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

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... observations from the North Atlantic Ocean, inferred from taxonomic composition (Benedetti et al. 2019) or remote sensing data (Kostadinov et al. 2010(Kostadinov et al. , 2017 are consistent with the model's predictions of seasonality in community size structure in the temperate ocean; however, direct size distribution measurements are still lacking. ...
... Analysis of pigments other than Chl a is also commonly used to retrieve major size classes based on empirical relationships among pigment composition, phytoplankton taxa, and their associated size (Mackey et al. 1996;Vidussi et al. 2001;Uitz et al. 2006). This approach has been widely used to obtain a global view of PSC through the development of remote sensing algorithms (Kostadinov et al. 2017;Mouw et al. 2017), but requires further validation as some key taxa exhibit a wide range of cell size (e.g., diatoms and dinoflagellates have representatives in both nano-and micro-size classes; (Uitz et al. 2006;Kostadinov et al. 2010;Chase et al. 2020). Direct measurements of cell size have been done traditionally by microscopy for cells > 5 μm (Huete-Ortega et al. 2011;Marañ on et al. 2012), but they are time consuming and therefore limited in temporal and spatial coverage. ...
... The seasonal variation described here is based on samples collected in different years, but the pattern is consistent with the phenology of phytoplankton functional type (PFT) obtained from monthly climatology of remote sensing data. These models, however, do not estimate size per se but infer seasonality in size from changes in the fraction of microphytoplankton (or diatoms) to total phytoplankton, expressed in [Chl a], biovolume, or relative units (Kostadinov et al. 2017). An inter-comparison of the different size algorithms, however, revealed large qualitative and quantitative differences in relative contributions of microphytoplankton to total phytoplankton biomass. ...
Article
Full-text available
Phytoplankton play a major role on Earth, impacting the global distribution and cycles of carbon, oxygen, nitrogen, sulfur, and other elements, and structuring marine food webs. One fundamental trait of phytoplankton with direct biogeochemical implications is their size, as it governs metabolic and sinking rates as well as prey-predator interactions. Phytoplankton size spans approximately 3.5 orders of magnitude (when expressed as an equivalent spherical diameter), and thus measuring the full range in size distribution of phytoplankton is challenging and rarely attempted. Here, we constructed phytoplankton size spectra by merging state-of-the-art cyto-metry and imaging cytometry measurements that were collected in the western North Atlantic Ocean, along a latitudinal gradient (36 N to 55 N) and during different phases of the annual cycle of phytoplankton. The derived spectra show a seasonal pattern that parallels changes in phytoplankton biomass, and do not always follow a commonly assumed power-law model. Shifts in size spectra were more pronounced in the sub-Arctic and temperate subregions, compared to the subtropical region of the study area. We evaluated the relationships between different size groups and environmental parameters to derive ecologically meaningful size groups. Finally, to simulate Ocean Color remote-sensing algorithms of phytoplankton size, we compared temporal variations in descriptors of the size spectra (median particle size, phytoplankton size distribution exponent) with optical size proxies derived from light absorption and attenuation; good agreement was observed in the northern sections of the study area where temporal changes in community size structure were more pronounced.
... Finally, we stress that there are large uncertainties in the in situ PG and PSC data used for product validation (Brewin et al., 2014;Kramer and Siegel, 2019;Chase et al., 2020). Over the past decade, there have been some efforts to intercompare these different satellite approaches Hirata et al., 2012;Kostadinov et al., 2017;Stock and Subramaniam, 2020), and to identify the best algorithms for a particular application. However, undoubtedly, more work is needed in this direction (IOCCG, 2014) as well a better definition of PG and PSC products with comparable metrics that are also compliant with user's needs and with in situ observations (Bracher et al., 2017a). ...
... Considering the variety of algorithms available, it becomes important to intercompare them, to assess their consistency and accuracy, and limitations to guide users choices on what algorithm is best suited for a specific application. However, considering that the various PG or PSC algorithms are based on different principles, retrieve different variables, with differing units and scales (e.g., Chl-a due to diatoms vs. microphytoplankton C, IOCCG, 2014; Mouw et al., 2017;Kostadinov et al., 2017), direct comparisons are often nontrivial. Generally, comparison of phenological parameters can be more meaningful than directly comparing disparate remote-sensing variables. ...
... By comparing phenological metrics instead, these issues can be avoided to a certain extent. Kostadinov et al. (2017) compared phenology metrics among 10 different PSC and PG algorithms using a 5-year (2003-2007) monthly satellite dataset based mainly from SeaWiFS, and SCIAMACHY for PhytoDOAS (Bracher et al., 2009;Sadeghi et al., 2012a), and seven coupled model intercomparison project earth system models (CMIP5), as part of the International Satellite PFT Algorithm Inter-Comparison Project . The phenological inter-comparisons were based on the fraction of microphytoplankton, or a similar variable for the satellite PG algorithms, and on diatom C for the CMIP5 models. ...
Chapter
Differences in morphology, size, and pigmentation among various phytoplankton taxonomic groups impact their light absorption and scattering properties (e.g., Morel and Bricaud, 1981; Stramski and Kiefer, 1991; IOCCG, 2014), which modifies the color of the ocean. Optical satellite remote sensing enables the detection of backscattered sunlight emanating from the water surface (so-called ocean color). These observations can be exploited to obtain information not only on the overall biomass of phytoplankton, but also on the absence, presence, and dominance, of different phytoplankton groups. The remote-sensing products provide synoptic coverage of surface waters at global scale, and with a spatial coverage impossible from in-situ sampling. In this chapter, we define phytoplankton groups (PG) based on taxonomic criteria and also categorize phytoplankton composition using three phytoplankton size classes (PSC), following the classification of Sieburth et al. (1978). We discuss the characteristics required of satellite ocean-color sensors for PG and PSC detection, the algorithms' principles, we show applications of these satellite products, and discuss the societal impacts of these data sets.
... A few studies have assimilated PFT ocean-colour products into ecosystem models (Ciavatta et al. 2018(Ciavatta et al. , 2019Skákala et al. 2018, see Chapter 6). Studies suggest that model and satellite estimates of phytoplankton biogeography often do not compare well in terms of phytoplankton dominance patterns (Vogt et al. 2013;Ciavatta et al. 2019), and in the timing of blooms Kostadinov et al. 2017). These discrepancies may suggest that the numerical models are not sophisticated enough, or the satellite products are not well developed, or (more likely) that the groupings that each capture are different enough that the comparison is ill-posed. ...
... Satellite PFT algorithms have a variety of phytoplankton classes, units, and satellite product outputs, precluding direct comparison of algorithm performance. Instead, Kostadinov et al. (2017) compared phenological cycles (bloom timing) between PFT algorithms and several CMIP5 models to identify spatial patterns of agreement and disagreement (Figure 4.7). The timing, amplitude and duration of blooms of microplankton were compared. ...
... Reviews and intercomparisons of phytoplankton functional types exist (IOCCG 2014;Kostadinov et al. 2017;Mouw et al. 2017), but new methods are continually being developed. ...
... A few studies have assimilated PFT ocean-colour products into ecosystem models (Ciavatta et al. 2018(Ciavatta et al. , 2019Skákala et al. 2018, see Chapter 6). Studies suggest that model and satellite estimates of phytoplankton biogeography often do not compare well in terms of phytoplankton dominance patterns (Vogt et al. 2013;Ciavatta et al. 2019), and in the timing of blooms Kostadinov et al. 2017). These discrepancies may suggest that the numerical models are not sophisticated enough, or the satellite products are not well developed, or (more likely) that the groupings that each capture are different enough that the comparison is ill-posed. ...
... Satellite PFT algorithms have a variety of phytoplankton classes, units, and satellite product outputs, precluding direct comparison of algorithm performance. Instead, Kostadinov et al. (2017) compared phenological cycles (bloom timing) between PFT algorithms and several CMIP5 models to identify spatial patterns of agreement and disagreement (Figure 4.7). The timing, amplitude and duration of blooms of microplankton were compared. ...
... Reviews and intercomparisons of phytoplankton functional types exist (IOCCG 2014;Kostadinov et al. 2017;Mouw et al. 2017), but new methods are continually being developed. ...
Technical Report
Full-text available
The goal of this report is to improve the communication between numerical modellers and the ocean colour community. It provides non-expert accessible information about both ocean colour and biogeochemical and ecosystem modelling. The report discusses methods of model skill assessment using ocean colour products, introduces and highlights case studies of data assimilation involving ocean colour products, and provides examples where models and ocean colour are used synergistically to better understand processes and trends in the ocean’s ecosystem and biogeochemistry. Additionally, the report explores how models can help inform on ocean colour, with the goal of fostering further use of models in ocean colour studies, in helping elucidate uncertainties, and in algorithm development.
... A few studies have assimilated PFT ocean-colour products into ecosystem models (Ciavatta et al. 2018;Skákala et al. 2018, see Chapter 6). Studies suggest that model and satellite estimates of phytoplankton biogeography often do not compare well in terms of phytoplankton dominance patterns (Vogt et al. 2013;Ciavatta et al. 2019), and in the timing of blooms Kostadinov et al. 2017). These discrepancies may suggest that the numerical models are not sophisticated enough, or the satellite products are not well developed, or (more likely) that the groupings that each capture are different enough that the comparison is ill-posed. ...
... Satellite PFT algorithms have a variety of phytoplankton classes, units, and satellite product outputs, precluding direct comparison of algorithm performance. Instead, Kostadinov et al. (2017) compared phenological cycles (bloom timing) between PFT algorithms and several CMIP5 models to identify spatial patterns of agreement and disagreement (Figure 4.7). The timing, amplitude and duration of blooms of microplankton were compared. ...
... These would be more useful with greater information supplied by the ocean colour community. Reviews and intercomparisons of phytoplankton functional types exist (IOCCG 2014;Kostadinov et al. 2017;Mouw et al. 2017), but new methods are continually being developed. ...
Technical Report
Full-text available
This report is intended as part of the important dialogue between the ocean colour and the biogeochemical/ecosystem/climate modelling communities. Numerical modellers are frequent users of ocean colour products, but many modellers remain unsure of the best way to use these products, and are often unaware of the uncertainties associated with them. On the other hand, the ocean colour community often are unsure on how models work, their usefulness and their limitations. This report is not intended to be comprehensive. We focus particularly on large scale, three dimensional models, and often consider open ocean (rather than coastal) processes. We have also included mostly phytoplankton-centric research. This is a result of the working group’s main interests and we emphasis that there are many more types of models and relevant work, beyond what is discussed here. We have attempted to add numerous references for further exploration by an interested reader.
