Phytoplankton Functional Type Research Articles

A singular value decomposition approach for detecting and delineating harmful algal blooms in the Red Sea

Harmful algal blooms (HABs) have adverse effects on marine ecosystems. An effective approach for detecting, monitoring, and eventually predicting the occurrences of such events is required. By combining a singular value decomposition (SVD) approach and satellite remote sensing observations, we propose a remote sensing algorithm to detect and delineate species-specific HABs. We implemented and tested the proposed SVD algorithm to detect HABs associated with the mixed assemblages of different phytoplankton functional type (PFT) groupings in the Red Sea. The results were validated with concurrent in-situ data from surface samples, demonstrating that the SVD-model performs remarkably well at detecting and distinguishing HAB species in the Red Sea basin. The proposed SVD-model offers a cost-effective tool for implementing an automated remote-sensing monitoring system for detecting HAB species in the basin. Such a monitoring system could be used for predicting HAB outbreaks based on near real-time measurements, essential to support aquaculture industries, desalination plants, tourism, and public health.

Physiological flexibility of phytoplankton impacts modelled chlorophyll and primary production across the North Pacific Ocean

Abstract. Phytoplankton growth, and hence biomass, responds to variations in light and nutrient availability in the near-surface ocean. A wide variety of models have been developed to capture variable chlorophyll : carbon ratios due to photoacclimation, i.e. the dynamic physiological response of phytoplankton to varying light and nutrient availability. Although photoacclimation models have been developed and tested mostly against laboratory results, their application and testing against the observed flexible response of phytoplankton communities remains limited. Hence, the biogeochemical implications of photoacclimation in combination with ocean circulation have yet to be fully explored. We compare modelled chlorophyll and primary production from an inflexible phytoplankton functional type model (InFlexPFT), which assumes fixed carbon (C) : nitrogen (N) : chlorophyll (Chl) ratios, to that from a recently developed flexible phytoplankton functional type model (FlexPFT), which incorporates photoacclimation and variable C : N : Chl ratios. We couple each plankton model with a 3-D eddy-resolving ocean circulation model of the North Pacific and evaluate their respective performance versus observations (e.g. satellite imagery and vertical profiles of in situ observations) of Chl and primary production. These two models yield different horizontal and vertical distributions of Chl and primary production. The FlexPFT reproduces observed subsurface Chl maxima in the subtropical gyre, although it overestimates Chl concentrations. In the subtropical gyre (where light is sufficient), even at low nutrient concentrations, the FlexPFT yields higher chlorophyll concentrations and faster growth rates, which result in higher primary production in the subsurface, compared to the InFlexPFT. Compared to the FlexPFT, the InFlexPFT yields slower growth rates and lower Chl and primary production. In the subpolar gyre, the FlexPFT also predicts faster growth rates near the surface, where light and nutrient conditions are most favourable. Compared to the InFlexPFT, the key differences that allow the FlexPFT to better reproduce the observed patterns are its assumption of variable, rather than fixed, C : N : Chl ratios and interdependent, rather than strictly multiplicative, effects of light limitation (photoacclimation) and nutrient limitation (uptake). Our results suggest that incorporating these processes has the potential to improve chlorophyll and primary production patterns in the near-surface ocean in future biogeochemical models.

Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea

Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly-averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements.

Improved Representation of Underwater Light Field and Its Impact on Ecosystem Dynamics: A Study in the North Sea

