Measurements of the particulate volume scattering function, βp(ψ), at light wavelength of 532 nm, particle size distribution, PSD, and several metrics of particulate concentration and composition ...were made on eight contrasting seawater samples from nearshore and coastal oceanic environments including river estuary and offshore locations. Both βp(ψ) and PSDs were measured on original (unfiltered) samples and particle sizefractionated samples obtained through filtration using mesh filters with pore sizes of 5 and 20 μm. We present results based on direct size-fractionated measurements and data adjusted for imperfect fractionation, which provide insights into the roles played by particle size and composition in angle-resolved light scattering produced by highly variable natural assemblages of aquatic particles. Despite intricate interplay between the effects of particle size and composition, small particles (< 5 μm in size) consistently produced a major or dominant contribution (~ 50–80%) to the particulate backscattering coefficient, bbp, in organic, either phytoplankton or nonalgal, dominated samples regardless of significant variations in PSD between these samples. The notable exception was a sample dominated by large-celled diatoms from microphytoplankton size range, which exemplifies a scenario when large particles (> 20 μm) can produce a considerable contribution (~ 40%) to b
bp. We also observed a trend for inorganic-dominated samples exhibiting consistently lower contributions (~ 30–40%) of small particles to b
bp. The particle size-based budget for the particulate scattering coefficient, b
p, indicates a significant decrease in the role of small particles accompanied by an increase in the role of larger particles compared to the b
bp budget.
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The magnitude and spectral shape of the optical backscattering coefficient of particles, b
bp(λ), is being increasingly used to infer information about the particles present in seawater. ...Relationships between b
bp and particle properties in the Arctic are poorly documented, and may differ from other oceanic regions which contribute the majority of data used to develop and parameterize optical models. We utilize recent field measurements from the Chukchi and Beaufort Seas to examine relationships between the spectral backscattering coefficient of particles in seawater and the mass concentration, bulk composition, and size distribution of the suspended particle assemblage. The particle backscattering coefficient spanned six orders of magnitude from the relatively clear waters of the Beaufort Sea to extremely turbid waters on the Mackenzie shelf. This coefficient was highly correlated with the mass concentration of particles, and to a lesser extent with other measures of concentration such as particulate organic carbon or chlorophyll a. Increased backscattering and high mass-specific b
bp(λ) was associated with mineral-rich assemblages that tended to exhibit steeper size distributions, while reduced backscattering was associated with organic-dominated assemblages having a greater contribution of large particles. Our results suggest that algorithms which employ composition-specific relationships can lead to improved estimates of particle mass concentration from backscattering measurements. In contrast to theoretical models, however, we observe no clear relationship between the spectral slope of b
bp(λ) and the slope of the particle size distribution in this environment.
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We apply an innovative approach to time series data of surface chlorophyll from satellite observations with SeaWiFS (Sea‐viewing Wide Field‐of‐view Sensor) to estimate the primary production ...associated with three major phytoplankton classes (micro‐, nano‐, and picophytoplankton) within the world's oceans. Statistical relationships, determined from an extensive in situ database of phytoplankton pigments, are used to infer class‐specific vertical profiles of chlorophyll a concentration from satellite‐derived surface chlorophyll a. This information is combined with a primary production model and class‐specific photophysiological parameters to compute global seasonal fields of class‐specific primary production over a 10‐year period from January 1998 through December 2007. Microphytoplankton (mostly diatoms) appear as a major contributor to total primary production in coastal upwelling systems (70%) and temperate and subpolar regions (50%) during the spring‐summer season. The contribution of picophytoplankton (e.g., prokaryotes) reaches maximum values (45%) in subtropical oligotrophic gyres. Nanophytoplankton (e.g., prymnesiophytes) provide a ubiquitous, substantial contribution (30–60%). Annual global estimates of class‐specific primary production amount to 15 Gt C yr−1 (32% of total), 20 Gt C yr−1 (44%) and 11 Gt C yr−1 (24%) for micro‐, nano‐, and picophytoplankton, respectively. The analysis of interannual variations revealed large anomalies in class‐specific primary production as compared to the 10‐year mean cycle in both the productive North Atlantic basin and the more stable equatorial Pacific upwelling. Microphytoplankton show the largest range of variability of the three phytoplankton classes on seasonal and interannual time scales. Our results contribute to an understanding and quantification of carbon cycle in the ocean.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
This study assesses the ability of hyperspectral optical measurements to discriminate changes in the composition of phytoplankton communities in open-ocean non-bloom environments. A large set of in ...situ near-surface measurements, comprising phytoplankton pigment determinations and hyperspectral optical data of phytoplankton absorption coefficient, aph(λ), and remote-sensing reflectance, Rrs(λ), are used in the analysis. Measurements were collected in different ecological provinces in the Pacific and Atlantic Oceans with chlorophyll-a concentrations ranging from about 0.02 to 1.5mgm−3. Hierarchical cluster analysis was applied to measured spectra of aph(λ) and Rrs(λ) and the second-derivative spectra of these optical variables. The resulting optical-based classifications of the examined stations compared favorably (similarity index≥0.73) with a classification of phytoplankton community composition calculated from pigment measurements. Similarities between pigment-based and optically-based classifications were better for the optical data of aph(λ) than Rrs(λ), with only slight improvements resulting from the use of the second derivative spectra as opposed to the non-differentiated spectra. An Empirical Orthogonal Function (EOF) analysis was applied to the optical spectra to examine the correlation of dominant modes of variability with several bio-optical and biogeochemical properties. This analysis supports the notion that the performance of the optical approach is strongly associated with the effects of differences in pigment composition, cell size, and intracellular pigment concentration among different phytoplankton communities on the optical properties of the ocean.