... High-resolution variables are down-sampled to a 1 grid using a averaging kernel. If >50% of the pixels being averaged are invalid data, the pixel is assigned a missing data value (as in Kostadinov et al., 2017). All monthly time resolution datasets are used. ...
... Daily datasets were converted to monthly resolution. The 9km resolution Chl-a and biomass products were re-gridded on a 1° grid using an averaging kernel (Kostadinov et al., 2017). Nearest neighbor linear extrapolation was applied to fill in the data gaps due to sparse clouds. ...
... Chl-a, PAR and Kd490 are expressed in mgm -3 , Einstein m -2 day -1 and m -1 , respectively. All monthly gridded data from the above table was down-sampled to a 1 resolution using two-dimensional convolution with an averaging kernel (as in Kostadinov et al., 2017). If >50% of the pixels being averaged were invalid data, the pixel in the down-sampled image was assigned a missing data value. ...
Article
Phytoplankton are the base of the marine food web, and, importantly, drive the biological carbon pump, the combination of photosynthesis, organic carbon sinking and subsurface decomposition of organic matter which effectively sequesters carbon away from the atmosphere. Our knowledge of phytoplankton activity is currently advancing fast through developments of multiple ocean-color remote sensing algorithms and via developments in ecological modules incorporated in climate models. While climate models are projecting relatively clear trends in ocean ecology over the next century, distinguishing between interannual variability and ocean biology trends from satellite observations is difficult. Short record length, satellite data continuity issues and strong interannual variability all impact quantified trends. Additionally, commonly observed chlorophyll-a is not strictly indicative of underlying phytoplankton biomass because of phytoplankton adaptation. This thesis investigates the trends, interannual variability and seasonality in new size-partitioned phytoplankton biomass products, with a focus on the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) mission period (1997-2010). In Chapter 2 we found phytoplankton biomass increases in the warm ocean regions over this period, opposing common expectations of decreases in warming oceans. Biomass increases are due to increased physical mixing of the watercolumn and are partially attributed to the large scale El Nino Southern Oscillation (ENSO) phenomenon. Recent studies have highlighted the emergence of different types of ENSO, with a shift towards more Central Pacific ENSO events. Chapter 3 uses statistical techniques (agglomerative hierarchical clustering (AHC), empirical orthogonal functional analysis (EOF)) on phytoplankton biomass to characterize ENSO “flavors” in the tropical Pacific. For the first time, we empirically derive biological indices for different ENSO types and show high correlations with existing climate indices. In Chapter 4 we examine in depth seasonal in phytoplankton ecology between the North Eastern Pacific subpolar region and contrast it with North Atlantic subpolar ecology. We discuss drivers of biological changes (iron, nutrients, light, mixing). We reveal large differences between biological variables across ocean-color algorithms, as well as across the latest generation Earth System model suite (Carbon Model Intercomparison Project, CMIP5). Chapter 5 summarizes our findings and future work suggestions. Future work should link surface phytoplankton ecology to ocean-atmosphere carbon fluxes and ocean carbon pump efficiency.
... Thus, we exclude ecologically based methods that require additional physical and spatio-temporal information (e.g., Raitsos et al., 2008;Palacz et al., 2013). We utilize all of the algorithms that Kostadinov et al. (2017) directly compare plus three additional algorithms (Hirata et al., 2008;Devred et al., 2011;Li et al., 2013). ...
... This presents an additional layer of challenge, precluding direct comparison of algorithm performance and explicit "how to" instructions as found in Behrenfeld and Falkowski (1997). Instead, other metrics, such as phenological cycles, are being explored as a way to intercompare PFT algorithms (Kostadinov et al., 2017). It is not our purpose here to inter-compare algorithm performance, rather we seek to provide users with a simplified "go to" reference to understand existing algorithm types, their associated strengths and limitations, input requirements and output products, to aid in selecting the satellite PFT model that may best fit their application. ...
... Prochlorococcus and Synechococcus, along with the broader prokaryotes class obtained by Hirata et al. (2011), are grouped as cyanobacteria ( Table 2). Algorithm abbreviations follow those established by the algorithm's author(s), are consistent with those in Kostadinov et al. (2017), and are noted in Figure 1 and Table 2. ...
Article
Full-text available
Phytoplankton are composed of diverse taxonomical groups, which are manifested as distinct morphology, size and pigment composition. These characteristics, modulated by their physiological state, impact their light absorption and scattering, allowing them to be detected with ocean color satellite radiometry. There is a growing volume of literature describing satellite algorithms to retrieve information on phytoplankton composition in the ocean. This synthesis provides a review of current methods and a simplified comparison of approaches. The aim is to provide an easily comprehensible resource for non-algorithm developers, who desire to use these products, thereby raising the level of awareness and use of these products and reducing the boundary of expert knowledge needed to make a pragmatic selection of output products with confidence. The satellite input and output products, their associated validation metrics, as well as assumptions, strengths and limitations of the various algorithm types are described, providing a framework for algorithm organization to assist users and inspire new aspects of algorithm development capable of exploiting the higher spectral, spatial and temporal resolutions from the next generation of ocean color satellites.
... Consistent with this, we find that slope and BV are negatively correlated (r 2 = 0.4, p < 0.01 Figures 5a and 5b). Spatial patterns in BV and slope roughly follow the distribution of satellite-derived chlorophyll and primary production estimates, suggesting that phytoplankton and photosynthesis exert a strong control on the total abundance of particles in any given region (Cram et al., 2018;Kostadinov et al., 2009Kostadinov et al., , 2017. Accordingly, we find a positive correlation between BV and surface chlorophyll (R = 0.49 for observations, and R = 0.68 for reconstructions, both with p < 0.01, Figures 5a and 5b) and a negative correlation for slope (R = −0.18 ...
... However, methodological shortcomings and disagreement between different approaches (such as satellite based retrievals) currently limit the applicability of these data sets-something that may be mitigated by future advances. It is also likely that information related to phytoplankton composition and size structure retrieved from satellite implicitly enters the RF regression via relationships with environmental predictors such as surface chlorophyll and temperature (Kostadinov et al., 2017;Mouw et al., 2017). ...
Article
Full-text available
The abundance and size distribution of marine particles control a range of biogeochemical and ecological processes in the ocean, including carbon sequestration. These quantities are the result of complex physical‐biological interactions that are difficult to observe, and their spatial and temporal patterns remain uncertain. Here, we present a novel analysis of particle size distributions (PSDs) from a global compilation of in situ Underwater Vision Profiler 5 (UVP5) optical measurements. Using a machine learning algorithm, we extrapolate sparse UVP5 observations to the global ocean from well‐sampled oceanographic variables. We reconstruct global maps of PSD parameters (biovolume [BV] and slope) for particles at the base of the euphotic zone. These reconstructions reveal consistent global patterns, with high chlorophyll regions generally characterized by high particle BV and flatter PSD slope, that is, a high relative abundance of large versus small particles. The resulting negative correlations between particle BV and slope further suggests synergistic effects on size‐dependent processes such as sinking particle fluxes. Our approach and estimates provide a baseline for an improved understanding of particle cycles in the ocean, and pave the way to global, three‐dimensional reconstructions of PSD and sinking particle fluxes from the growing body of UVP5 observations.
... Consequently, it provided a much-needed initial comparison, but complimentary additional analyses are required to obtain a reliable picture of different algorithms' performance in different situations. Fourth, a comparison of microplankton phenology predicted by various satellite algorithms and climate models found considerable differences between these model outputs (Kostadinov et al., 2017). For these reasons, additional investigations of the accuracy of satellite algorithms for predicting different aspects of phytoplankton community composition are needed. ...
... Lastly, statistical modeling extracts relationships between variables from existing data, but not all relationships between the variables of interest will remain the same in a changing climate. In contrast to mechanistic models based on stable relationships between variables (e.g., the laws of physics), statistical models are therefore not suitable to make predictions in time beyond the immediate future (Kostadinov et al., 2017;Sathyendranath et al., 2017). ...
Article
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Monitoring phytoplankton community composition from space is an important challenge in ocean remote sensing. Researchers have proposed several algorithms for this purpose. However, the in situ data used to train and validate such algorithms at the global scale are often clustered along ship cruise tracks and in some well-studied locations, whereas many large marine regions have no in situ data at all. Furthermore, oceanographic variables are typically spatially auto-correlated. In this situation, the common practice of validating algorithms with randomly chosen held-out observations can underestimate errors. Based on a global database of in situ HPLC data, we applied supervised learning methods to train and test empirical algorithms predicting the relative concentrations of eight diagnostic pigments that serve as biomarkers for different phytoplankton types. For each pigment, we trained three types of satellite algorithms distinguished by their input data: abundance-based (using only chlorophyll-a as input), spectral (using remote sensing reflectance), and ecological algorithms (combining reflectance and environmental variables). The algorithms were implemented as statistical models (smoothing splines, polynomials, random forests, and boosted regression trees). To address clustering of data and spatial auto-correlation, we tested the algorithms by means of spatial block cross-validation. This provided a less confident picture of the potential for global mapping of diagnostic pigments and hence the associated phytoplankton types using existing satellite data than suggested by some previous research and a fivefold cross-validation conducted for comparison. Of the eight diagnostic pigments, only two (fucoxanthin and zeaxanthin) could be predicted in marine regions that the algorithms were not trained in with considerably lower errors than a constant null model. Thus, global-scale algorithms based on existing, multi-spectral satellite data and commonly available environmental variables can estimate relative diagnostic pigment concentrations and hence distinguish phytoplankton types in some broad classes, but are likely inaccurate for some classes and in some marine regions. Overall, the ecological algorithms had the lowest prediction errors, suggesting that environmental variables contain information about the global spatial distribution of phytoplankton groups that is not captured in multi-spectral remote sensing reflectance and satellite-derived Chl a concentrations. Weighting training observations inversely to the degree of spatial clustering improved predictions. Finally, our results suggest that more discussion of the best approaches for training and validating empirical satellite algorithms is needed if the in situ data are unevenly distributed in the study region and spatially clustered.
... We refer the readers to the recent reviews by Mouw et al. (2017) and IOCCG (2014) for detailed descriptions and classifications of these various methods and products. For another view, a phenological compilation of these algorithms was compiled by Kostadinov et al. (2017), highlighting commonalities and differences in the global cyclic behavior of these products. ...
