares (PLS) regression (PLS model) were compared. Both the Gaussian model and the PLS model were applied to hyperspectral phytoplankton absorption data. Statistical analysis revealed the advantages and limitations of each model. The Chl-a model performed well for chlorophyll-c (Chl-c), diadinoxanthin, fucoxanthin, photosynthetic carotenoids (PSC), and photoprotective carotenoids (PPC), with a median absolute percent difference for cross-validation (MAPDCV) < 58%. The Gaussian model yielded good results for predicting Chl-a, Chl-c, PSC, and PPC (MAPDCV < 43%). The performance of the PLS model was comparable to that of the Chl-a model, and it exhibited improved retrievals of chlorophyll-b, alloxanthin, peridinin, and zeaxanthin. Additional work undertaken with the PLS model revealed the prospects of hyperspectral-resolution data and spectral derivative analyses for retrieving marker pigment concentrations. This study demonstrated the applicability of in situ hyperspectral phytoplankton absorption data for retrieving pigment composition and provided useful insights regarding the development of bio-optical algorithms from hyperspectral and satellite-based ocean-colour observations.

Ross Sea Dissolved Organic Matter Optical Properties During an Austral Summer: Biophysical Influences

The Ross Sea, one of the most productive regions in the Southern Ocean, plays a significant role in deep water formation and carbon cycling. Dissolved organic carbon (DOC) concentrations and chromophoric dissolved organic matter (CDOM) absorption and fluorescence (FDOM) properties were studied in conjunction with biophysical properties during austral summer. Elevated values of both DOC (mean 47.82 ± 5.70 μM) and CDOM (absorption coefficient at 325 nm, acdom325: mean 0.31 ± 0.18 m–1) observed in the upper shelf waters in the southwest (SW), north of the Ross Ice Shelf (RIS), the northwest and along a transect inward of the shelf break, suggested in situ production and accumulation linked to the productive spring/summer season. However, regional differences were observed in CDOM with acdom325 higher (0.63 ± 0.19 m–1) and its spectral slope S275–295 lower (24.06 ± 2.93 μm–1) in the SW compared to other regions (0.25 ± 0.08 m–1 and 28.92 ± 2.67 μm–1, respectively). Similarly, the specific UV absorption coefficient or SUVA254 determined at 254 nm was greater (1.85 ± 0.55 m2 mg–1 C) compared to other regions (1.07 ± 0.24 m2 mg–1 C), indicating CDOM of greater molecular weight and aromaticity in the SW. Phytoplankton absorption spectra indicated the shallow mixed layer of SW Ross Sea to be dominated by diatoms (e.g., Fragilariopsis spp.), a preferential food source for grazers such as the Antarctic krill, which in large numbers have been shown to enhance CDOM absorption, a likely source in the SW. Excitation-emission matrix (EEM) fluorescence combined with parallel factor analysis (PARAFAC) retrieved one protein-like and two humic-like FDOM fractions commonly observed in the global ocean. In contrast to acdom325 which was uncorrelated to DOC, we observed weak but significant positive correlations between the humic-like FDOM with salinity and DOC, high value of the biological index parameter BIX and an instance of increasing FDOM with depth at a location with sinking organic matter, suggesting autochthonous production of FDOM. The absorption budget showed a relatively higher contribution by CDOM (70.7 ± 18.3%) compared to phytoplankton (22.5 ± 15.2%) absorption coefficients at 443 nm with implications to ocean color remote sensing. This first study of DOM optical properties provides additional insights on carbon cycling in the Ross Sea.

Characterization of phytoplankton productivity and bio-optical variability in a polar marine ecosystem

Knowledge of Southern Ocean carbon cycling is limited by a paucity of phytoplankton primary productivity (PP) and spectral absorption data in this globally-important region. We measured 13C-based PP in the Indian sector of Southern Ocean (ISSO) during austral summer 2017, examining its link with spectral absorption coefficients and phytoplankton size structure derived from an absorption-based global model. Phytoplankton productivity was assessed at both coastal (60°S-69°S) and frontal stations (40°S-60°S), characterized by silicate- replete and -deplete water masses, respectively (indicated by measured nutrient ratios) to capture a range of phytoplankton growth conditions. Bio-optical relationships were used as indicators of phytoplankton community size structure and to assess the extent of cellular pigment packaging - a phenomenon reported previously for phytoplankton in this region. Blue-Red (B/R) ratios of phytoplankton absorption (aph) spectra indicated that microphytoplankton (more prone to “package effects”) were the dominant size class at most sites sampled. Overall, PP was better explained by aph (R2 = 0.85) than total chlorophyll-a (R2 = 0.64) in surface waters. The a*ph (675)-chlorophyll-a relationship explained package effects more effectively in frontal regions (R2 = 0.63) than stations further south (R2 = 0.30). The global absorption-based model captured smaller (pico, nano) phytoplankton size classes but failed to identify larger microphytoplankton, underscoring the need for region-specific algorithm modifications. Our findings improve existing understanding of spatio-temporal trends in PP and bio-optical variability within the Indian Sector of the Southern Ocean (ISSO) – knowledge that is essential to improve capacity to retrieve PP from satellite-based models in this region.