AbstractUnderstanding ecosystem state on the North‐West European (NWE) Shelf is of major importance for both economy and climate research. The purpose of this work is to advance our modeling of in‐water optics on the NWE Shelf, with important implications for how we model primary productivity, as well as for assimilation of water‐leaving radiances. We implement a stand‐alone bio‐optical module into the existing coupled physical‐biogeochemical model configuration. The advantage of the bio‐optical module, when compared to the preexisting light scheme is that it resolves the underwater light spectrally and distinguishes between direct and diffuse downwelling streams. The changed underwater light compares better with both satellite and in situ observations. The module lowered the underwater photosynthetically active radiation, decreasing the simulated primary productivity, but overall, the improved underwater light had relatively limited impact on the phytoplankton seasonal dynamics. We showed that the model skill in representing phytoplankton seasonal cycle (e.g., phytoplankton bloom) can be substantially improved either by assimilation of satellite phytoplankton functional type (PFT) chlorophyll, or by assimilating a novel PFT absorption product. Assimilation of the two PFT products yields similar results, with an important difference in the PFT community structure. Both assimilative runs lead to lower plankton biomass and increase the nutrient concentrations. We discuss some future directions on how to improve our model skill in biogeochemistry without using assimilation, for example, by improving nutrient forcing, retuning the model parameters, and using the bio‐optical module to provide a two‐way physical‐biogeochemical coupling, improving the consistency between model physical and biogeochemical components.

Senescence as the main driver of iodide release from a diverse range of marine phytoplankton

Abstract. The reaction between ozone and iodide at the sea surface is now known to be an important part of atmospheric ozone cycling, causing ozone deposition and the release of ozone-depleting reactive iodine to the atmosphere. The importance of this reaction is reflected by its inclusion in chemical transport models (CTMs). Such models depend on accurate sea surface iodide fields, but measurements are spatially and temporally limited. Hence, the ability to predict current and future sea surface iodide fields, i.e. sea surface iodide concentration on a narrow global grid, requires the development of process-based models. These models require a thorough understanding of the key processes that control sea surface iodide. The aim of this study was to explore if there are common features of iodate-to-iodide reduction amongst diverse marine phytoplankton in order to develop models that focus on sea surface iodine and iodine release to the troposphere. In order to achieve this, rates and patterns of changes in inorganic iodine speciation were determined in 10 phytoplankton cultures grown at ambient iodate concentrations. Where possible these data were analysed alongside results from previous studies. Iodate loss and some iodide production were observed in all cultures studied, confirming that this is a widespread feature amongst marine phytoplankton. We found no significant difference in log-phase, cell-normalised iodide production rates between key phytoplankton groups (diatoms, prymnesiophytes including coccolithophores and phaeocystales), suggesting that a phytoplankton functional type (PFT) approach would not be appropriate for building an ocean iodine cycling model. Iodate loss was greater than iodide formation in the majority of the cultures studied, indicating the presence of an as-yet-unidentified “missing iodine” fraction. Iodide yield at the end of the experiment was significantly greater in cultures that had reached a later senescence stage. This suggests that models should incorporate a lag between peak phytoplankton biomass and maximum iodide production and that cell mortality terms in biogeochemical models could be used to parameterise iodide production.

Responses of phytoplankton functional groups to environmental factors in the Pearl River, South China.

The variations of phytoplankton functional groups and their correlation with environmental factors, as well as the applicability of phytoplankton functional groups to serve as biological water quality indicator in the Pearl River, South China, were studied in the present study. A total of 96 samples were collected and divided into 21 functional groups from September 2016 to July 2017. The phytoplankton functional groups P and G were dominant during the investigation, and their biomass contributing was ranged 0.06 to 89.07%, the average 30.73%, and ranged 1.47 to 62.40%, the average 9.33% of the total biomass, respectively. The results showed environmental estrogens-BPA (bisphenol A), E2 (17β-estradiol), E1 (estrone), 4-t-OP (4-tert-octylphenol), 4-NP (nonylphenol), TCS (triclosan), and TCC (triclocarban)-in the Pearl River were significantly different, and with average values of 269.30ngL-1, 2.76ngL-1, 4.24ngL-1, 53.68ngL-1, 952.72ngL-1, 16.79ngL-1, and 8.61ngL-1, respectively. This was likely responsible for the differences in the phytoplankton functional groups. We observed positive correlations between P and A, and G and J. Functional groups P biomass decreased significantly with functional groups LM increased, and functional groups M and J with X2. We found positive correlations between functional group G and concentrations of E1 and TCC; functional group A and total nitrogen (TN), 4-NP, 4-t-OP, and E2; functional groups LM and L1 and total suspended particles, BPA, and TCS; and functional group G and negative total phosphorus (TP), pH, and TCS. The study showed that TN, TP, and the environmental estrogens in the aquatic ecosystems were correlation with phytoplankton functional groups type, and affected the ecological balance in aquatic environments.

Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R2 = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.

A Satellite Assessment of Environmental Controls of Phytoplankton Community Size Structure

AbstractPhytoplankton play a key role as the base of the marine food web and a crucial component in the Earth's carbon cycle. There have been a few regional studies that have utilized satellite‐estimated phytoplankton functional type products in conjunction with other environmental metrics. Here we expand to a global perspective and ask, what are the physical drivers of phytoplankton composition variability? Using a variety of satellite‐observed ocean color products and physical properties spanning 1997–2015, we characterize spatial and temporal variability in phytoplankton community size structure in relation to satellite‐based physical drivers. We consider the relationships globally and by major thermal regimes (cold and warm), dominant size distribution, and chlorophyll concentration variability. Globally, euphotic depth is the most important parameter driving phytoplankton size variability and also over the majority of the high‐latitude ocean and the central gyres. In all other regions, size variability is driven by a balance of light and mode of nutrient delivery. We investigated the relationship between size composition and chlorophyll concentration and the physical drivers through correlation analysis. Changes in size composition over time are regionally varying and explained by temporal shifts in the varying physical conditions. These changes in phytoplankton size composition and the varying underlying physical drivers will ultimately impact carbon export and food web processes in our changing ocean.

Coccolithophore Growth and Calcification in an Acidified Ocean: Insights From Community Earth System Model Simulations

AbstractAnthropogenic CO2 emissions are inundating the upper ocean, acidifying the water, and altering the habitat for marine phytoplankton. These changes are thought to be particularly influential for calcifying phytoplankton, namely, coccolithophores. Coccolithophores are widespread and account for a substantial portion of open ocean calcification; changes in their abundance, distribution, or level of calcification could have far‐reaching ecological and biogeochemical impacts. Here, we isolate the effects of increasing CO2 on coccolithophores using an explicit coccolithophore phytoplankton functional type parameterization in the Community Earth System Model. Coccolithophore growth and calcification are sensitive to changing aqueous CO2. While holding circulation constant, we demonstrate that increasing CO2 concentrations cause coccolithophores in most areas to decrease calcium carbonate production relative to growth. However, several oceanic regions show large increases in calcification, such the North Atlantic, Western Pacific, and parts of the Southern Ocean, due to an alleviation of carbon limitation for coccolithophore growth. Global annual calcification is 6% higher under present‐day CO2 levels relative to preindustrial CO2 (1.5 compared to 1.4 Pg C/year). However, under 900 μatm CO2, global annual calcification is 11% lower than under preindustrial CO2 levels (1.2 Pg C/year). Large portions of the ocean show greatly decreased coccolithophore calcification relative to growth, resulting in significant regional carbon export and air‐sea CO2 exchange feedbacks. Our study implies that coccolithophores become more abundant but less calcified as CO2 increases with a tipping point in global calcification (changing from increasing to decreasing calcification relative to preindustrial) at approximately ∼600 μatm CO2.

Increasing Biomass in the Warm Oceans: Unexpected New Insights From SeaWiFS

AbstractMarine phytoplankton biomass and community structure are expected to change under global warming, with potentially significant impacts on ocean carbon, nutrient cycling, and marine food webs. Previous studies have indicated decreases of primary production and chlorophyll a concentrations and oligotrophic gyre expansions from satellite ocean‐color measurements, purportedly due to global warming. We review this topic via a reanalysis of a novel backscattering‐based phytoplankton functional type and phytoplankton biomass time series over the 1997–2010 period. Unlike previous work, we find that globally the biomass and the percent of large (small) phytoplankton increase (decrease). The oligotrophic gyres contract or expand depending on the chlorophyll a threshold definition employed. In the subtropical gyres, chlorophyll a trends are likely due to physiological changes, while the increasing biomass trends are due to winds and relevant mixing length scale increases.