... In general, derived products from these models agree on the commonly accepted distribution patterns of phytoplankton size/taxa (see Kostadinov et al., 2017), but verifying their accuracy and associated uncertainties is challenging. This is due to the sparseness of phytoplankton composition data from the field, and to the difficulties in defining a universal test metric for these algorithms. ...
Article
Environmental conditions are important drivers in regulating the distribution pattern of phytoplankton composition in the world's oceans. We constructed models that predict pico-, nano-and micro-phytoplankton size classes and assessed the impact of separately including sea surface temperature (SST) and estimates of light level in the surface mixed-layer on model skill. The empirical models were trained using size classes estimated by chemotaxonomic analysis of in situ high performance liquid chromatography (HPLC) pigments and environmental data originating from the Atlantic Ocean. As the accuracy of transforming pigment data into quantitative size classes is crucial when constructing phytoplankton size composition (PSC) models, we also quantified the resulting differences of our and several existing PSC models when using class sizes derived from HPLC pigments by two common chemotaxonomic methods, CHEMTAX and Diagnostic Pigments (DP). Addition of the environmental variables to abundance-based models using our approach improved the skill of correctly predicting PSC, reducing the root mean square difference (RMSD) by 10 to 20% in the best cases. Addition of SST yielded the highest percentage decreases, on average, for all three size classes, with greatest improvement in micro-plankton and nanoplankton fractions. These models performed equal to or better than several existing abundance based models. The improvements in model predictions, however, could be obscured by the choice of pigment method used to generate the initial PSC data set. Insufficient data is available to assess whether CHEMTAX or DP is the more appropriate chemotaxonomic method to employ when estimating PSC. Further collection and analysis of additional water samples for phytoplankton taxa and size by microscopic methods-including traditional microscopic cell counts and automated methods-and HPLC pigment data are required to answer this question.
... The volume concentrations for different plankton size classes are calculated, using the PSD and its assumed relationship with backscattering spectral slope and additional assumptions, such as that relative proportions of biovolume are roughly constant across size classes (Kostadinov et al., 2016). One of the strengths of the assumption is that the particles are less sensitive to the physiological variability (Kostadinov et al., 2016(Kostadinov et al., , 2017Mouw et al., 2017). However, the particle size class includes all particle sizes. ...
... Daily data sets were converted to monthly resolution. The 9-km resolution Chl-a and biomass products were regridded on a 1°grid using twodimensional convolution with a 12×12 top-hat averaging kernel (Kostadinov et al., 2017). Nearest neighbor linear extrapolation was applied to fill in the data gaps due to sparse clouds. ...
Article
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Observed variations in the tropical phytoplankton community structure and biogeochemical processes have been linked to the El Niño Southern Oscillation, a driver of large‐scale natural climate variability on interannual timescales. Satellite bio‐optical algorithms have allowed us to derive complex biological parameters from the surface ocean via remote sensing, providing a scientific platform to investigate biological relationships with climate indices. Studies have focused in‐depth on contrasting types of the ENSO types with various physical parameters with only a few recent studies focusing on satellite‐observed chlorophyll‐a, with however none focusing on phytoplankton biomass itself. Here we review the types of ENSO and its effect on backscattering‐based biomass using different statistical techniques, over the 1997‐2007 period. We also contrast the responses of phytoplankton biomass with those of chlorophyll‐a and their physical drivers in various types of ENSO. Signatures of various ENSO types are observed in the physical and biological fields
... This presents an additional layer of challenge, precluding direct comparison of algorithm performance. Instead, metrics, such as phenological cycle, have been used as a way to intercompare PFT algorithms (Kostadinov et al., 2017). This intercomparison revealed that while PFT algorithms agree across broad scales, they do not all agree under all circumstances. ...
... This intercomparison revealed that while PFT algorithms agree across broad scales, they do not all agree under all circumstances. Here we sought to utilize a PFT product that performed near the mean of the phenological metrics (phenological shape, magnitude and month of maximum) that Kostadinov et al. (2017) assessed, as well as that with high validation metrics reported from the original publication (compiled by Mouw et al., 2017). Further, phytoplankton size is one of the best characterized traits structuring food webs due to many ecosystem and physiological processes that are mediated by size such as nutrient acquisition and utilization, light acquisition, sinking, and grazer interactions (Finkel, 2007;Finkel et al., 2009;Litchman et al., 2010;Litchman & Klausmeier, 2008;Wirtz, 2012). ...
Article
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Phytoplankton 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.
... Phytoplankton phenology has been the subject of intense research in the last decade, mostly stimulated by the availability of satellite-retrieved surface chlorophyll-a concentration (Chl-a) and the anticipated climate-induced changes in marine ecosystems (e.g., Platt and Sathyendranath, 2008;Platt et al., 2010;Racault et al., 2012Racault et al., , 2014aFriedland et al., 2018;Henson et al., 2018). As an integrative environmental science (Schwartz, 2003), phenological studies have evaluated phytoplankton periodic events as well as their interactions with environmental conditions and climatic forcing (e.g., Henson et al., 2006Henson et al., , 2018Demarcq et al., 2012;Racault et al., 2012;Sapiano et al., 2012;Cabré et al., 2016;Kostadinov et al., 2017). Phytoplankton phenology has usually been synthesized into a set of ecologically relevant indices: the timing, duration and magnitude of bloom events (Platt and Sathyendranath, 2008;Platt et al., 2009Platt et al., , 2010Racault et al., 2014a). ...
... In addition to the multiple potential applications associated with the delineation of ecosystem partitions (e.g., biogeochemical modelling, marine spatial planning, ecosystem-based management; see review by Krug et al., 2017a), this SWIP partition was specifically used as a framework for discriminating the environmental drivers of phytoplankton phenology over a complex marine domain. Most phytoplankton global and regional phenology studies have addressed indices related to the principal annual bloom event (e.g., spring bloom), including bloom magnitude, timing and duration (e.g., Henson et al., 2006Henson et al., , 2010Henson et al., , 2018Racault et al., 2012;Sapiano et al., 2012;Soppa et al., 2016;Kostadinov et al., 2017;see Friedland et al., 2016see Friedland et al., , 2018. The number of studies evaluating multiple bloom events per year, usually two (e.g., spring and autumn blooms; Winder and Cloern, 2010;Martinez et al., 2011;Sapiano et al., 2012;Chiswell et al., 2013;González Taboada and Anadón, 2014;Land et al., 2014;Racault et al., 2015Racault et al., , 2017Friedland et al., 2016) is, in fact, limited. ...
Article
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Phytoplankton patterns, tightly linked to the dynamics of the ocean surface layer and its atmospheric forcing, have major impacts on ecosystem functioning and are valuable indicators of its response to environmental variability and change. Phytoplankton phenology and its underlying drivers are spatially variable, and the study of its patterns, particularly over heterogeneous regions, benefits from a delineation of regions with specific phenological properties, or phenoregions. The area Southwest off the Iberian Peninsula (SWIP, NE Atlantic) integrates a highly complex set of coastal and ocean domains that collectively challenge the understanding of regional phytoplankton phenology and related forcing mechanisms. This study aims to evaluate phytoplankton phenology patterns over the SWIP area, during an 18-year period (September 1997 - August 2015), using an objective, unsupervised partition strategy (Hierarchical Agglomerative Clustering – HAC) based on phenological indices derived from satellite ocean colour data. The partition is then used to describe region-specific phytoplankton phenological patterns related to bloom magnitude, frequency, duration and timing. Region-specific variability patterns in phenological indices and their linkages with environmental determinants, including local ocean physical-chemical variables, hydrodynamic variables and large scale climate indices, were explored using Generalized Additive Models (GAM). HAC analyses identified five coherent phenoregions over SWIP, with distinctive phytoplankton phenological properties: two open ocean and three coastal regions. Over the open ocean, a single, low magnitude and long bloom event per year, was regularly observed. Coastal phenoregions exhibited up to six short bloom events per year, and higher intra-annual and variability. GAM models explained 50 to 90% of the variance of all phenological indices except bloom initiation timing, and revealed that interannual patterns in phytoplankton phenology and their environmental drivers varied markedly among the five phenoregions. Over the oceanic phenoregions, large-scale climate indices (Eastern Atlantic Pattern, Atlantic Meridional Oscillation), mixed layer depth (MLD) and nitrate concentration preceding primary bloom events were influential predictors, reflecting the relevance of nutrient limitation. For the Coastal-Slope, a relatively more light-limited phenoregion, North Atlantic Oscillation and wind speed were more relevant, and bloom magnitude was also positively influenced by riverine discharge. This variable was a significant predictor of bloom frequency, magnitude and duration over the Riverine-influenced region. Over the Upwelling-influenced region, upwelling intensity and mean annual MLD showed stronger partial effects on phytoplankton phenology. Overall, our phenology-based unsupervised approach produced a biologically-relevant SWIP partition, providing an evaluation of the complexity of interactions between phytoplankton and multiple environmental forcing, particularly over coastal areas.
... However, the difference in peak timing does not affect the duration of the blooms, and the in situ duration is well within the ensemble interquartile range. More generally, discrepancies in predicting bloom timing by largescale biogeochemical models are reported in many studies, e.g., Henson et al. (2018) and Kostadinov et al. (2017). Henson et al. (2018) show that, compared with the satellite data, the 3-D MEDUSA 2.0 (Yool et al., 2013) model estimates spring blooms starting ∼ 50 days late and Southern Hemisphere subtropical blooms starting ∼ 50 days earlier. ...
... By generating an ensemble of seven CMIP5 models, Kostadinov et al. (2017) highlighted that the difference in bloom timing between the model ensemble and satellitederived chlorophyll is typically > 1 month over most of the ocean. This agrees with our study (see Table 4), as most of our ensemble members have earlier bloom initiation dates, and the differences between the ensemble mean and in situ bloom timing, e.g. ...