Linking phytoplankton absorption to community composition in Chinese marginal seas

Phytoplankton pigments significantly affect photosynthesis and play a crucial role in regulating marine ecological and biogeochemical processes. Assessment of phytoplankton pigments through optical means is desirable as it may be extended to satellite remote sensing. Here, using an extensive data set of high performance liquid chromatography (HPLC) phytoplankton pigment concentrations and phytoplankton absorption spectra (aph(λ)) collected through five cruise surveys of the Chinese marginal seas during 2016 and 2017, we explore the potentials of using aph(λ) to estimate twenty pigments. Specifically, the first and second derivatives of aph(λ) are used to construct an aph(λ) - pigment model. The validation of the aph(λ)-derived pigment classes, specific to individual phytoplankton community and size groups, shows a generally satisfactory model performance. Additionally, hierarchical cluster analysis also exhibits high similarity within the classification results based on the measured and modeled pigments, where only two pigments were decided into different clusters. Although still preliminary in nature, this proof-of-concept study for the Chinese marginal seas shows the potentials of using satellite remote sensing to assess phytoplankton pigment composition once hyperspectral satellite data are available and aph(λ) inversion algorithms are developed and validated.

Dissolved organic matter from tropical peatlands reduces shelf sea light availability in the Singapore Strait, Southeast Asia

Shelf seas provide valuable ecosystem services, but their productivity and ecological functioning depend critically on sunlight transmitted through the water column. Anthropogenic reductions in underwater light availability are thus a serious threat to coastal habitats. The flux of light-absorbing coloured dissolved organic matter (CDOM) from land to sea may have increased world-wide, but how this has altered the availability and spectral quality of light in shelf seas remains poorly known. We present time-series data from the Sunda Shelf in Southeast Asia, where the monsoon-driven reversal in ocean currents supplies water enriched in CDOM from tropical peatlands for part of the year, resulting in 5- to 10-fold seasonal variation in light absorption by CDOM. We show that this terrigenous CDOM can dominate underwater light absorption at wavelengths up to 500 nm, and shift the underwater irradiance spectrum towards longer wavelengths. The seasonal presence of terrigenous CDOM also reduces the 10% light penetration depth by 1-5 m, or 10-45%. We estimate that on average 0.6 m, or 25%, of this terrigenous CDOM-mediated shoaling might be attributable to the enhanced input of dissolved organic matter following peatland disturbance. The seasonal change in the light environment is correlated with changes in phytoplankton absorption spectra that suggest a photo-acclimation response, and we infer that terrigenous CDOM likely contributes to limiting the depth distribution of photosynthetic corals. The results reveal an ecologically important but largely overlooked impact of human modifications to carbon fluxes that is likely increasingly important in coastal seas.

Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters

Following more than two decades of research and developments made possible through various proof-of-concept hyperspectral remote sensing missions, it has been anticipated that hyperspectral imaging would enhance the accuracy of remotely sensed in-water products. This study investigates such expected improvements and demonstrates the utility of hyperspectral radiometric measurements for the retrieval of near-surface phytoplankton properties11Codes are accessible via https://github.com/STREAM-RS/STREAM-RS, i.e., phytoplankton absorption spectra (aph) and biomass evaluated through examining the concentration of chlorophyll-a (Chla). Using hyperspectral data (409–800 nm at ~5 nm resolution) and a class of neural networks known as Mixture Density Networks (MDN) (Pahlevan et al., 2020), we show that the median error in aph retrievals is reduced two-to-three times (N = 722) compared to that from heritage ocean color algorithms. The median error associated with our aph retrieval across all the visible bands varies between 20 and 30%. Similarly, Chla retrievals exhibit significant improvements (i.e., more than two times; N = 1902), with respect to existing algorithms that rely on select spectral bands. Using an independent matchup dataset acquired near-concurrently with the acquisition of the Hyperspectral Imager for the Coastal Ocean (HICO) images, the models are found to perform well, but at reduced levels due to uncertainties in the atmospheric correction. The mapped spatial distribution of Chla maps and aph spectra for selected HICO swaths further solidify MDNs as promising machine-learning models that have the potential to generate highly accurate aquatic remote sensing products in inland and coastal waters. For aph retrieval to improve further, two immediate research avenues are recommended: a) the network architecture requires additional optimization to enable a simultaneous retrieval of multiple in-water parameters (e.g., aph, Chla, absorption by colored dissolved organic matter), and b) the training dataset should be extended to enhance model generalizability. This feasibility analysis using MDNs provides strong evidence that high-quality, global hyperspectral data will open new pathways toward a better understanding of biodiversity in aquatic ecosystems.