The Fundamental Contribution of Phytoplankton Spectral Scattering to Ocean Colour: Implications for Satellite Detection of Phytoplankton Community Structure

There is increasing interdisciplinary interest in phytoplankton community dynamics as the growing environmental problems of water quality (particularly eutrophication) and climate change demand attention. This has led to a pressing need for improved biophysical and causal understanding of Phytoplankton Functional Type (PFT) optical signals, in order for satellite radiometry to be used to detect ecologically relevant phytoplankton assemblage changes. Biophysically and biogeochemically consistent phytoplankton Inherent Optical Property (IOP) models play an important role in achieving this understanding, as the optical effects of phytoplankton assemblage changes can be examined systematically in relation to the bulk optical water-leaving signal. The Equivalent Algal Populations (EAP) model is used here to investigate the source and magnitude of size- and pigment- driven PFT signals in the water-leaving reflectance, as well as the potential to detect these using satellite radiometry. This model places emphasis on the determination of biophysically consistent phytoplankton IOPs, with both absorption and scattering determined by mathematically cogent relationships to the particle complex refractive indices. All IOPs are integrated over an entire size distribution. A distinctive attribute is the model’s comprehensive handling of the spectral and angular character of phytoplankton scattering. Selected case studies and sensitivity analyses reveal that phytoplankton spectral scattering is most useful and the least ambiguous driver of the PFT signal. Key findings are that there is the most sensitivity in phytoplankton backscatter ( b b ϕ ) in the 1–6 μ m size range; the backscattering-driven signal in the 520 to 570 nm region is the critical PFT identifier at marginal biomass, and that, while PFT information does appear at blue wavelengths, absorption-driven signals are compromised by ambiguity due to biomass and non-algal absorption. Low signal in the red, due primarily to absorption by water, inhibits PFT detection here. The study highlights the need to quantitatively understand the constraints imposed by phytoplankton biomass and the IOP budget on the assemblage-related signal. A proportional phytoplankton contribution of approximately 40% to the total b b appears to a reasonable minimum threshold in terms of yielding a detectable optical change in R r s . We hope these findings will provide considerable insight into the next generation of PFT algorithms.

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

Phytoplankton functional diversity plays a key role in structuring the ocean carbon cycle and can be estimated using measurements of phytoplankton functional type (PFT) groupings. Concentrations of 18 phytoplankton pigments were calculated using a linear matrix inversion algorithm, with an average r2 value of 0.70 for all pigments with p-values below the statistical threshold of 0.05. The inversion algorithm was then used with a chlorophyll-based absorption spectra model and Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) chlorophyll observations to calculate phytoplankton pigment concentrations in an area of the Atlantic Ocean off the United States east coast. Pigment distributions were analyzed to assess the distribution of PFTs. Five unique PFTs were found and delineated into three distinct offshore, transition, and open ocean groups. Group 1 (Diatoms) had highest abundance along the coast. Group 2 (prymnesiophytes, prokaryotes, and green algae) was a year-round stable offshore community that extended at reduced levels into the coast. Group 3 (dinoflagellates) dominated offshore between the Groups 1 and 2. Phytoplankton communities were delineated into coastal and offshore populations, with Group 2 having a dampened seasonal cycle, relative to the coastal populations. Shannon Diversity Indices (H) for the PFTs showed both spatial and temporal variability and had a clear non-linear relationship with chlorophyll. Diversity levels varied seasonally with changes in chlorophyll a levels. Peak PFT H was observed on the shelf where frontal features dominate, with diversity levels declining nearshore and offshore. This region marks an ecotone for phytoplankton in the study domain, and is associated with the coastal-side boundary of dinoflagellate dominance. Highest levels of diversity were observed in the tidally well-mixed regions of the Gulf of Maine and along a band that ran along the shelf region of the study area that was narrowest in the summer periods and broadened during the winter. These peak diversity zones were associated with moderate levels (~0.8 mg m-3) of chlorophyll a. While the sign in the linear trends in chlorophyll between 2002-2016 varied depending on the region, the trends in the PFT H values were nearly all negative due to the non-linear relationship between chlorophyll levels and H.

