Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site)

Model for partitioning the non-phytoplankton absorption coefficient of seawater in the ultraviolet and visible spectral range into the contributions of non-algal particulate and dissolved organic matter

Non-algal particles and chromophoric dissolved organic matter (CDOM) are two major classes of seawater constituents that contribute substantially to light absorption in the ocean within the ultraviolet (UV) and visible (VIS) spectral regions. The similarities in the spectral shape of these two constituent absorption coefficients, a d (λ) and a g (λ), respectively, have led to their common estimation as a single combined non-phytoplankton absorption coefficient, a d g (λ), in optical remote-sensing applications. Given the different biogeochemical and ecological roles of non-algal particles and CDOM in the ocean, it is important to determine and characterize the absorption coefficient of each of these constituents separately. We describe an ADG model that partitions a d g (λ) into a d (λ) and a g (λ). This model improves upon a recently published model [Appl. Opt.58, 3790 (2019)APOPAI0003-693510.1364/AO.58.003790] through implementation of a newly assembled dataset of hyperspectral measurements of a d (λ) and a g (λ) from diverse oceanic environments to create the spectral shape function libraries of these coefficients, a better characterization of variability in spectral shape of a d (λ) and a g (λ), and a spectral extension of model output to include the near-UV (350-400 nm) in addition to the VIS (400-700 nm) part of the spectrum. We developed and tested two variants of the ADG model: the ADG_UV-VIS model, which determines solutions over the spectral range from 350 to 700 nm, and the ADG_VIS model, which determines solutions in the VIS but can also be coupled with an independent extrapolation model to extend output to the near-UV. This specific model variant is referred to as A D G _ V I S-U V E x t . Evaluation of the model with development and independent datasets demonstrates good performance of both ADG_UV-VIS and A D G _ V I S-U V E x t . Comparative analysis of model-derived and measured values of a d (λ) and a g (λ) indicates negligible or small median bias, generally within ±5% over the majority of the 350-700 nm spectral range but extending to or above 10% near the ends of the spectrum, and the median percent difference generally below 20% with a maximum reaching about 30%. The presented ADG models are suitable for implementation as a component of algorithms in support of satellite ocean color missions, especially the NASA PACE mission.

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.

Recent Progress on Wave Energy Marine Buoys

This study aims to introduce and discuss the recent research, development and application of wave energy marine buoys. The topic becomes increasingly appealing after the observation that wave energy technologies have been evolving in the recent decades, yet have not reached convergence. The power supply is usually the bottleneck for marine distributed systems such as buoys. Wave energy technologies are especially useful in this sense, as they can capture and convert the promising “native” renewable energy in the ocean (i.e., wave energy) into electricity. The paper enumerates the recent developments in wave energy capture (e.g., oscillating bodies) and power take-off (e.g., nanogenerators). The study also introduces the typical marine buoys and discusses the applicability of wave energy technologies on them. It is concluded that the wave energy technologies could be implemented as a critical addition to the comprehensive power solution of marine distributed systems. Wave energy buoys are likely to differentiate into “wave energy converter buoys” and “wave-energy-powered buoys”, which is indicated by the ratio of the generated power to the load power.

Evaluation of diagnostic pigments to estimate phytoplankton size classes

Phytoplankton accessory pigments are commonly used to estimate phytoplankton size classes, particularly during development and validation of biogeochemical models and satellite ocean color-based algorithms. The diagnostic pigment analysis (DPA) is based on bulk measurements of pigment concentrations and relies on assumptions regarding the presence of specific pigments in different phytoplankton taxonomic groups. Three size classes are defined by the DPA: picoplankton, nanoplankton, and microplankton. Until now, the DPA has not been evaluated against an independent approach that provides phytoplankton size calculated on a per-cell basis. Automated quantitative cell imagery of microplankton and some nanoplankton, used in combination with conventional flow cytometry for enumeration of picoplankton and nanoplankton, provide a novel opportunity to perform an independent evaluation of the DPA. Here, we use a data set from the North Atlantic Ocean that encompasses all seasons and a wide range of chlorophyll concentrations (0.18-5.14 mg m^-3). Results show that the DPA overestimates microplankton and picoplankton when compared to cytometry data, and subsequently underestimates the contribution of nanoplankton to total biomass. In contrast to the assumption made by the DPA that the microplankton size class is largely made up of diatoms and dinoflagellates, imaging-in-flow cytometry shows significant presence of diatoms and dinoflagellates in the nanoplankton size class. Additionally, chlorophyll b is commonly attributed solely to picoplankton by the DPA, but Chl b-containing phytoplankton are observed with imaging in both nanoplankton and microplankton size classes. We suggest revisions to the DPA equations and application of uncertainties when calculating size classes from diagnostic pigments.