Vertical Distribution of Bio-Optical Properties of the Azov – Black Sea Basin Waters in April – May, 2019

Purpose. The work is aimed at investigating spatial distribution of the chlorophyll a concentration and the spectral light absorption coefficients by all optically active components in the Azov and Black seas in spring, when the seawater hydrophysical structure changes. Methods and Results. The data collected in the 106th scientific cruise of R/V Professor Vodyanitsky in April 19 – May 10, 2019 were used. The chlorophyll a concentration was measured by the spectrophotometric method. The spectral light absorption coefficients were determined in accordance with the NASA protocol 2018. The optical measurements were performed using the dual-beam spectrophotometer Lambda 35 (PerkinElmer). It was shown that in the surface layer of the Black Sea, the chlorophyll a concentration varied from 0.21 to 1.2 mg/m3. At some stations in the deep-water region, the increased values of this parameter were observed in the lower part of the euphotic zone that was associated with the beginning of seasonal water stratification due to the surface water heating. At these stations, the phytoplankton absorption spectra were more smoothed in the lower part of the euphotic zone than those in the upper layer. In the deep-water region, the non-algal particles contribution to the total particulate light absorption at wavelength 438 nm changed with depth from 40 ± 15 % at the surface to 29 ± 12 % near the bottom of the euphotic zone; whereas in the coastal waters this parameter was almost unchangeable within the water column (54 ± 11 %). No significant change of the colored dissolved organic matter contribution to the total light absorption with depth was revealed (69% on average). In the Sea of Azov, vertical distribution both of the chlorophyll a concentration (6.2 mg/m3 on average) and the spectral light absorption coefficients by all the optically active components was uniform. The non-algal particles contribution to the particulate light absorption was 40 ± 14 %, and the colored dissolved organic matter contribution to the total light absorption constituted 52 ± 6 %. Conclusions. New data on spatial distribution of the chlorophyll a concentration and the spectral light absorption coefficients by the optically active components in the Black and Azov seas were obtained for the spring period when the seawater hydrophysical characteristics changed.

Study of absorption characteristics of phytoplankton, particles and colored dissolved organic matter in Lake Baikal (July 2018 and September 2019)

Results of two scientific cruises carried out in July 2018 and September 2019 were used for an analysis of variability in spectral bio-optical properties of Lake Baikal. Chlorophyll a concentration, spectral light absorption coefficient of phytoplankton, non-algal particles and colored dissolved organic matter varied in ~order of magnitude between stations. Vertical distribution of these bio-optical parameters depended on water column stability in euphotic layer. The chlorophyll a profiles were characterized by maximum near the bottom of euphotic zone. In the layer of deep chlorophyll a maximum the phytoplankton absorption spectra had a specific feature – a local maximum at 550-570 nm. It was associated with relative increasing of phycoerythrin containing species in phytoplankton community. The absorption budget at 440 nm showed that the CDOM was the main optically active component, which provided ~ 50 % contribution to total absorption on averaged over Lake Baikal surface.