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.

Understanding the contribution of phytoplankton phase functions to uncertainties in the water colour signal.

The accurate description of a water body's volume scattering function (VSF), and hence its phase functions, is critical to the determination of the constituent inherent optical properties (IOPs), the associated spectral water-leaving reflectance, and consequently the retrieval of phytoplankton functional type (PFT) information. The equivalent algal populations (EAP) model has previously been evaluated for phytoplankton-dominated waters, and offers the ability to provide phytoplankton population-specific phase functions, unveiling a new opportunity to further understanding of the causality of the PFT signal. This study presents and evaluates the wavelength dependent, spectrally variable EAP particle phase functions and the subsequent effects on water-leaving reflectance. Comparisons are made with frequently used phase function approximations e.g. the Fournier Forand formulation, as well as with phase functions inferred from measured VSFs in coastal waters. Relative differences in shape and magnitude are quantified. Reflectance modelled with the EAP phase functions is then compared against measured reflectance data from phytoplankton-dominated waters. Further examples of modelled phytoplankton-dominated waters are discussed with reference to choice of phase function for two PFTs (eukaryote and prokaryote) across a range of biomass. Finally a demonstration of the sensitivity of reflectance due to the choice of phase function is presented. The EAP model phase functions account for both spectral and angular variability in phytoplankton backscattering i.e. they display variability which is both spectral and shape-related. It is concluded that phase functions modelled in this way are necessary for investigating the effects of assemblage variability on the ocean colour signal, and should be considered for model closure even in relatively low scattering conditions where phytoplankton dominate the IOPs.

Inter-comparison of phytoplankton functional type phenology metrics derived from ocean color algorithms and Earth System Models

Ocean color remote sensing of chlorophyll concentration has revolutionized our understanding of the biology of the oceans. However, a comprehensive understanding of the structure and function of oceanic ecosystems requires the characterization of the spatio-temporal variability of various phytoplankton functional types (PFTs), which have differing biogeochemical roles. Thus, recent bio-optical algorithm developments have focused on retrieval of various PFTs. It is important to validate and inter-compare the existing PFT algorithms; however direct comparison of retrieved variables is non-trivial because in those algorithms PFTs are defined differently. Thus, it is more plausible and potentially more informative to focus on emergent properties of PFTs, such as phenology. Furthermore, ocean color satellite PFT data sets can play a pivotal role in informing and/or validating the biogeochemical routines of Earth System Models. Here, the phenological characteristics of 10 PFT satellite algorithms and 7 latest-generation climate models from the Coupled Model Inter-comparison Project (CMIP5) are inter-compared as part of the International Satellite PFT Algorithm Inter-comparison Project. The comparison is based on monthly satellite data (mostly SeaWiFS) for the 2003–2007 period. The phenological analysis is based on the fraction of microplankton or a similar variable for the satellite algorithms and on the carbon biomass due to diatoms for the climate models. The seasonal cycle is estimated on a per-pixel basis as a sum of sinusoidal harmonics, derived from the Discrete Fourier Transform of the variable time series. Peak analysis is then applied to the estimated seasonal signal and the following phenological parameters are quantified for each satellite algorithm and climate model: seasonal amplitude, percent seasonal variance, month of maximum, and bloom duration. Secondary/double blooms occur in many areas and are also quantified. The algorithms and the models are quantitatively compared based on these emergent phenological parameters. Results indicate that while algorithms agree to a first order on a global scale, large differences among them exist; differences are analyzed in detail for two Longhurst regions in the North Atlantic: North Atlantic Drift Region (NADR) and North Atlantic Subtropical Gyre West (NASW). Seasonal cycles explain the most variance in zonal bands in the seasonally-stratified subtropics at about 30° latitude in the satellite PFT data. The CMIP5 models do not reproduce this pattern, exhibiting higher seasonality in mid and high-latitudes and generally much more spatially homogeneous patterns in phenological indices compared to satellite data. Satellite data indicate a complex structure of double blooms in the Equatorial region and mid-latitudes, and single blooms on the poleward edges of the subtropical gyres. In contrast, the CMIP5 models show single annual blooms over most of the ocean except for the Equatorial band and Arabian Sea.