Information content of absorption spectra and implications for ocean color inversion

The increasing use of hyperspectral optical data in oceanography, both in situ and via remote sensing, holds the potential to significantly advance characterization of marine ecology and biogeochemistry because, in principle, hyperspectral data can provide much more detailed inferences of ecosystem properties via inversion. Effective inferences, however, require careful consideration of the close similarity of different signals of interest, and how these interplay with measurement error and uncertainty to reduce the degrees of freedom (DoF) of hyperspectral measurements. Here we discuss complementary approaches to quantify the DoF in hyperspectral measurements in the case of in situ particulate absorption measurements, though these approaches can also be used on other such data, e.g., ocean color remote sensing. Analyses suggest intermediate (${\sim}5 $∼5) DoF for our dataset of global hyperspectral particulate absorption spectra from the Tara Oceans expedition, meaning that these data can yield coarse community structure information. Empirically, chlorophyll is an effective first-order predictor of absorption spectra, meaning that error characteristics and the mathematics of inversion need to be carefully considered for hyperspectral data to provide information beyond that which chlorophyll provides. We also discuss other useful analytical tools that can be applied to this problem and place our results in the context of hyperspectral remote sensing.

The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic

We present a model that estimates the spectral phytoplankton absorption coefficient (aph(λ)) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on aph(λ) (at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total aph(λ) with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine aph(λ) for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific aph(λ) using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.

Synoptic relationships to estimate phytoplankton communities specific to sizes and species from satellite observations in coastal waters

Knowing variations of phytoplankton community characteristics is of great significance to many marine ecological and biogeochemical processes in oceanography and related fields research. Satellite remote sensing provides the only viable path for continuously detecting phytoplankton community characteristics in the large-scale spatial areas. However, remote sensing approaches are currently hindered by limited understanding on reflectance responses to variations from phytoplankton community compositions and further do not achieve a true application by satellite observations. Here we analyze in situ observation data sets from three cruises in a dynamic marine environment covering those coastal water areas in the marginal seas of the Pacific Northwest (Bohai Sea, Yellow Sea, and East China Sea). The size/species-specific phytoplankton assemblages can be quantitatively defined by the high-performance liquid chromatography (HPLC)-derived phytoplankton pigments and customized diagnostic pigment analysis, as well as a matrix factorization "CHEMTAX" program. Therein, note that a suit of updated weight values for diagnostic pigments are proposed with better performance than others. The above-mentioned size/species-specific phytoplankton assemblages include three size classes, i.e., micro-, nano-, and picoplankton, and eight species typically existing in the investigated water areas. Relationship analysis illustrates us that relatively close and robust models can be established to associate three size-specific and four dominant species-specific phytoplankton biomasses with the total chlorophyll a. Those models are then applied to the Geostationary Ocean Color Imager (GOCI) images for the whole 2015 year, which generated annual mean distributions of size/species-specific phytoplankton biomasses. The current study represents a meaningful attempt to achieve the satellite remote-sensing retrievals on the phytoplankton community composition, especially the species-specific phytoplankton biomass in the study region.

Model for separating the contributions of non-algal particles and colored dissolved organic matter to light absorption by seawater

We evaluated the performance of a recently developed absorption partitioning model [J. Geophys. Res. Oceans120, 2601 (2015)JGRCEY0148-022710.1002/2014JC010604] that derives the spectral absorption coefficients of non-algal particles, a _{N A P} (λ), and colored dissolved organic matter, a _g (λ), from the total absorption coefficient of seawater. The model's performance was found unsatisfactory when the model was tested with a large dataset of absorption measurements from diverse open-ocean and coastal aquatic environments. To address these limitations, we developed a new model based on a different approach for estimating a _{N A P} (λ) and a _g (λ) from the sum of these two coefficients, a _{d g} (λ), within the visible spectral region. The very good overall performance of the model is demonstrated, with no tendency for bias and relatively small absolute differences (the median ≤20%) between the model-derived and measured values of a _{N A P} (λ) and a _g (λ) over a wide range of aquatic environments.