Retrieval of Phytoplankton Pigments from Underway Spectrophotometry in the Fram Strait

Phytoplankton in the ocean are extremely diverse. The abundance of various intracellular pigments are often used to study phytoplankton physiology and ecology, and identify and quantify different phytoplankton groups. In this study, phytoplankton absorption spectra ( a p h ( λ ) ) derived from underway flow-through AC-S measurements in the Fram Strait are combined with phytoplankton pigment measurements analyzed by high-performance liquid chromatography (HPLC) to evaluate the retrieval of various pigment concentrations at high spatial resolution. The performances of two approaches, Gaussian decomposition and the matrix inversion technique are investigated and compared. Our study is the first to apply the matrix inversion technique to underway spectrophotometry data. We find that Gaussian decomposition provides good estimates (median absolute percentage error, MPE 21–34%) of total chlorophyll-a (TChl-a), total chlorophyll-b (TChl-b), the combination of chlorophyll-c1 and -c2 (Chl-c1/2), photoprotective (PPC) and photosynthetic carotenoids (PSC). This method outperformed one of the matrix inversion algorithms, i.e., singular value decomposition combined with non-negative least squares (SVD-NNLS), in retrieving TChl-b, Chl-c1/2, PSC, and PPC. However, SVD-NNLS enables robust retrievals of specific carotenoids (MPE 37–65%), i.e., fucoxanthin, diadinoxanthin and 19 ′ -hexanoyloxyfucoxanthin, which is currently not accomplished by Gaussian decomposition. More robust predictions are obtained using the Gaussian decomposition method when the observed a p h ( λ ) is normalized by the package effect index at 675 nm. The latter is determined as a function of “packaged” a p h ( 675 ) and TChl-a concentration, which shows potential for improving pigment retrieval accuracy by the combined use of a p h ( λ ) and TChl-a concentration data. To generate robust estimation statistics for the matrix inversion technique, we combine leave-one-out cross-validation with data perturbations. We find that both approaches provide useful information on pigment distributions, and hence, phytoplankton community composition indicators, at a spatial resolution much finer than that can be achieved with discrete samples.

Gaussian decomposition and component pigment spectral analysis of phytoplankton absorption spectra

The absorption spectrum of phytoplankton is an important bio-optical parameter for ocean color hyperspectral remote sensing; its magnitude and shape can be affected considerably by pigment composition and concentration. We conducted Gaussian decomposition to the absorption spectra of phytoplankton pigment and studied the spectral components of the phytoplankton, in which the package effect was investigated using pigment concentration data and phytoplankton absorption spectra. The decomposition results were compared with the corresponding concentrations of the five main pigment groups (chlorophylls a, b, and c, photo-synthetic carotenoids (PSC), and photo-protective carotenoids (PPC)). The results indicate that the majority of residual errors in the Gaussian decomposition are <0.001 m−1, and R2 of the power regression between characteristic bands and HPLC pigment concentrations (except for chlorophyll b) was 0.65 or greater for surface water samples at autumn cruise. In addition, we determined a strong predictive capability for chlorophylls a, c, PPC, and PSC. We also tested the estimation of pigment concentrations from the empirical specific absorption coefficient of pigment composition. The empirical decomposition showed that the Ficek model was the closest to the original spectra with the smallest residual errors. The pigment decomposition results and HPLC measurements of pigment concentration are in a high consistency as the scatter plots are distributed largely near the 1:1 line in spite of prominent seasonal variations. The Woźniak model showed a better fit than the Ficek model for Chl a, and the median relative error was small. The pigment component information estimated from the phytoplankton absorption spectra can help better remote sensing of hyperspectral ocean color that related to the changes in phytoplankton communities and varieties.

Comparison of Satellite-Derived Phytoplankton Size Classes Using In-Situ Measurements in the South China Sea

Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with in-situ High Performance Liquid Chromatography (HPLC) data for the South China Sea (SCS), collected from August 2006 to September 2011. Four algorithms were evaluated to determine their ability to detect three phytoplankton size classes. Chlorophyll-a (Chl-a) and absorption spectra of phytoplankton (aph(λ)) were also measured to help understand PSC’s algorithm performance. Results show that the three abundance-based approaches performed better than the inherent optical property (IOP)-based approach in the SCS. The size detection of microplankton and picoplankton was generally better than that of nanoplankton. A three-component model was recommended to produce maps of surface PSCs in the SCS. For the IOP-based approach, satellite retrievals of inherent optical properties and the PSCs algorithm both have impacts on inversion accuracy. However, for abundance-based approaches, the selection of the PSCs algorithm seems to be more critical, owing to low uncertainty in satellite Chl-a input data

Phytoplankton light absorption and the package effect in relation to photosynthetic and photoprotective pigments in the northern tip of Antarctic Peninsula