Can simple models predict large-scale surface ocean isoprene concentrations?

Abstract. We use isoprene and related field measurements from three different ocean data sets together with remotely sensed satellite data to model global marine isoprene emissions. We show that using monthly mean satellite-derived chl a concentrations to parameterize isoprene with a constant chl a normalized isoprene production rate underpredicts the measured oceanic isoprene concentration by a mean factor of 19 ± 12. Improving the model by using phytoplankton functional type dependent production values and by decreasing the bacterial degradation rate of isoprene in the water column results in only a slight underestimation (factor 1.7 ± 1.2). We calculate global isoprene emissions of 0.21 Tg C for 2014 using this improved model, which is twice the value calculated using the original model. Nonetheless, the sea-to-air fluxes have to be at least 1 order of magnitude higher to account for measured atmospheric isoprene mixing ratios. These findings suggest that there is at least one missing oceanic source of isoprene and, possibly, other unknown factors in the ocean or atmosphere influencing the atmospheric values. The discrepancy between calculated fluxes and atmospheric observations must be reconciled in order to fully understand the importance of marine-derived isoprene as a precursor to remote marine boundary layer particle formation.

Remote sensing of phytoplankton functional types in the coastal ocean from the HyspIRI Preparatory Flight Campaign

The 2013–2015 Hyperspectral Infrared Imager (HyspIRI) Preparatory Flight Campaign, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER), seeks to demonstrate appropriate sensor signal, spatial and spectral resolution, and orbital pass geometry for a global mission to reveal ecological and climatic gradients expressed in the selected California, USA study area. One of the awarded projects focused on the flight transects covering the coastal ocean to demonstrate that the AVIRIS data can be used to infer phytoplankton functional types at the land–sea interface. Specifically, this project directly assesses whether HyspIRI can provide adequate signal in the complex aquatic environment of the coastal zone to address questions of algal bloom dynamics, water quality, transient responses to human disturbance, river runoff, and red tides. Phytoplankton functional type (PFT), or biodiversity, can be determined from ocean color using the Phytoplankton Detection with Optics (PHYDOTax) algorithm and this information can be used to detect and monitor for harmful algal blooms. PHYDOTax is sensitive to spectral shape and accurate retrievals of ocean color across the visible spectral range is needed. The specific goal of this paper is to address the challenges of sensor capabilities and atmospheric correction in coastal environments by assessing two atmospheric correction methods using AVIRIS data for the retrieval of ocean color for use in derived products of chlorophyll-a and phytoplankton functional type. The atmospheric correction algorithms Atmospheric Removal (ATREM) and Tafkaa were applied to AVIRIS imagery of Monterey Bay, CA collected on 10 April 2013 and 31 October 2013. Data products from the imagery were compared with shipboard measurements including chlorophyll-a from whole-water samples and phytoplankton community structure estimated from diagnostic pigment markers using CHEMical TAXonomy (CHEMTAX). Using ATREM and Tafkaa and a selected set of input parameters for the scenes, we were unable to produce accurate retrievals of ocean color for the determination of chlorophyll-a and phytoplankton diversity. A modified ATREM correction produced science-quality data in which chlorophyll-a was accurately estimated using the Ocean Color 3 (OC3) chlorophyll-a algorithm, but biodiversity using PHYDOTax was not accurately estimated. Improvements in sensor calibration, sensitivity, and atmospheric correction of the HyspIRI imagery data set is needed in order to adequately estimate biogeochemically meaningful data products for the ocean such as chlorophyll-a, inherent optical properties, or PFTs. The HyspIRI Science Team is seeking improvements so the HyspIRI Airborne Campaign data set can be used for algorithm development to understand biodiversity and ecosystem function of coastal habitats that are facing increasing threats of human impact and climate change.