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The dynamics of biogeochemical models are determined by the mathematical equations used to describe the main biological processes. Earlier studies have shown that small changes in the model formulation may lead to major changes in system dynamics, a property known as structural sensitivity. We assessed the impact of structural sensitivity in a biogeochemical model of intermediate complexity by modelling the chlorophyll and dissolved inorganic nitrogen (DIN) concentrations. The model is run at five different oceanographic stations spanning three different regimes: oligotrophic, coastal, and the abyssal plain, over a 10-year timescale to observe the effect in different regions. A 1-D Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration, and Acidification (MEDUSA) ensemble was used with each ensemble member having a combination of tuned function parameterizations that describe some of the key biogeochemical processes, namely nutrient uptake, zooplankton grazing, and plankton mortalities. The impact is quantified using phytoplankton phenology (initiation, bloom time, peak height, duration, and termination of phytoplankton blooms) and statistical measures such as RMSE (root-mean-squared error), mean, and range for chlorophyll and nutrients. The spread of the ensemble as a measure of uncertainty is assessed against observations using the normalized RMSE ratio (NRR). We found that even small perturbations in model structure can produce large ensemble spreads. The range of 10-year mean surface chlorophyll concentration in the ensemble is between 0.14 and 3.69mgm−3 at coastal stations, 0.43 and 1.11mgm−3 on the abyssal plain, and 0.004 and 0.16mgm−3 at the oligotrophic stations. Changing both phytoplankton and zooplankton mortalities and the grazing functions has the largest impact on chlorophyll concentrations. The in situ measurements of bloom timings, duration, and terminations lie mostly within the ensemble range. The RMSEs between in situ observations and the ensemble mean and median are mostly reduced compared to the default model output. The NRRs for monthly variability suggest that the ensemble spread is generally narrow (NRR 1.21–1.39 for DIN and 1.19–1.39 for chlorophyll profiles, 1.07–1.40 for surface chlorophyll, and 1.01–1.40 for depth-integrated chlorophyll). Among the five stations, the most reliable ensembles are obtained for the oligotrophic station ALOHA (for the surface and integrated chlorophyll and bloom peak height), for coastal station L4 (for inter-annual mean), and for the abyssal plain station PAP (for bloom peak height). Overall our study provides a novel way to generate a realistic ensemble of a biogeochemical model by perturbing the model equations and parameterizations, which will be helpful for the probabilistic predictions.
... ees.hokudai.ac.jp/satellite/index.shtml. The initiative strengthens the links between algorithm developers at a global scale which will help also to guide modelers and policy makers on the specific assumptions underlying each product: the intercomparison among most algorithms presented in Table 2 and to an ensemble mean of Earth System Models is presented in Kostadinov et al. (2017). A user guide for application to open ocean waters on the most common algorithms (Mouw et al., 2017) explains the current global PG algorithms and their associated uncertainties and also includes a discussion on the advantages and disadvantages of these algorithms. ...
... While biogeochemical and RT models require a quantitative assessment of PT or PSC, end users for coastal environmental management need PG products as indicators for water quality, HAB presence, eutrophication and fisheries stock assessment. To help users selecting the appropriate PG data sets, the work already accomplished by inter-comparing (Kostadinov et al., 2017) and by setting up a user guide (Mouw et al., 2017) on global satellite PG needs to be extended to new algorithms and more explicit recommendations on which algorithm is best suited for specific users and science questions. The later can only be done when the uncertainties of these algorithms have been evaluated more consistently (see Gap 2). ...
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To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors’ and in situ data to ensure long-term monitoring of phytoplankton composition.
... ees.hokudai.ac.jp/satellite/index.shtml. The initiative strengthens the links between algorithm developers at a global scale which will help also to guide modelers and policy makers on the specific assumptions underlying each product: the intercomparison among most algorithms presented in Table 2 and to an ensemble mean of Earth System Models is presented in Kostadinov et al. (2017). A user guide for application to open ocean waters on the most common algorithms (Mouw et al., 2017) explains the current global PG algorithms and their associated uncertainties and also includes a discussion on the advantages and disadvantages of these algorithms. ...
... While biogeochemical and RT models require a quantitative assessment of PT or PSC, end users for coastal environmental management need PG products as indicators for water quality, HAB presence, eutrophication and fisheries stock assessment. To help users selecting the appropriate PG data sets, the work already accomplished by inter-comparing (Kostadinov et al., 2017) and by setting up a user guide (Mouw et al., 2017) on global satellite PG needs to be extended to new algorithms and more explicit recommendations on which algorithm is best suited for specific users and science questions. The later can only be done when the uncertainties of these algorithms have been evaluated more consistently (see Gap 2). ...
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To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition.
... The global spatiotemporal distribution of the PFTs both in-T. S. Kostadinov et al.: Carbon-based phytoplankton size classes fluences (e.g., Falkowski and Oliver, 2007) and can be influenced by (e.g., Marinov et al., 2013;Cabré et al., 2014) climate (and shorter-term processes such as seasonality; e.g., Kostadinov et al., 2016a). Therefore, detailed characterization of the structure and function of oceanic ecosystems (i.e., descriptive and predictive understanding of the PFTs) is required as a crucial component of Earth system and climate modeling. ...
... 0) that is also based on the KSM09 PSD retrieval. The effect of recasting to carbon using the allometric relationships is illustrated in Fig. 5, and further discussion is provided in Sect. S3. Comparison with other PFT algorithms is outside the scope of this work, but summaries of the available algorithms can be found in IOCCG, 2014and Hirata, 2015. Kostadinov et al. (2016a compare phenological parameters among 10 PFT algorithms and 7 CMIP5 models as part of the PFT Intercomparison Project (Hirata et al., 2012;Hirata, 2015). ...
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Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the ĝ€unit of accountingĝ€ in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size - picophytoplankton (0.5-2ĝ€-μm in diameter), nanophytoplankton (2-20ĝ€-μm) and microphytoplankton (20-50ĝ€-μm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global <span styleCombining double low line"" classCombining double low line"text">SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield ĝ-1/4 ĝ€-0.25ĝ€-Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter N o which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the N o parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.
... Protistan communities in the surface ocean are taxonomically and functionally highly diverse, with a biomass distribution that is patchy in both space and time, which challenges our ability to assess the biogeography of protistan communities at different scales (Kostadinov et al., 2017). Remote sensing techniques have improved our understanding of near-surface spatial and temporal changes in chlorophyll-a concentrations, a proxy for biomass of all organisms capable of phototrophy, and this technology enables us to determine the interannual variability in peak chlorophyll at any location in the ocean (Brody et al., 2013;Marchese et al., 2019). ...
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Surface ocean eukaryotic phytoplankton biogeography can be determined as chlorophyll-a using remote sensing techniques yet evaluating its community composition remains limited. Given our ability to track site-specific chlorophyll-a concentration, we tested which factors influenced protistan functional trait distribution, and whether the distributions can be inferred from bloom succession. Here we surveyed the Labrador Sea during spring over three consecutive years, sequenced 18S data over 15 stations and collected satellite-derived chlorophyll-a concentration from March to July for each year. We evaluated changes in distribution of taxonomic composition as well as the functional traits of protistan size, trophic strategy (defined as phototrophy, phagotrophy, and mixotrophy as capable of both), motility and dimethylsulfoxide or dimethylsulfoniopropionate production by building a functional trait database after an extensive literature review. More variability in the biogeography of protistan functional traits was explained across water masses, and among years than taxonomic composition and patterns in trait variability were more apparent when site-specific timing of peak chlorophyll-a was considered. We found that reconstructing bloom phenology using days before peak (DBP) chlorophyll explained a significant amount of variability in functional trait community structure that was previously attributed to water masses or years, suggesting that spatial and interannual variations can be explained by the sampling moment during succession. Approximately 30 days prior to peak, mixotrophy as a trophic strategy was replaced by phototrophic protists of typically larger size classes. Our work suggests DBP influences protistan community trait succession that could inform biogeochemical models, and likely acts a proxy for the onset of stratification.
... Percent of subsets in which each validation method correctly identified the algorithm with the lowest true prediction error when algorithms were trained and tested on data from random locations and times as opposed to the locations of true in-situ measurements (random forest with spatial coordinates included). Kostadinov et al., 2017;Reichstein et al., 2019;Xue et al., 2013). While outputs from simulation models have their own errors, agreement or disagreement between data-driven and simulation-driven predictions can yield insights into both models' uncertainties. ...
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Supervised learning allows the prediction of variables measured in-situ from variables that can be measured from satellites. A labeled data set for this purpose is typically created by matching in-situ and satellite data and split into subsets for model training and initial validation. However, the available data are often not randomly distributed in space and time. In theory, this can bias estimates of prediction errors. Here, remote sensing of chlorophyll a in the Baltic Sea serves as an example to demonstrate the importance of this problem in marine remote sensing, and to test how well different statistical designs for validation mitigate it. Semi-synthetic data sets were created by combining daily chlorophyll a fields from a biogeochemical model hindcast with real-world locations and times of in-situ measurements, generated by sampling 2,000 combinations of cruises from an oceanographic database. These data sets were matched with co-located satellite data and used to train and validate four algorithms using remote sensing reflectances as input. The algorithms were validated using different methods including random hold-out sets and various block cross-validation designs based on geographical location, time, or location in predictor space. The resulting error estimates were compared to true errors calculated from differences to the biogeochemical model outputs serving as response variable. All validation methods underestimated prediction errors, in many cases by >30%. While a simple band-ratio algorithm had the smallest true errors (e.g., absolute percentage difference: APD = 50%), estimated errors were smallest for more complicated and in fact less accurate machine learning algorithms. For example, tenfold cross-validation led to selection of the truly best algorithm among four candidates for <10% of data sets. The biases were smallest, but not absent, for spatial block cross-validation, which selected the truly best algorithm for 21-46% of data sets, depending on the error measure. When the analyses were repeated with data that were randomly distributed in space and time, the biases of error estimates based on random splits became much smaller (e.g., 10-fold cross-validation estimated errors within 2% of their true values and selected the truly best algorithm for >99% of data sets), spatial block cross-validation overestimated prediction errors (often by >40%), all algorithms achieved lower true errors, and a random forest made the most accurate predictions overall (APD = 27%). These results show that more attention should be paid to statistical methods for estimating the errors of supervised learning algorithms, e.g., by using multiple validation methods in combination and critically discussing error estimates considering questions of dependence, representativeness, and stationarity. Furthermore, non-random spatiotemporal distribution of labeled data can be a barrier to harnessing the full potential of machine learning algorithms in marine remote sensing.
... Cole et al. (2012) showed that the gaps in the Chl-a series display uncertainty of about 30 days in the initiation (b i ) and up to 15 days in b p of phytoplankton growth. The amplitude (b p ) is the most crucial parameter of phytoplankton growth (Verbesselt et al., 2010;Ferreira et al., 2014;Kostadinov et al., 2017), and therefore the levels of noise-to-signal in Chl-a series significantly affect the phenology. In this regard, gap-filling and noise reduction should be performed before analysis with cautions and considering the spatio-temporal characteristics of Chl-a data. ...
