Model of phytoplankton absorption based on three size classes

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.

Predicting global distributions of eukaryotic plankton communities from satellite data

Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic-subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.

A Self-Improving Framework for Joint Depth Estimation and Underwater Target Detection from Hyperspectral Imagery

Underwater target detection (UTD) is one of the most attractive research topics in hyperspectral imagery (HSI) processing. Most of the existing methods are presented to predict the signatures of desired targets in an underwater context but ignore the depth information which is position-sensitive and contributes significantly to distinguishing the background and target pixels. So as to take full advantage of the depth information, in this paper a self-improving framework is proposed to perform joint depth estimation and underwater target detection, which exploits the depth information and detection results to alternately boost the final detection performance. However, it is difficult to calculate depth information under the interference of a water environment. To address this dilemma, the proposed framework, named self-improving underwater target detection framework (SUTDF), employs the spectral and spatial contextual information to pick out target-associated pixels as the guidance dataset for depth estimation work. Considering the incompleteness of the guidance dataset, an expectation-maximum liked updating scheme has also been developed to iteratively excavate the statistical and structural information from input HSI for further improving the diversity of the guidance dataset. During each updating epoch, the calculated depth information is used to yield a more diversified dataset for the target detection network, leading to a more accurate detection result. Meanwhile, the detection result will in turn contribute in detecting more target-associated pixels as the supplement for the guidance dataset, eventually promoting the capacity of the depth estimation network. With this specific self-improving framework, we can provide a more precise detection result for a hyperspectral UTD task. Qualitative and quantitative illustrations verify the effectiveness and efficiency of SUTDF in comparison with state-of-the-art underwater target detection methods.

Dynamic partitioning of tropical Indian Ocean surface waters using ocean colour data - management and modelling applications

Over the past few decades, partitioning of the surface ocean into ecologically-meaningful spatial domains has been approached using a range of data types, with the aim of improving our understanding of open ocean processes, supporting marine management decisions and constraining coupled ocean-biogeochemical models. The simplest partitioning method, which could provide low-latency information for managers at low cost, remains a purely optical classification based on ocean colour remote sensing. The question is whether such a simple approach has value. Here, the efficacy of optical classifications in constraining physical variables that modulate the epipelagic environment is tested for the tropical Indian Ocean, with a focus on the Chagos marine protected area (MPA). Using remote sensing data, it was found that optical classes corresponded to distinctive ranges of wind speed, wind stress curl, sea surface temperature, sea surface slope, sea surface height anomaly and geostrophic currents (Kruskal-Wallis and post-hoc Tukey honestly significantly different tests, α = 0.01). Between-class differences were significant for a set of sub-domains that resolved zonal and meridional gradients across the MPA and Seychelles-Chagos Thermocline Ridge, whereas between-domain differences were only significant for the north-south gradient (PERMANOVA, α = 0.01). A preliminary test of between-class differences in surface CO₂ concentrations from the Orbiting Carbon Observatory-2 demonstrated a small decrease in mean pCO₂ with increasing chlorophyll (chl), from 418 to 398 ppm. Simple optical class maps therefore provide an overview of growth conditions, the spatial distribution of resources - from which habitat fragmentation metrics can be calculated, and carbon sequestration potential. Within the 17 year study period, biotic variables were found to have decreased at up to 0.025%a^-1 for all optical classes, which is slower than reported elsewhere (Mann-Kendall-Sen regression, α = 0.01). Within the MPA, positive Indian Ocean Dipole conditions and negative Southern Oscillation Indices were weakly associated with decreasing chl, fluorescence line height (FLH), eddy kinetic energy, easterly wind stress and wind stress curl, and with increasing FLH/chl, sea surface temperature, SSH gradients and northerly wind stress, consistent with reduced surface mixing and increased stratification. The optical partitioning scheme described here can be applied in Google Earth Engine to support management decisions at daily or monthly scales, and potential applications are discussed.

Reconciling models of primary production and photoacclimation [Invited]

Primary production and photoacclimation models are two important classes of physiological models that find applications in remote sensing of pools and fluxes of carbon associated with phytoplankton in the ocean. They are also key components of ecosystem models designed to study biogeochemical cycles in the ocean. So far, these two classes of models have evolved in parallel, somewhat independently of each other. Here we examine how they are coupled to each other through the intermediary of the photosynthesis-irradiance parameters. We extend the photoacclimation model to accommodate the spectral effects of light penetration in the ocean and the spectral sensitivity of the initial slope of the photosynthesis-irradiance curve, making the photoacclimation model fully compatible with spectrally resolved models of photosynthesis in the ocean. The photoacclimation model contains a parameter θ_m, which is the maximum chlorophyll-to-carbon ratio that phytoplankton can attain when available light tends to zero. We explore how size-class-dependent values of θ_m could be inferred from field data on chlorophyll and carbon content in phytoplankton, and show that the results are generally consistent with lower bounds estimated from satellite-based primary production calculations. This was accomplished using empirical models linking phytoplankton carbon and chlorophyll concentration, and the range of values obtained in culture measurements. We study the equivalence between different classes of primary production models at the functional level, and show that the availability of a chlorophyll-to-carbon ratio facilitates the translation between these classes. We discuss the importance of the better assignment of parameters in primary production models as an important avenue to reduce model uncertainties and to improve the usefulness of satellite-based primary production calculations in climate research.

Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades

Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of primary production with remote-sensing observations of phytoplankton biomass. One of the major challenges in this approach lies in the assignment of the appropriate model parameters that define the photosynthetic response of phytoplankton to the light field. In the present study, a global database of in situ measurements of photosynthesis versus irradiance (P-I) parameters and a 20-year record of climate quality satellite observations were used to assess global primary production and its variability with seasons and locations as well as between years. In addition, the sensitivity of the computed primary production to potential changes in the photosynthetic response of phytoplankton cells under changing environmental conditions was investigated. Global annual primary production varied from 38.8 to 42.1 Gt C yr − 1 over the period of 1998–2018. Inter-annual changes in global primary production did not follow a linear trend, and regional differences in the magnitude and direction of change in primary production were observed. Trends in primary production followed directly from changes in chlorophyll-a and were related to changes in the physico-chemical conditions of the water column due to inter-annual and multidecadal climate oscillations. Moreover, the sensitivity analysis in which P-I parameters were adjusted by ±1 standard deviation showed the importance of accurately assigning photosynthetic parameters in global and regional calculations of primary production. The assimilation number of the P-I curve showed strong relationships with environmental variables such as temperature and had a practically one-to-one relationship with the magnitude of change in primary production. In the future, such empirical relationships could potentially be used for a more dynamic assignment of photosynthetic rates in the estimation of global primary production. Relationships between the initial slope of the P-I curve and environmental variables were more elusive.

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.

Factors Regulating the Relationship Between Total and Size-Fractionated Chlorophyll-a in Coastal Waters of the Red Sea

Phytoplankton biomass and size structure are recognized as key ecological indicators. With the aim to quantify the relationship between these two ecological indicators in tropical waters and understand controlling factors, we analyzed the total chlorophyll-a concentration, a measure of phytoplankton biomass, and its partitioning into three size classes of phytoplankton, using a series of observations collected at coastal sites in the central Red Sea. Over a period of 4 years, measurements of flow cytometry, size-fractionated chlorophyll-a concentration, and physical-chemical variables were collected near Thuwal in Saudi Arabia. We fitted a three-component model to the size-fractionated chlorophyll-a data to quantify the relationship between total chlorophyll and that in three size classes of phytoplankton [pico- (<2 μm), nano- (2–20 μm) and micro-phytoplankton (>20 μm)]. The model has an advantage over other more empirical methods in that its parameters are interpretable, expressed as the maximum chlorophyll-a concentration of small phytoplankton (pico- and combined pico-nanophytoplankton, Cpm and Cp,nm, respectively) and the fractional contribution of these two size classes to total chlorophyll-a as it tends to zero (D_p and D_p,n). Residuals between the model and the data (model minus data) were compared with a range of other environmental variables available in the dataset. Residuals in pico- and combined pico-nanophytoplankton fractions of total chlorophyll-a were significantly correlated with water temperature (positively) and picoeukaryote cell number (negatively). We conducted a running fit of the model with increasing temperature and found a negative relationship between temperature and parameters Cpm and Cp,nm and a positive relationship between temperature and parameters D_p and D_p,n. By harnessing the relative red fluorescence of the flow cytometric data, we show that picoeukaryotes, which are higher in cell number in winter (cold) than summer (warm), contain higher chlorophyll per cell than other picophytoplankton and are slightly larger in size, possibly explaining the temperature shift in model parameters, though further evidence is needed to substantiate this finding. Our results emphasize the importance of knowing the water temperature and taxonomic composition of phytoplankton within each size class when understanding their relative contribution to total chlorophyll. Furthermore, our results have implications for the development of algorithms for inferring size-fractionated chlorophyll from satellite data, and for how the partitioning of total chlorophyll into the three size classes may change in a future ocean.

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.

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.

Discrimination of phytoplankton functional groups using an ocean reflectance inversion model

Ocean reflectance inversion models (ORMs) provide a mechanism for inverting the color of the water observed by a satellite into marine inherent optical properties (IOPs), which can then be used to study phytoplankton community structure. Most ORMs effectively separate the total signal of the collective phytoplankton community from other water column constituents; however, few have been shown to effectively identify individual contributions by multiple phytoplankton groups over a large range of environmental conditions. We evaluated the ability of an ORM to discriminate between Noctiluca miliaris and diatoms under conditions typical of the northern Arabian Sea. We: (1) synthesized profiles of IOPs that represent bio-optical conditions for the Arabian Sea; (2) generated remote-sensing reflectances from these profiles using Hydrolight; and (3) applied the ORM to the synthesized reflectances to estimate the relative concentrations of diatoms and N. miliaris. By comparing the estimates from the inversion model with those from synthesized vertical profiles, we identified those conditions under which the ORM performs both well and poorly. Even under perfectly controlled conditions, the absolute accuracy of ORM retrievals degraded when further deconstructing the derived total phytoplankton signal into subcomponents. Although the absolute magnitudes maintained biases, the ORM successfully detected whether or not Noctiluca miliaris appeared in the simulated water column. This quantitatively calls for caution when interpreting the absolute magnitudes of the retrievals, but qualitatively suggests that the ORM provides a robust mechanism for identifying the presence or absence of species.

Generalized ocean color inversion model for retrieving marine inherent optical properties

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

Particle backscattering as a function of chlorophyll and phytoplankton size structure in the open-ocean

Using an extensive database of in situ observations we present a model that estimates the particle backscattering coefficient as a function of the total chlorophyll concentration in the open-ocean (Case-1 waters). The parameters of the model include a constant background component and the chlorophyll-specific backscattering coefficients associated with small (<20 μm) and large (>20 μm) phytoplankton. The new model performed with similar accuracy when compared with a traditional power-law function, with the additional benefit of providing information on the role of phytoplankton size. The observed spectral-dependency (γ) of model parameters was consistent with past observations, such that γ associated with the small phytoplankton population was higher than that of large phytoplankton. Furthermore, γ associated with the constant background component suggests this component is likely attributed to submicron particles. We envisage that the model would be useful for improving Case-1 ocean-colour models, assimilating light into multi-phytoplankton ecosystem models and improving estimates of phytoplankton size structure from remote sensing.