Biophysical modelling of phytoplankton communities from first principles using two-layered spheres: Equivalent Algal Populations (EAP) model.

There is a pressing need for improved bio-optical models of high biomass waters as eutrophication of coastal and inland waters becomes an increasing problem. Seasonal boom conditions in the Southern Benguela and persistent harmful algal production in various inland waters in Southern Africa present valuable opportunities for the development of such modelling capabilities. The phytoplankton-dominated signal of these waters additionally addresses an increased interest in Phytoplankton Functional Type (PFT) analysis. To these ends, an initial validation of a new model of Equivalent Algal Populations (EAP) is presented here. This paper makes a first order comparison of two prominent phytoplankton Inherent Optical Property (IOP) models with the EAP model, which places emphasis on explicit bio-physical modelling of the phytoplankton population as a holistic determinant of inherent optical properties. This emphasis is shown to have an impact on the ability to retrieve the detailed phytoplankton spectral scattering information necessary for PFT applications and to successfully simulate reflectance across wide ranges of physical environments, biomass, and assemblage characteristics.

Sensitivity in reflectance attributed to phytoplankton cell size: forward and inverse modelling approaches.

Synoptic scale knowledge of the size structure of phytoplankton communities can offer insight in to primary ecosystem diversity and biogeochemical variability from operational to the decadal scales. Accordingly, obtaining estimates of size and other phytoplankton functional type descriptors within known confidence limits from remotely sensed data has become a major objective to extend the use of ocean colour data beyond chlorophyll a retrievals. Here, a new forward and inverse modelling structure is proposed to determine information about the cell size of phytoplankton communities using Standard size distributions of two layered spheres to derive a full suite of algal inherent optical properties for a coupled radiative transfer model. This new capability allows explicit quantification of the remote sensing reflectance signal attributable to changes in phytoplankton cell size. Inversion of this model reveals regions within the parameter space where ambiguity may limit potential of inversion algorithms. Validation of the algorithm within the Benguela upwelling system using independent data shows promise for ecosystem applications and further investigation of the interaction between phytoplankton functional types and optical signals. The results here suggest that the utility of assemblage related signals in spectral reflectance is highly sensitive to algal biomass, the presence of other absorbing and scattering constituents and the resultant constituent-specific inherent optical property budget. As such, optimal methods for determining phytoplankton size from (in situ or satellite) ocean colour data will likely rely on appropriately spectrally dense and optimised sensors, well characterised measurement errors including those from atmospheric correction, and an ability to appropriately limit ambiguity within the context of regional inherent optical properties.

Comparing satellite‐based phytoplankton classification methods

Satellite Phytoplankton Functional Type Algorithm Intercomparison Workshop; Sapporo, Japan, 22–23 November 2011 Satellite observations of ocean color have become synonymous with derivations of chlorophyll a concentration as a proxy for phytoplankton biomass. In addition, a number of satellite algorithms for estimating the phytoplankton community structure have been developed that provide size‐structure estimates of phytoplankton and detect taxonomic groups (termed phytoplankton functional types, or PFTs). These new algorithms provide an increased level of observational detail for ecosystem and biogeochemical studies of the role of phytoplankton in marine systems.