Article
Spatial and temporal patterns of climatological seasonality, interannual variability, and phytoplankton phenology were estimated using satellite-derived ocean color chlorophyll-a data (Chl-a) 1998 to 2020 in the Persian Gulf from. Biogeography of phytoplankton seasonal and interannual climatology was determined using k-means multivariate clustering analysis applied on the Chl-a time-series data. As a result, two distinct regions were identified: one in the deep north and middle area (DZC) with a minimum value of Chl-a in April–July (0.62–0.76 mg m−3) and maximum in December–February (1.07–1.59 mg m−3), and the other in the north–west coastal areas and along the southwest-southern area (SZC) with a minimum value of Chl-a in March (0.84 mg m−3) and maximum in September–October (1.35–1.42 mg m−3). More than 90% of both DZC and SZC regions remained spatially unchanged during the study period. Spatial shifts from SZC to DZC and DZC to SZC were located along the boundaries between two clusters and covered 5.37% and 1.95% of the whole study area, respectively. Phytoplankton phenological metrics (timings of initiation, peak, termination, and growth duration) were estimated from Chl-a data in the stable regions during the 23-years of the study period. Interannual and seasonal variations of phenological metrics on stable pixels showed unparalleled episodic shifts in phytoplankton growth in the SZC and DZC in December–January and May–September, respectively. The initiation of phytoplankton growth shifts to earlier months by 0.58 days y−1 in the DZC and 0.56 days y−1 in the SZC, while the timings of maximum amplitude of phytoplankton growth become later by 0.59 days y−1 in the DZC and 0.47 days y−1 in the SZC, according to interannual linear trends of phenological metrics. Likewise, a relatively stable trend in phytoplankton growth duration and termination was observed over the whole study area. Geographically, a gradient of the growing period detected from northwest (90–110 days) to the east (175 days). However, the spatial analysis showed that the correlation between depth and phytoplankton phenology was not significant, although depth-dependent patterns were observed in both DZC and SZC regions. Moreover, the spatio-temporal correlation between interannual and seasonal cycles of phytoplankton phenology and wind stress, solar radiation, and Sea Surface Temperature (SST) are discussed in the context of the ecosystem state assessment. The results show that the stratification of water layers in summer and wind mixings are the primary seasonal factors affecting the total phytoplankton phenology in the Persian Gulf.
... The abundance-based models chosen are among the most commonly applied in the literature and have been successfully reparameterized for studies in diverse ocean regions, including continental shelf systems (Brito et al., 2015;Sun et al., 2018). The absorptionbased models were chosen based on their global performance metrics (Mouw et al., 2017b) and their demonstrated consistency in capturing multiple phytoplankton phenology metrics in the North Atlantic (Kostadinov et al., 2017). The following sections provide brief overviews of each model, including their principal frameworks, methods used for model development/parameterization, and key differences. ...
Article
The size structure of phytoplankton communities influences important ecological and biogeochemical processes, including the transfer of energy through marine food webs. A variety of algorithms have been developed to estimate phytoplankton size classes (PSCs) from satellite ocean color data. However, many of these algorithms were developed for application to the global ocean, and their performance in more productive, optically complex coastal and continental shelf regions warrants evaluation. In this study, several existing PSC models were applied in the Northeast U.S. continental shelf (NES) region and compared with in situ PSC estimates derived from a local HPLC pigment data set. The effect of regional re-parameterization and incorporation of sea surface temperature (SST) into existing abundance-based model frameworks was investigated and model performance was assessed using an independent data set. Abundance-based model re-parameterization alone did not result in significant improvement in model performance compared with other models. However, the inclusion of SST led to a consistent reduction in model error for all size classes. Of two absorption-based algorithms tested, the best performing approach displayed similar performance metrics to the regional SST-dependent abundance-based model. The SST-dependent model and the absorption-based method were applied to monthly composites of the NES region for April and September 2019 and qualitatively compared. The results highlight the benefit of considering SST in abundance-based models and the applicability of absorption-based PSC methods in optically complex regions.
... The development and application of spectral inversion algorithms to ocean color data have further provided assessments of absorption by phytoplankton pigment [34, 71,72,[80][81][82][83]. Additional algorithm development using these properties has led to new retrievals regarding plankton community composition, including phytoplankton size fractions, the slope of the particle size distribution, and even specific phytoplankton groups, such as coccolithophores (Phylum Haptophyta, Class Coccolithophyceae), Trichodesmium (Phylum Cyanobacteria), and harmful algal species (e.g., [84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99] and references therein). ...
Chapter
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Pigments, as a vital part of phytoplankton, act as the light harvesters and protectors in the process of photosynthesis. Historically, most of the previous studies have been focused on chlorophyll a, the primary light harvesting pigment. With the advances in technologies, especially High-Performance Liquid Chromatography (HPLC) and satellite ocean color remote sensing, recent studies promote the importance of the phytoplankton accessory pigments. In this chapter, we will overview the technology advances in phytoplankton pigment identification, the history of ocean color remote sensing and its application in retrieving phytoplankton pigments, and the existing challenges and opportunities for future studies in this field.
... Synergy between ocean-colour products and SCIAMACHY data have been exploited , as well as synergy with other satellite products (Raitsos et al., 2008;Palacz et al., 2013;Ward, 2015;Brewin et al., 2017a;Lange et al., 2018;Sun et al., 2019;Moore and Brown, 2020). An active grassroots level initiative of interested scientists has assembled validation data, and carried out inter-comparison exercises (Brewin et al., 2011;Hirata et al., 2012;Kostadinov et al., 2017;Mouw et al., 2017). However, much of the work carried out in this domain, so far, has focussed on partitioning the phytoplankton community according to their contributions to the total chlorophyll concentration, rather than carbon concentration. ...
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The element carbon plays a central role in climate and life on Earth. It is capable of moving among the geosphere, cryosphere, atmosphere, biosphere and hydrosphere. This flow of carbon is referred to as the Earth's carbon cycle. It is also intimately linked to the cycling of other elements and compounds. The ocean plays a fundamental role in Earth's carbon cycle, helping to regulate atmospheric CO2 concentration. The ocean biological carbon pump (OBCP), defined as a set of processes that transfers organic carbon from the surface to the deep ocean, is at the heart of the ocean carbon cycle. Monitoring the OBCP is critical to understanding how the Earth's carbon cycle is changing. At present, satellite remote sensing is the only tool available for viewing the entire surface ocean at high temporal and spatial scales. In this paper, we review methods for monitoring the OBCP with a focus on satellites. We begin by providing an overview of the OBCP, defining and describing the pools of carbon in the ocean, and the processes controlling fluxes of carbon between the pools, from the surface to the deep ocean, and among ocean, land and atmosphere. We then examine how field measurements, from ship and autonomous platforms, complement satellite observations, provide validation points for satellite products and lead to a more complete view of OBCP than would be possible from satellite observations alone. A thorough analysis is then provided on methods used for monitoring the OBCP from satellite platforms, covering current capabilities, concepts and gaps, and the requirement for uncertainties in satellite products. We finish by discussing the potential for producing a satellite-based carbon budget for the oceans, the advantages of integrating satellite-based observations with ecosystems models and field measurements, and future opportunities in space, all with a view towards bringing satellite observations into the limelight of ocean carbon research.
... PFTs can be investigated by means of ocean color observations (IOCCG, 2014). A variety of algorithms have been proposed to derive PFTs biomass and production from satellite observations (Kostadinov et al., 2017). However, these data sets are limited to the first optical depth of the ocean, and the derivation of vertical structure and of fluxes other than primary production is highly uncertain (Lee et al., 2015). ...
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In this work we produced a long‐term reanalysis of the phytoplankton community structure in the Mediterranean Sea and used it to define ecoregions. These were based on the spatial variability of the phytoplankton type fractions and their influence on selected carbon fluxes. A regional ocean color product of four phytoplankton functional types (PFTs; diatoms, dinoflagellates, nanophytoplankton, and picophytoplankton) was assimilated into a coupled physical‐biogeochemical model of the Mediterranean Sea (Proudman Oceanographic Laboratory Coastal Ocean Modelling System‐European Regional Seas Ecosystem Model, POLCOMS–ERSEM) by using a 100‐member ensemble Kalman filter, in a reanalysis simulation for years 1998–2014. The reanalysis outperformed the reference simulation in representing the assimilated ocean color PFT fractions to total chlorophyll, although the skill for the ocean color PFT concentrations was not improved significantly. The reanalysis did not impact noticeably the reference simulation of not assimilated in situ observations, with the exception of a slight bias reduction for the situ PFT concentrations, and a deterioration of the phosphate simulation. We found that the Mediterranean Sea can be subdivided in three PFT‐based ecoregions, derived from the spatial variability of the PFT fraction dominance or relevance. Picophytoplankton dominates the largest part of open ocean waters; microphytoplankton dominates in a few, highly productive coastal spots near large‐river mouths; nanophytoplankton is relevant in intermediate‐productive coastal and Atlantic‐influenced waters. The trophic and carbon sedimentation efficiencies are highest in the microphytoplankton ecoregion and lowest in the picophytoplankton and nanophytoplankton ecoregions. The reanalysis and regionalization offer new perspectives on the variability of the structure and functioning of the phytoplankton community and related biogeochemical fluxes, with foreseeable applications in Blue Growth of the Mediterranean Sea.
... Several ocean color algorithms have also been proposed for phytoplankton information inversion in natural waters [21][22][23][24]. Among these efforts, a derivative spectroscopy/similarity index (SI) approach is the most common method for identifying dominant phytoplankton species or groups [25][26][27]. ...
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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.
... The spatiotemporal distribution and identification of remote sensing-derived phytoplankton groups give powerful insights on the dynamics of the marine food web and the ocean's role in climate regulation in the context of the global change Kostadinov et al., 2017). It has been for several decades recognized that detection of phytoplankton from remote sensing images was a major challenge in ocean optics. ...