Retrieving Phytoplankton Size Class from the Absorption Coefficient and Chlorophyll A Concentration Based on Support Vector Machine

The phytoplankton size class (PSC) plays an important role in biogeochemical processes in the ocean. In this study, a regional model of PSCs is proposed to retrieve vertical PSCs from the total minus water absorption coefficient (at-w(λ)) and Chlorophyll a concentration (Chla). The PSC model is developed by first reconstructing phytoplankton absorption and Chla from at-w(λ), and then extracting PSC from them using the support vector machine (SVM). In situ bio-optical data collected in the South China Sea from 2006 to 2013 were used to train the SVM. The proposed PSC model was subsequently validated using an independent PSC dataset from the Northeast South China Sea Cruise in 2015. The results indicate that the PSC model performed better than the three components model, with a value of r2 between 0.35 and 0.66, and the absolute percentage difference between 56% and 181%. On the whole, our PSC model shows a remarkable utility in terms of inferring vertical PSCs from the South China Sea.

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.

Concentrations of Multiple Phytoplankton Pigments in the Global Oceans Obtained from Satellite Ocean Color Measurements with MERIS

The remote sensing of chlorophyll a concentration from ocean color satellites has been an essential variable quantifying phytoplankton in the past decades, yet estimation of accessory pigments from ocean color remote sensing data has remained largely elusive. In this study, we validated the concentrations of multiple pigments (Cpigs) retrieved from in situ and MEdium Resolution Imaging Spectrometer (MERIS) measured remote sensing reflectance (Rrs(λ)) in the global oceans. A multi-pigment inversion model (MuPI) was used to semi-analytically retrieve Cpigs from Rrs(λ). With a set of globally optimized parameters, the accuracy of the retrievals obtained with MuPI is quite promising. Compared with High-Performance Liquid Chromatography (HPLC) measurements near Bermuda, the concentrations of chlorophyll a, b, c ([Chl-a], [Chl-b], [Chl-c]), photoprotective carotenoids ([PPC]), and photosynthetic carotenoids ([PSC]) can be retrieved from MERIS data with a mean unbiased absolute percentage difference of 38%, 78%, 65%, 36%, and 47%, respectively. The advantage of the MuPI approach is the simultaneous retrievals of [Chl-a] and the accessory pigments [Chl-b], [Chl-c], [PPC], [PSC] from MERIS Rrs(λ) based on a closure between the input and output Rrs(λ) spectra. These results can greatly expand scientific studies of ocean biology and biogeochemistry of the global oceans that are not possible when the only available information is [Chl-a].

An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral waterleaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches.

On the discrimination of multiple phytoplankton groups from light absorption spectra of assemblages with mixed taxonomic composition and variable light conditions

According to recommendations of the international community of phytoplankton functional type algorithm developers, a set of experiments on marine algal cultures was conducted to (1) investigate uncertainties and limits in phytoplankton group discrimination from hyperspectral light absorption properties of assemblages with mixed taxonomic composition, and (2) evaluate the extent to which modifications of the absorption spectral features due to variable light conditions affect the optical discrimination of phytoplankton. Results showed that spectral absorption signatures of multiple species can be extracted from mixed assemblages, even at low relative contributions. Errors in retrieved pigment abundances are, however, influenced by the co-occurrence of species with similar spectral features. Plasticity of absorption spectra due to changes in light conditions weakly affects interspecific differences, with errors <21% for retrievals of pigment concentrations from mixed assemblages.

Cost-Effective Hyperspectral Transmissometers for Oceanographic Applications: Performance Analysis

The recent development of inexpensive, compact hyperspectral transmissometers broadens the research capabilities of oceanographic applications. These developments have been achieved by incorporating technologies such as micro-spectrometers as detectors as well as light emitting diodes (LEDs) as light sources. In this study, we evaluate the performance of the new commercial LED-based hyperspectral transmissometer VIPER (TriOS GmbH, Rastede, Germany), which combines different LEDs to emulate the visible light spectrum, aiming at the determination of attenuation coefficients in coastal environments. For this purpose, experimental uncertainties related to the instrument stability, the effect of ambient light and derived temperature, and salinity correction factors are analyzed. Our results identify some issues related to the thermal management of the LEDs and the contamination of ambient light. Furthermore, the performance of VIPER is validated against other transmissometers through simultaneous field measurements. It is demonstrated that VIPER provides a compact and cost-effective alternative for beam attenuation measurements in coastal waters, but it requires the consideration of several optimizations.