•Optical-based classifications are similar to those based on phytoplankton pigments.•EOF analysis identifies dominant modes of variability in shape of optical spectra.•Optical discrimination driven by pigment composition, concentration, and cell size
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Ocean color algorithms for obtaining marine inherent optical properties are reviewed.•Known uncertainties associated with each approach and knowledge gaps are discussed.•Common performance metrics ...used to evaluate the satellite retrievals are provided.•Recommendations for future investment for upcoming satellite missions are presented.
Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving 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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
We develop an optical classification of marine particle assemblages from an extensive dataset of particle optical properties collected in the Chukchi and Beaufort Seas. Hierarchical cluster analysis ...of the spectral particulate backscattering-to-absorption ratio partitioned the dataset into seven optically-distinct clusters of particle assemblages, each associated with different characteristics of particle concentration, composition, and phytoplankton taxonomic composition and size. Three phytoplankton-dominated clusters were identified. One was characterized by small-sized phytoplankton that are typically associated with regenerated production, and comprised samples from the subsurface chlorophyll-a maximum in oligotrophic waters of the Beaufort Sea. The other two clusters represented diatom-dominated particle assemblages in turbid shelf waters with differing contributions of photoprotective pigments. Such situations are generally associated with significant new production. Two clusters were dominated by organic nonalgal material, one representing clear waters off the shelf, the other representative of post-diatom bloom conditions in the Chukchi Sea. Another distinct cluster represented mineral-dominated particle assemblages that were observed in the Colville and Mackenzie River plumes and near the seafloor. Finally, samples in a cluster of mixed particle composition were scattered throughout all locations. Optical classification improved performance of predictive bio-optical relationships. These results demonstrate a capability to discriminate distinct assemblages of suspended particles associated with specific ecological conditions from hyperspectral measurements of optical properties, and the potential for identification of ecological provinces at synoptic time and space scales from optical sensors. Analogous analysis of multispectral optical data strongly reduced this capability.
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An extensive bio-optical data set from field measurements was used to evaluate the performance of standard Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view ...Sensor (SeaWiFS) ocean color (in-water) algorithms in the Baltic Sea, which represents an example of optically complex Case 2 waters with high concentration of colored dissolved organic matter (CDOM). The data set includes coincident measurements of radiometric quantities, chlorophyll
a concentration (Chl
a), and absorption coefficient of CDOM, which were taken on 25 cruises between 1993 and 2001. The data cover a wide range of variability with Chl
a in surface waters from about 0.3 to 100 mg m
−3. All the MODIS pigment algorithms examined as well as the SeaWiFS OC4v4 algorithm showed a systematic and large overestimation in chlorophyll retrievals. The mean systematic and random errors based on our entire data set exceeded 150% or even 200% in some cases, making these standard algorithms inadequate for pigment determinations in the Baltic. Although new parameterization of the standard pigment algorithms based on our field measurements in the Baltic resulted in a significant reduction of errors, the overall performance of such regionally tuned algorithms remained unsatisfactory. For example, the mean normalized bias (MNB) for the regionally tuned MODIS chlor_a_2 algorithm was reduced to 26% (from over 200% for the standard algorithm), but the root mean square (RMS) error was still large (>100%). The MODIS K_490 algorithm for estimating the diffuse attenuation coefficient of downwelling irradiance showed the best performance among all the algorithms examined. With the new coefficients based on our field data, the regional version of this algorithm showed an acceptable level of errors, MNB=4% and RMS=30%. In addition to the apparent problems of the standard in-water bio-optical algorithms, we found that the atmospheric correction currently in use for MODIS and SeaWiFS imagery usually fails to retrieve upwelling radiances emerging from the Baltic Sea. The match-up comparisons of the coincident in situ and satellite determinations of normalized water-leaving radiances showed generally poor agreement, especially in the blue spectral region. It appears that new approaches for ocean color algorithms are required in the Baltic Sea.