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We present a new method to identify phytoplankton functional types (PFTs) in the Mediterranean Sea from ocean color data (GlobColour data in the present study) and AVHRR sea surface temperature. The principle of the method is constituted by two very fine clustering algorithms, one mapping the relationship between the satellite data and the pigments and the other between the pigments and the PFTs. The clustering algorithms are constituted of two efficient self‐organizing maps, which are neural network classifiers. We were able to identify and estimate the percentage of six PFTs: haptophytes, chlorophytes, cryptophytes, Synechococcus, Prochlorococcus, and diatoms. We found that these PFTs present a peculiar variability due to the complex physical and biogeochemical characteristics of the Mediterranean Sea: Haptophytes and chlorophytes dominate during winter and mainly in the western Mediterranean basin, while Synechococcus and Prochlorococcus dominate during summer. The dominance of diatoms was mainly observed in spring in the Balearic Sea in response to deep water convection phenomena and near the coastline and estuaries due to important continental inputs. Cryptophytes present a weak concentration in the Aegean Sea in autumn. The validation tests performed on in situ matchups showed satisfying results and proved the ability of the method to reconstruct efficiently the spatiotemporal patterns of phytoplankton groups in the Mediterranean Sea. The method can easily be applied to other oceanic regions.
... 1 grid using a 12x12 top-hat averaging kernel. If >50% of the pixels being averaged are invalid 80 data, the pixel is assigned a missing data value (as inKostadinov et al., 2017). All monthly time 81 resolution datasets are used. ...
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Marine 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.
... Some pigments only occur in specific phytoplankton groups and are thus indicator pigments for their identification, for example, fucoxanthin in diatoms and peridinin in dinoflagellates (Letelier et al., 1993;Vidussi et al., 2001). The identification of these pigments by remote sensing would provide an unprecedented spatio-temporal distribution (Kostadinov et al., 2017) that would give powerful insights on the phytoplankton composition, light absorption, physiological state (Behrenfeld & Boss, 2006), and the dynamics of the marine food web and marine productivity . This was early recognized that detection of major characteristics of the phytoplankton community from remote sensing images was a major challenge in ocean optics. ...
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This study presents a method for estimating secondary phytoplankton pigments from satellite ocean colour observations. We first compiled a large training data set composed of 12000 samples; each sample is composed of ten in‐situ phytoplankton HPLC measured pigment concentrations, GlobColour products of Chlorophyll‐a concentration (Chla) and remote sensing reflectance (Rrs(λ)) data at different wavelengths, in addition to AVHRR sea surface temperature measurements (SST). The resulting dataset regroups a large variety of encountered situations between 1997 and 2014. The non‐linear relationship between the in‐situ and satellite components was identified using a self‐organizing map (SOM), which is a neural network classifier. As a major result, the SOM enabled reliable estimations of the concentration of Chla and of nine different pigments from satellite observations. A cross‐validation procedure showed that the estimations were robust for all pigments (R2>0.75 and an average RMSE=0.016 mg.m‐3). A consistent association of several phytoplankton pigments indicating phytoplankton group specific dynamic was shown at a Global scale. We also showed the uncertainties for the estimation of each pigment.
... deriving phytoplankton taxonomic groups (PTGs) using field and satellite data in the last decades [6][7][8]. Often, there is a good correspondence between PTGs and phytoplankton size structure or functional types. For example, diatoms are the major utilizer of silicate and contribute approximately 20% of global carbon fixation, most of which belong to microphytoplankton size class (> 20μm); Cyanobacteria are another key group that belong to picophytoplankton class (< 2μm), some species of which are the major nitrogen fixers, e.g., Trichodesmium genus [6,9]. ...
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Knowledge on the phenology and distribution of phytoplankton taxonomic groups (PTGs) represent valuable information when studying marine ecosystem, especially in the Arctic Ocean where rapid warming has drastic effects on sea-ice dynamics, which affect the marine food web. Taxonomic groups of phytoplankton can be discriminated based on their pigment signatures, which, in turn, impact their absorption spectra, given that different pigments have different absorption windows in the visible. Using concurrent measurements of phytoplankton diagnostic pigments and absorption spectra (a ph) collected in the Bering and Chukchi Seas, a novel and direct approach was designed for simultaneously estimating the biomass concentrations of several PTGs (C i) as well as their specific absorption coefficient. The chemotaxonomic tool CHEMTAX was applied to twelve diagnostic pigments measured by high-performance liquid chromatography (HPLC). Their results revealed that the phytoplankton community composition was made of nine groups, from which six dominant were identified: diatoms, dinoflagellates, c3-flagellate, haptophytes type 7, two types of prasinophytes. Out of 117 samples, twenty pairs of C i derived by CHEMTAX and measured a ph were randomly selected and used in a linear unmixing model to extract the specific absorption spectral of each group. This step was repeated 1000 times to provide the mean specific absorption of a given phytoplankton group. These specific absorption spectra were used to reconstruct total a ph , which was consistent with the measured a ph (R 2 from 0.8 to 0.95) at all visible wavelengths (400-700 nm). The derived specific absorption spectra were further used with the measured a ph (λ) at ten Moderate Resolution Imaging Spectroradiometer (MODIS) wavebands in a linear unmixing model to test the ability to retrieve the concentrations of PTGs from satellite remote sensing. A comparison between estimated and measured C i showed that the approach used in this study performed best when retrieving five groups (i.e., dinoflagellates, c3-flagellate, haptophytes, two types of prasinophytes) from the nine initially identified using CHEMTAX with a mean absolute percentage error (MAPE) <35%, except for diatoms with a MAPE value of about 45%. Our approach provides a practical basis for estimation of PTGs using a ph (λ) derived from satellite observations and field measurements.
... Even though remote sensing derived phytoplankton types does not provide a full description of the marine ecosystem, its spatio-temporal distribution (including phenology, Kostadinov et al., 2017), and identification of key groups give powerful insights on the dynamics of the marine food web and the ocean's role in climate regulation in the context of the global change . This relevance was early recognized by Platt et al. (2006), who concluded that detection of phytoplankton from remote sensing images was a major challenge in ocean optics. ...
... Similar approaches to the one we use in the present work have been successfully applied at global scale (including tropical and subtropical regions (e.g. Kostadinov et al., 2017;Racault et al., 2017), as well as at regional scale in tropical regions such as the Red Sea (e.g., Gittings et al., 2018;Racault et al., 2015). ...
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of satellite-derived chlorophyll concentration (Chl) are used to analyse the seasonal and non-seasonal patterns of Chl variability and the long-term trends in phytoplankton phenology in the Mediterranean Sea. With marked regional variations, we observe that seasonality dominates variability representing up to 80% of total Chl variance in oceanic areas, whereas in shelf-sea regions high frequency variations may be dominant representing up to 49% of total Chl variance. Seasonal variations are typically characterized by a phytoplankton growing period occurring in spring and spanning on average 170 days in the western basin and 150 days in the eastern basin. The variations in peak Chl concentrations are higher in the western basin (0.88 ± 1.01 mg m −3) compared to the eastern basin (0.35 ± 1.36 mg m −3). Differences in the seasonal cycle of Chl are also observed between open ocean and coastal waters where more than one phyto-plankton growing period are frequent (> 0.8 probability). During the study period, on average in the western Mediterranean basin (based on significant trends observed over ~95% of the basin), we show a positive trend in Chl of +0.015 ± 0.016 mg m −3 decade −1 , and an increase in the amplitude and duration of the phytoplankton growing period by +0.27 ± 0.29 mg m −3 decade −1 and +11 ± 7 days decade −1 respectively. Changes in Chl concentration in the eastern (and more oligotrophic) basin are generally low, with a trend of −0.004 ± 0.024 mg m −3 decade −1 on average (based on observed significant trends over ~70% of the basin). In this basin, the Chl peak has declined by −0.03 ± 0.08 mg m −3 decade −1 and the growing period duration has decreased by −12 ± 7 days decade −1. The trends in phytoplankton Chl and phenology, estimated in this study over the period 1998-2014, do not reveal significant overall decline/increase in Chl concentration or earlier/delayed timings of the seasonal peak on average over the entire Mediterranean Sea basin. However, we observed large regional variations, suggesting that the response of phytoplankton to environmental and climate forcing may be complex and regionally driven.
... There is a plethora of remote sensing algorithms that have been developed over the recent years to estimate the phytoplankton community size structure and functional types (IOCCG, 2014;Mouw et al., 2017). The comparison of the amended model results with three of those remote sensing approaches covering the spectrum of community structure estimates (Kostadinov et al., 2017) is generally consistent with expectations from both explanatory hypotheses: the sequential invasion of phytoplankton size classes along a resource supply gradient, and with expectations from the transient trophic decoupling mechanisms. Discrepancies remain, however, in the degree to which large phytoplankton dominate both between simulated and remote sensing-derived estimates, and among remote sensing estimates of phytoplankton size class abundance (Supp. ...
Article
The measured concentration of chlorophyll a in the surface ocean spans four orders of magnitude, from ~0.01 mg m-3 in the oligotrophic gyres to >10 mg m-3 in coastal zones. Productive regions encompass only a small fraction of the global ocean area yet they contribute disproportionately to marine resources and biogeochemical processes, such as fish catch and coastal hypoxia. These regions and/or the full observed range of chlorophyll concentration, however, are often poorly represented in global earth system models (ESMs) used to project climate change impacts on marine ecosystems. Furthermore, recent high resolution (~10 km) global earth system simulations suggest that this shortfall is not solely due to coarse resolution (~100 km) of most global ESMs. By integrating a global biogeochemical model that includes two phytoplankton size classes (typical of many ESMs) into a regional simulation of the California Current System (CCS) we test the hypothesis that a combination of higher spatial resolution and enhanced resolution of phytoplankton size classes and grazer linkages may enable global ESMs to better capture the full range of observed chlorophyll. The CCS is notable for encompassing both oligotrophic (<0.1 mg m-3) and productive (>10 mg m-3) endpoints of the global chlorophyll distribution. As was the case for global high-resolution simulations, the regional high-resolution implementation with two size classes fails to capture the productive endpoint. The addition of a third phytoplankton size class representing a chain-forming coastal diatom enables such models to capture the full range of chlorophyll concentration along a nutrient supply gradient, from highly productive coastal upwelling systems to oligotrophic gyres. Weaker ‘top-down’ control on coastal diatoms results in stronger trophic decoupling and increased phytoplankton biomass, following the introduction of new nutrients to the photic zone. The enhanced representation of near-shore chlorophyll maxima allows the model to better capture coastal hypoxia along the continental shelf of the North American west coast and may improve the representation of living marine resources. Your personalized Share Link until December 1, 2018: https://authors.elsevier.com/a/1XtyDI7ECcMSw
... However, these in situ observations were taken mainly in the open oligotrophic ocean. Given that previous evaluations of PSC algorithms have focused on the open ocean [30][31][32], a comprehensive assessment of different PSC algorithms is needed for productive continental shelf waters, such as the Northwest Atlantic (NWA). In this study, a total of 9 satellite-derived PSC algorithms were evaluated against in situ observations to assess their ability to detect dominant phytoplankton size classes (pico-, nano-, and micro-phytoplankton) from three sensors, namely the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate-resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). ...