AbstractThis study investigates the variability in the spectral absorption of phytoplankton in Antarctic waters. A large in situ data set comprising phytoplankton pigments and hyperspectral absorption was measured in the northern tip of Antarctic Peninsula during 2013 and 2014 summers at several depths. A proxy of package effect was estimated from the phytoplankton absorption spectra, independently of chlorophyll a. Variations in the concentration of photosynthetic and photoprotective pigments were discernible by changes in this metric but not in the chlorophyll a specific absorption coefficient of phytoplankton. The fucoxanthin to chlorophyll a ratio correlated positively to package effect due to an increase in cell size of phytoplankton (diatoms) and increasing fucoxanthin content per cell to maximize light harvesting in depth. The package effect was found to covary inversely with photoprotective pigments relative to chlorophyll a, partially due to their contribution to enhance absorption in the blue part of the spectrum. Using a cluster analysis (k‐means algorithm) on the phytoplankton absorption spectra, we illustrate the capacity to identify a regular increase in the degree of package effect. This approach can be useful to classify the phytoplankton assemblages in Antarctic waters according to different degrees of pigment packaging, each one related to a specific pigment composition. Our results demonstrate the potential for this classification at different temporal and spatial scales from ocean color satellite data. This should improve our understanding of deviations in global bio‐optical algorithms when applied to the Southern Ocean.

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

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 log10 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, R2 = 0.77 and MPD = 24%, RMSE = 0.15, R2 = 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 R2 (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.

Light absorption by phytoplankton in the North Pacific Subtropical Gyre

AbstractTo constrain the energy fueling photosynthesis in the North Pacific Subtropical Gyre (NPSG) we characterize the variability of phytoplankton absorption spectra in conjunction with that of the light field at Station ALOHA (22°45′N, 158°00′W). Furthermore, we decompose the phytoplankton absorption into photosynthetic and photoprotective components based on high‐performance liquid chromatography pigment analysis. Between January 2006 and December 2012 the variability in chlorophyll‐specific absorption ( ) above the deep chlorophyll maximum (DCM) layer was driven by changes in photoprotective carotenoid concentrations while the chlorophyll‐specific absorption of photosynthetic pigments ( ) remained nearly constant with a mean (± SD) value of 0.008 ± 0.001 m2 (mg chl a)−1. In contrast, below the DCM layer changes in resulted from increases in the relative contribution of photosynthetic pigments with depth, suggesting that the constancy in above the DCM layer is controlled by nutrient limitation. While the daily photon fluxes absorbed by photosynthetic pigments in the upper 45 m did not vary at a seasonal scale, averaging 0.45 ± 0.12 mol quanta m−2 d−1 in winter and 0.46 ± 0.10 mol quanta m−2 d−1 in summer, when integrated over the upper 200 m these fluxes ranged from 0.64 ± 0.16 to 0.79 ± 0.19 mol quanta m−2 d−1 in winter and summer, respectively. Based on the rate of photons trapped by the photosynthetic pigments and on the seasonal euphotic zone depth integrated gross O2 evolution rates derived from in situ incubations we estimate a mean photosynthetic yield of ∼0.1 mol O2 evolved per mol quanta absorbed by photosynthetic pigments.

A Spectral Model of Phytoplankton Absorption for Inland Lake

Cyanobacteria (primarily Microcystis aeruginosa; MA) blooms, caused by eutrophic waters, have a significant and negative effect on human health and on the ecosystem as a result of blue-green algal toxins. In this study, a novel phytoplankton absorption spectral model was established using normalized MA and Scenedesmus obliquus (SO) absorption spectra as reference vectors to characterize the absorption properties of cyanobacteria in an inland lake (Dongting Lake, China). This model can be used to estimate the relative contributions of cyanobacteria to the phytoplankton population and thus to phytoplankton absorption. The ability of the model to accurately estimate the percentage of MA in the phytoplankton population and to simulate phytoplankton absorption spectra was examined using in situ data, collected during four campaigns at Dongting Lake. The results show that the absorption characteristics of phycoerythrin and phycocyanin were well simulated by the model in highly turbid and eutrophic inland waters. The percentage of mean square root (RMSP) of the model is 0.71–11.90%, while the model parameters (Sm and 1 − Sm) linearly correlated to the percentage of MA (R2 = 0.70) and SO (R2 = 0.94). Sm is highly correlated with the traditional cyanobacterial percentage indicator and provides a proxy for the relative contribution of cyanobacteria to phytoplankton absorption. This phytoplankton absorption model fully considers the effect of accessory pigments, which are important for the identification and monitoring of cyanobacterial blooms. The results show that the model can successfully retrieve the composition ratio of harmful algae (MA) and well reconstruct the absorption characteristics of phycocyanin.