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We evaluated the performance of the Quasi-Analytical Algorithm (QAA, v5 with modifications) for deriving the spectral total absorption, a(λ), and backscattering, bb(λ), coefficients of seawater and ...partitioning of a(λ) into phytoplankton and non-phytoplankton components from input spectrum of remote-sensing reflectance, Rrs(λ), with field data collected in the Arctic and lower-latitude open waters from the Atlantic and Pacific Oceans. The systematic error based on median ratio between QAA-derived and measured a(λ) varied from about 1% to ±10% depending on light wavelength and the oceanic region. The QAA typically overestimated bb(λ) from 3% to 14% compared with field measurements. These results were obtained with a correction for Raman-scattering contribution to Rrs and separate parameterization of molecular and particulate backscattering in the Rrs vs. bb/a relationship. Without these features the earlier versions of the QAA can overestimate bb(λ) by as much as 35% in clear waters. The use of pure seawater backscattering coefficients accounting for water temperature and salinity improved the accuracy of QAA-derived a(λ) in Arctic waters. The absorption-partitioning component of the QAA significantly underestimated phytoplankton absorption and overestimated non-phytoplankton absorption in both Arctic and lower-latitude waters.
•An inverse ocean reflectance model (QAA) is evaluated with in situ optical data.•The absorption coefficient of seawater is derived generally with good accuracy.•Performance of different versions of the QAA model is tested.•Accounting for Raman scattering in QAA is essential in clear waters.•The absorption partitioning part of the QAA shows large systematic errors.
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Accurate estimates of the oceanic particulate organic carbon concentration (POC) from optical measurements have remained challenging because interactions between light and natural assemblages of ...marine particles are complex, depending on particle concentration, composition, and size distribution. In particular, the applicability of a single relationship between POC and the spectral particulate backscattering coefficient
b
bp
(λ) across diverse oceanic environments is subject to high uncertainties because of the variable nature of particulate assemblages. These relationships have nevertheless been widely used to estimate oceanic POC using, for example,
in situ
measurements of
b
bp
from Biogeochemical (BGC)-Argo floats. Despite these challenges, such an
in situ
based approach to estimate POC remains scientifically attractive in view of the expanding global-scale observations with the BGC-Argo array of profiling floats equipped with optical sensors. In the current study, we describe an improved empirical approach to estimate POC which takes advantage of simultaneous measurements of
b
bp
and chlorophyll-a fluorescence to better account for the effects of variable particle composition on the relationship between POC and
b
bp
. We formulated multivariable regression models using a dataset of field measurements of POC,
b
bp
, and chlorophyll-a concentration (Chla), including surface and subsurface water samples from the Atlantic, Pacific, Arctic, and Southern Oceans. The analysis of this dataset of diverse seawater samples demonstrates that the use of
b
bp
and an additional independent variable related to particle composition involving both
b
bp
and Chla leads to notable improvements in POC estimations compared with a typical univariate regression model based on
b
bp
alone. These multivariable algorithms are expected to be particularly useful for estimating POC with measurements from autonomous BGC-Argo floats operating in diverse oceanic environments. We demonstrate example results from the multivariable algorithm applied to depth-resolved vertical measurements from BGC-Argo floats surveying the Labrador Sea.
A dataset of nearly 400 measurements of the particle size distribution (PSD) compiled from the Pacific, Atlantic, and Arctic Oceans is used to examine variability in the magnitude and shape of the ...PSD, and to characterize the partitioning of particle number, cross‐sectional area, and volume concentration among defined size intervals. The results indicate that the relative contributions of three size classes based upon the pico‐, nano‐, and microplankton size range exhibit substantial changes among measures of particle size and between oceanic environments. The single‐slope power law model commonly employed to characterize the PSD in aquatic studies is demonstrated to have significant limitations in capturing the complexity of PSD shapes observed for natural particle assemblages, and in consequence poorly predicts the relative contributions of these different size intervals. We show that specific percentile diameters derived from the cumulative distributions of particle size are strongly correlated with the contributions of these three size classes, and that these non‐parametric descriptors of the cumulative distribution provide superior performance for estimating their contributions while requiring no assumption of underlying PSD shape. A comparison of these predictive relationships with independent field measurements suggests that this approach is generally robust for particle assemblages representing a wide diversity of marine environments.
Plain Language Summary
The particle size distribution (PSD) describes how the concentration of particles changes with particle size, and it is an important characteristic of suspended oceanic particles that influences ocean ecology and biogeochemistry. We analyze an extensive set of measurements of the PSD from the Pacific, Atlantic, and Arctic Oceans to characterize how different size classes of particles contribute to the total concentration of particle number, cross‐sectional area, and volume. Our results quantify this partitioning and demonstrate the variability observed among different oceanic environments. A frequently used model of the PSD employed for aquatic studies is shown to have strong limitations in representing these measurements of natural samples, and consequently performs poorly in estimating the relative contributions of individual size classes. We show that a novel approach of describing the PSD based on specific percentile diameters derived from the cumulative distribution of size‐dependent particle concentration provides a better means to characterize the shape of the PSD for oceanic particle assemblages, and provides superior performance in estimating size class contributions in diverse marine environments. Further research establishing relationships between these percentile diameters and optical proxies obtained from autonomous sensing platforms could greatly extend the observational capabilities of particle size in the oceanic environment.
Key Points
Measurements from diverse oceanic environments are used to characterize variability in the size distribution of suspended particles
A power law model performs poorly in estimating relative contributions of three size classes to the particle size distribution
A novel approach is developed that utilizes percentiles of the cumulative particle size distribution to predict size class contributions
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