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Phytoplankton community structure and phytoplankton size class (PSC) are linked to ecological and biogeochemical changes in the oceanic environment. Many models developed to obtain the fraction of PSCs from satellite remote sensing have only been evaluated in open oceans, and very limited effort has been carried out to report on the performance of these PSC models in productive continental shelf waters. In this study, we evaluated the performance of nine PSC models in the coastal Northwest Atlantic (NWA) by comparison of in situ phytoplankton pigment measurements with coincidental satellite data from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate-resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). Our results show that no PSC model retrieved all three phytoplankton size classes (pico-, nano-, and micro-phytoplankton) with reliable accuracy in the region of interest. In particular, these PSC models showed poor performance for retrieval of the picophytoplankton fraction of total phytoplankton in our study region, which could be related to the under-representation of pico-dominated samples in the productive waters of the NWA. For the accuracy of retrieved microphytoplankton and combined nano-pico phytoplankton fraction, the regional model developed by Devred et al. (2011) yielded the best result, followed by the model of Brewin et al.
... In addition, a number of satellite algorithms have been developed for estimating the phytoplankton community structure, some of which provide size structure estimations of phytoplankton. The algorithms for assessing PSCs from remote sensing data can be mainly categorized into abundance-based and inherent optical property (IOP)-based approaches [10][11][12]. ...
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Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with in-situ High Performance Liquid Chromatography (HPLC) data for the South China Sea (SCS), collected from August 2006 to September 2011. Four algorithms were evaluated to determine their ability to detect three phytoplankton size classes. Chlorophyll-a (Chl-a) and absorption spectra of phytoplankton (aph(λ)) were also measured to help understand PSC’s algorithm performance. Results show that the three abundance-based approaches performed better than the inherent optical property (IOP)-based approach in the SCS. The size detection of microplankton and picoplankton was generally better than that of nanoplankton. A three-component model was recommended to produce maps of surface PSCs in the SCS. For the IOP-based approach, satellite retrievals of inherent optical properties and the PSCs algorithm both have impacts on inversion accuracy. However, for abundance-based approaches, the selection of the PSCs algorithm seems to be more critical, owing to low uncertainty in satellite Chl-a input data
... Most algorithms perform similarly well over large gradients of co-varying bio-optical properties and reproduce expected trends in the global distribution of PFTs but not at smaller scales of variability [8]. Since different PTF algorithms use distinct approaches, datasets, and validation metrics, the evaluation of their performances requires comprehensive inter-comparisons using the same validation data and needs to consider errors associated with each one of them [10,20,21]. Thus, an appropriate choice of a PFT algorithm will depend on the scientific objectives and the observational capabilities available to validate its performance. ...
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An approach that improves the spectral-based PHYSAT method for identifying phytoplankton functional types (PFT) in satellite ocean-color imagery is developed and applied to one study case. This new approach, called PHYSTWO, relies on the assumption that the dominant effect of chlorophyll-a (Chl-a) in the normalized water-leaving radiance (nLw) spectrum can be effectively isolated from the signal of accessory pigment biomarkers of different PFT by using Empirical Orthogonal Function (EOF) decomposition. PHYSTWO operates in the dimensionless plane composed by the first two EOF modes generated through the decomposition of a space-nLw matrix at seven wavelengths (412, 443, 469, 488, 531, 547, and 555 nm). PFT determination is performed using orthogonal models derived from the acceptable ranges of anomalies proposed by PHYSAT but adjusted with the available regional and global data. In applying PHYSTWO to study phytoplankton community structures in the coastal upwelling system off central Chile, we find that this method increases the accuracy of PFT identification, extends the application of this tool to waters with high Chl-a concentration, and significantly decreases (~60%) the undetermined retrievals when compared with PHYSAT. The improved accuracy of PHYSTWO and its applicability for the identification of new PFT are discussed.
... The annually averaged global stocks are: 0.044 Gt of carbohydrate with monthly range (0.041, 0.05) Gt; 0.17 Gt of protein with monthly range (0.155, 0.18) Gt; and 0.108 Gt of lipid with monthly range (0.098, 0.121) Gt (Fig. 6, and Supplementary Table S4). The largest global stocks are obtained in the month of September, which generally matches with the time of phytoplankton bloom in large parts of the equatorial-southern hemisphere [50]. The smallest stocks are obtained in the month of June, generally after the termination of the spring blooms. ...
Article
Energy value of phytoplankton regulates the growth of higher trophic species, affecting the tropic balance and sustainability of marine food webs. Therefore, developing our capability to estimate and monitor, on a global scale, the concentrations of macromolecules that determine phytoplankton energy value, would be invaluable. Reported here are the first estimates of carbohydrate, protein, lipid, and overall energy value of phytoplankton in the world oceans, using ocean-colour data from satellites. The estimates are based on a novel bio-optical method that utilises satellite-derived bio-optical fingerprints of living phytoplankton combined with allometric relationships between phytoplankton cells and cellular macromolecular contents. The annually averaged phytoplankton energy value, per cubic metre of sub-surface ocean, varied from less than 0.1 kJ in subtropical gyres, to 0.5-1.0 kJ in parts of the equatorial, northern and southern latitudes, and rising to >10 kJ in certain coastal and optically complex waters. The annually averaged global stocks of carbohydrate, protein and lipid were 0.044, 0.17 and 0.108 gigatonnes, respectively, with monthly stocks highest in September and lowest in June, over 1997-2013. The fractional contributions of phytoplankton size classes e.g., picoplankton, nanoplankton and microplankton to surface concentrations and global stocks of macromolecules varied considerably across marine biomes classified as Longhurst provinces. Among these provinces, the highest annually averaged surface concentrations of carbohydrate, protein, and lipid were in North-East Atlantic Coastal Shelves, whereas, the lowest concentration of carbohydrate or lipid were in North Atlantic Tropical Gyral, and that of protein was in North Pacific Subtropical Gyre West. The regional accuracy of the estimates and their sensitivity to satellite inputs are quantified from the bio-optical model, which show promise for possible operational monitoring of phytoplankton energy value from satellite ocean colour. Adequate in situ measurements of macromolecules and improved retrievals of inherent optical properties from high-resolution satellite images, would be required to validate these estimates at local sites, and to further improve their accuracy in the world oceans.
... As an alternative approach, harmonic analysis is based on the Fourier transform and is one of the most reliable techniques for land-cover discrimination from decoupled vegetation phenological signals (Andres, Salas, and Skole 1994;Kostadinov et al. 2017). Harmonic analysis enables a phenological time series to be expressed as a sum of cosine waves and an additive term. ...
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Assessments of tree/grass fractional cover in savannahs using remote sensing are challenging due to the heterogeneous mixture of the two plant functional types. Time-series decomposition models can be used to characterize vegetation phenology from satellite data, but have rarely been used for attributing phenological signal components to different plant functional types. Here, tree/grass dynamics are assessed in savannah ecosystems using time-series decomposition of 14 years of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index data acquired from 2002 to 2015. The decomposition method uses harmonic analysis and tests the individual harmonic terms for statistical significance. Field data of fractional cover of trees and grasses were collected for 28 plots in Kruger National Park, South Africa. Matching MODIS pixels were analysed for their tree/grass phenological signals. Tree/grass annual and interannual variability were then assessed based on the harmonic models. In most harmonic cycles, grass-dominated sites had higher amplitudes than tree-dominated sites, while the tree green-up started earlier than grasses, before the start of the wet season. While changes in tree phenology are gradual, grasses present higher variability over time. Tree cover showed a significant correlation with the amplitude (r (correlation coefficient) = −0.59, p = 0.001) and phase of the first harmonic term (r = −0.73, p = 0.0001) and the number of cycles of the second harmonic term (r = 0. 56, p = 0.002). Grass cover was also significantly correlated with the amplitude (r = 0. 51, p = 0.005) and phase of the first harmonic term (r = 0.55, p = 0.002) and the number of cycles of the second harmonic term (r = −0.52, p = 0.005). The positive correlation of grass cover with phase and negative correlation with number of cycles is indicating a late greening period and higher variability, respectively. Tree cover estimated from the phase of the strongest harmonic term showed a positive correlation with field-measured tree cover (R² (coefficient of determination) = 0.55, p < 0.01, slope = 0.93, root mean square error = 13.26%). The estimated tree cover also had a strong correlation with the woody cover map (r = 0.78, p < 0.01) produced by Bucini. The results show that MODIS time-series data can be used to estimate the fractional tree cover in heterogeneous savannahs from the phase of the plant functional type’s phenological behaviour. This study shows that harmonic analysis is able to discriminate between fractional cover by trees and grasses in savannahs. The quantitative analysis of tree/grass phenology from satellite time-series data enables a better understanding of the dynamics of the tree/grass competition and coexistence.
... Considering the importance of species groups to the role of phytoplankton production, the phenology of various methods to determine phytoplankton size has been compared (Kostadinov et al., 2017), and the phenology of some methods has been connected to environmental conditions (Cabr e, Shields, Soppa, Volker, & Bracher, 2016). However, the changes in phenology of various phytoplankton groups have yet to be explored, which could provide refinements to both retrospective and forecasted modelling efforts. ...