Inversion of spectral absorption coefficients to infer phytoplankton size classes, chlorophyll concentration, and detrital matter

Measured spectral absorption coefficients were inverted to infer phytoplankton concentration in three size classes (picoplankton, nanoplankton, and microplankton), chlorophyll concentration [Chl], and both magnitude and spectral shape of absorption by colored detrital matter (CDM). Our algorithm allowed us to solve for the nonlinear factor of CDM absorption slope separately from the other linear factors, thus fully utilizing the additive characteristic inherent in absorption coefficients. We validated the inversion with three datasets: two spatially distributed global datasets, the Laboratoire d'Océanographie de Villefranche dataset and the NASA bio-Optical Marine Algorithm Dataset, and a time series coastal dataset, the Martha's Vineyard Coastal Observatory dataset. Comparison with high performance liquid chromatography analyses showed that the phytoplankton size classes can be retrieved with correlation coefficients (r)>0.7, root mean square errors of 0.2, and median relative errors of 20% in oceanic waters and with similar performance in coastal waters. Much improved agreement was found for the entire phytoplankton population, with r>0.90 for [Chl] and absorption coefficients (a_ph) for all three datasets. The inferred a_CDM(400) and CDM spectral slope agree within ±4% of measurements in both oceanic and coastal waters. The results indicate that the chlorophyll-a specific absorption spectra used as an inversion kernel represent well the global mean states for each of the three phytoplankton size classes. The method can be applied to either bulk or particulate absorption data and is spectrally flexible.

Remote estimation of phytoplankton size fractions using the spectral shape of light absorption

Phytoplankton size structure plays an important role in ocean biogeochemical processes. The light absorption spectra of phytoplankton provide a great potential for retrieving phytoplankton size structure because of the strong dependence on the packaging effect caused by phytoplankton cell size and on different pigment compositions related to phytoplankton taxonomy. In this study, we investigated the variability in light absorption spectra of phytoplankton in relation to the size structure. Based on this, a new approach was proposed for estimating phytoplankton size fractions. Our approach use the spectral shape of the normalized phytoplankton absorption coefficient (a(ph)(λ)) through principal component analysis (PCA). Values of a(ph)(λ) were normalized to remove biomass effects, and PCA was conducted to separate the spectral variance of normalized a(ph)(λ) into uncorrelated principal components (PCs). Spectral variations captured by the first four PC modes were used to build relationships with phytoplankton size fractions. The results showed that PCA had powerful ability to capture spectral variations in normalized a(ph)(λ), which were significantly related to phytoplankton size fractions. For both hyperspectral a(ph)(λ) and multiband a(ph)(λ), our approach is applicable. We evaluated our approach using wide in situ data collected from coastal waters and the global ocean, and the results demonstrated a good and robust performance in estimating phytoplankton size fractions in various regions. The model performance was further evaluated by a(ph)(λ) derived from in situ remote sensing reflectance (R(rs)(λ)) with a quasi-analytical algorithm. Using R(rs)(λ) only at six bands, accurate estimations of phytoplankton size fractions were obtained, with R(2) values of 0.85, 0.61, and 0.76, and root mean-square errors of 0.130, 0.126, and 0.112 for micro-, nano-, and picophytoplankton, respectively. Our approach provides practical basis for remote estimation of phytoplankton size structure using a(ph)(λ) derived from satellite observations or rapid field instrument measurements in the future.

Novel method for quantifying the cell size of marine phytoplankton based on optical measurements

Phytoplankton size is important for the pelagic food web and oceanic ecosystems. However, the size of phytoplankton is difficult to quantify because of methodological constraints. To address this limitation, we have exploited the phytoplankton package effect to develop a new method for estimating the mean cell size of individual phytoplankton populations. This method was validated using a data set that contained simultaneous measurements of phytoplankton absorption and cell size distributions from 13 phytoplankton species. Comparing with existing methods, our method is more efficient with good accuracy, and it could potentially be applied in current in situ optical instruments.