Article
Aim: This study examined phytoplankton blooms on a global scale, with the intention of describing patterns of bloom timing and size, the effect of bloom timing on the size of blooms, and time series trends in bloom characteristics. Location: Global. Methods: We used a change‐point statistics algorithm to detect phytoplankton blooms in time series (1998–2015) of chlorophyll concentration data over a global grid. At each study location, the bloom statistics for the dominant bloom, based on the search time period that resulted in the most blooms detected, were used to describe the spatial distribution of bloom characteristics over the globe. Time series of bloom characteristics were also subjected to trend analysis to describe regional and global changes in bloom timing and size. Results: The characteristics of the dominant bloom were found to vary with latitude and in localized patterns associated with specific oceanographic features. Bloom timing had the most profound effect on bloom duration, with early blooms tending to last longer than later‐starting blooms. Time series of bloom timing and duration were trended, suggesting that blooms have been starting earlier and lasting longer, respectively, on a global scale. Blooms have also increased in size at high latitudes and decreased in equatorial areas based on multiple size metrics. Main conclusions: Phytoplankton blooms have changed on both regional and global scales, which has ramifications for the function of food webs providing ecosystem services. A tendency for blooms to start earlier and last longer will have an impact on energy flow pathways in ecosystems, differentially favouring the productivity of different species groups. These changes may also affect the sequestration of carbon in ocean ecosystems. A shift to earlier bloom timing is consistent with the expected effect of warming ocean climate conditions observed in recent decades. Supporting Information
... Ranking of algorithms according to their performance is a classic exercise for the ocean-color community, that has evolved from comparisons of chlorophyll algorithms (O'Reilly et al., 1998) to more complex and comprehensive approaches recently (Brewin et al., 2015;Kostadinov et al., 2017). Typically, a battery of statistical metrics is used to construct an index of overall performance against a set of matched data with in situ observations (Brewin et al., 2015). ...
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The differences among phytoplankton carbon (Cphy) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The matching in situ data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and in situ data provides a relatively large matching dataset (N > 500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in Cphy. Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions (Chlorophyll-a concentration, B, less than 0.15 mg Chl m−3), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from −35 to +150% and unbiased root mean squared difference from 5 to 10 mg C m−3 among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscattering-based algorithms produce different results at the clearest waters and these differences are discussed in terms of the different algorithms used for optical particle backscattering coefficient (bbp) retrieval.
... Even though remote sensing derived phytoplankton types does not provide a full description of the marine ecosystem, its spatio-temporal distribution (including phenology, Kostadinov et al., 2017), and identification of key groups give powerful insights on the dynamics of the marine food web and the ocean's role in climate regulation in the context of the global change . This relevance was early recognized by Platt et al. (2006), who concluded that detection of phytoplankton from remote sensing images was a major challenge in ocean optics. ...
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During the last two decades, several satellite algorithms have been proposed to retrieve information about phytoplankton groups using ocean color data. One of these algorithms, the so-called PHYSAT-Med, was developed specifically for the Mediterranean Sea due to the optical peculiarities of this basin. The method allows the detection from ocean color images of the dominant Mediterranean phytoplankton groups, namely nanoeukaryotes, Prochlorococcus, Synechococcus, diatoms, coccolithophorids, and Phaeocystis-like phytoplankton. Here, we present a new version of PHYSAT-Med applied to the Ocean Colour—Climate Change Initiative (OC-CCI) database. The OC-CCI database consists of a multi-sensor, global ocean-color product that merges observations from four different sensors. This retuned version presents improvements with respect to the previous version, as it increases the temporal range (since 1998), decreases the cloud cover, improves the bias correction and a validation exercise was performed in the NW Mediterranean Sea. In particular, the PHYSAT-Med version has been used here to analyse the annual cycles of the major phytoplankton groups in the Mediterranean Sea. Wavelet analyses were used to explore the spatial variability in dominance both in the time and frequency domains in several Mediterranean sub-regions, such as the Alboran Sea, Ligurian Sea, Northern Adriatic Sea, and Levantine basin. Results extended the interpretation of previously detected patterns, indicating the dominance of Synechococcus-like vs. prochlorophytes throughout the year at the basin level, and the predominance of nanoeukaryotes during the winter months. The method successfully reproduced the diatom blooms normally detected in the basin during the spring season (March to April), especially in the Adriatic Sea. According to our results, the PHYSAT-Med OC-CCI algorithm represents a useful tool for the spatio-temporal monitoring of dominant phytoplankton groups in Mediterranean surface waters. The successful applications of other regional ocean color algorithms to the OC-CCI database will give rise to extended time series of phytoplankton functional types, with promising applications to the study of long-term oceanographic trends in a global change context.
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Changes in the environmental condition associated with climatic events could potentially influence the PSC dynamics of the regional marine ecosystem. The Indian Ocean dipole (IOD) is one of the critical ocean–atmosphere interactions that affects the climate of the Arabian Sea, and it could be a potential factor influencing the regional PSC distribution. However, the relationship between PSC and IOD remains unclear and less explored. In this study, using the in-situ database acquired from the Arabian Sea, we reparametrized the three−component abundance−based phytoplankton size class model and applied it to reconstructed satellite−derived chlorophyll−a concentration to extract the fractional contribution of phytoplankton size classes to chlorophyll−a concentration. Further, we investigated the influence of IOD on the changes in the biological–physical properties in the Arabian Sea. The results showed that the biological–physical processes in the Arabian Sea are interlinked and the changes in the IOD mode control the physical variables like sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD), which influence the specific PSC abundance. Unprecedented changes in the PSC distribution and physical properties were observed during the extreme positive and negative IOD events, which clearly indicated the potential role of IOD in altering the PSC distribution in the Arabian Sea. This study highlights the impact of extreme climate events on PSC distribution and the need for a better understanding of the associated physical–biological–climate interactions.
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Cette thèse présente une approche novatrice d’analyse et d’observation de la structure de la communauté de phytoplancton à l'échelle mondiale et régionale à l'aide de données satellitaires (couleur de l‘océan et température de surface) et d'observations in-situ. L'approche est basée sur des méthodes neuronales de classification, telles que les cartes auto-organisatrices (SOM) calibrées sur une grande base de données globale formée de mesures satellitaires collocalisées avec des mesures in-situ. Nous avons d’abord développé une méthode d’estimation des pigments phytoplanctoniques secondaires appliquée à l’océan global à partir de mesures satellitaires. Ensuite nous avons réalisé une étude fine de la Méditerranée où les groupes phytoplanctoniques (PFTs) ont été identifiés. En se servant des mesures de profondeur de la couche de mélange (MLD) fournies par les flotteurs ARGO, de la température de surface de la mer (SST) et de la concentration en chlorophylle-a (Chla) satellitaire, nous avons déterminé sept bio-régions basées sur le cycle annuel de ces variables en utilisant une SOM modifiée. Enfin ces bio-régions ont été caractérisées en termes de PFTs. Les méthodes utilisées nous ont permis d’évaluer les incertitudes sur les pigments et sur les PFTs. L’ensemble des méthodes proposées dans la thèse permettent d’effectuer des études similaires dans d’autres régions.
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Colored dissolved organic matter (CDOM) absorption varies significantly across the global oceans, presumably due to differences in source and degradation pathways. Tracking this variability on a global, or even regional, scale requires broad temporal and spatial sampling at high frequency. Satellite remote sensing provides this platform; however, current and near future sensors are/will be limited to measurements within the UV and visible wavelengths (> 350 nm) while most optical proxies estimating CDOM composition, and relevant for understanding largescale biogeochemical processes, use wavelengths less than 350 nm. This dissertation examines global variability in CDOM spectral variability utilizing a variety of optical metrics. After assessing global variability in these optical metrics, we considered the ability to observe changes in remotely-sensed reflectance (Rrs(l)) strictly due to Sg variability. Using the radiative transfer software, HydroLight, and data from Lake Superior, modeled Rrs(l) showed that Sg variability significantly alters Rrs(l) in waters where ag(l) contributes >20% to total non-water absorption (at-w(l)) at 440 nm. Based on the proposed signal-to-noise ratio of NASA’s proposed Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) hyperspectral sensor, Sg variability on the order of 0.001 nm-1 is an observable feature in these waters. We then developed an capable of estimating Sdg free of bias on hyperspectral absorption data. The algorithm shows that the increased spectral resolution of hyperspectral sensors should allow for remote estimation of Sdg and potentially Sg, providing a broad view of biogeochemical variability reflected by Sg.
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A new method of retrieving the parameters of a power-law particle size distribution (PSD) from ocean color remote sensing data was used to assess the global distribution and dynamics of phytoplankton functional types (PFT's). The method retrieves the power-law slope, ξ, and the abundance at a reference diameter, N <sub>0</sub>, based upon the shape and magnitude of the particulate backscattering coefficient spectrum. Relating the PSD to PFT's on global scales assumes that the open ocean particulate assemblage is biogenic. The retrieved PSD's can be integrated to define three size-based PFT's by the percent volume concentration contribution of three phytoplankton size classes – picoplankton (0.5–2 μm in equivalent spherical diameter), nanoplankton (2–20 μm) and microplankton (20–50 μm). Validation with in-situ HPLC diagnostic pigments results in satisfactory match-ups for the pico- and micro-phytoplankton size classes. Global climatologies derived from SeaWiFS monthly data reveal PFT and particle abundance spatial patterns that are consistent with current understanding. Oligotrophic gyres are characterized by lower particle abundance and higher contribution by picoplankton-sized particles than transitional or eutrophic regions. Seasonal succession patterns for size-based PFT's reveal good correspondence between increasing chl and percent contribution by microplankton, as well as increasing particle abundance. Long-term trends in particle abundances are generally inconclusive yet are well correlated with the MEI index indicating increased oligotrophy (i.e. lower particle abundance and increased contribution of picoplankton-sized particles) during the warm phase of an El Niño event. This work demonstrates the utility and future potential of assessing phytoplankton functional types using remote characterization of the particle size distribution.
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A procedure has been proposed by Ciotti and Bricaud (2006) to retrieve spectral absorption coefficients of phytoplankton and colored detrital matter (CDM) from satellite radiance measurements. This was also the first procedure to estimate a size factor for phytoplankton, based on the shape of the retrieved algal absorption spectrum, and the spectral slope of CDM absorption. Applying this method to the global ocean color data set acquired by SeaWiFS over twelve years (1998-2009), allowed for a comparison of the spatial variations of chlorophyll concentration ([Chl]), algal size factor (Sf), CDM absorption coefficient (acdm) at 443 nm, and spectral slope of CDM absorption (Scdm). As expected, correlations between the derived parameters were characterized by a large scatter at the global scale. We compared temporal variability of the spatially averaged parameters over the twelve-year period for three oceanic areas of biogeochemical importance: the Eastern Equatorial Pacific, the North Atlantic and the Mediterranean Sea. In all areas, both Sf and acdm(443) showed large seasonal and interannual variations, generally correlated to those of algal biomass. The CDM maxima appeared in some occasions to last longer than those of [Chl]. The spectral slope of CDM absorption showed very large seasonal cycles consistent with photobleaching, challenging the assumption of a constant slope commonly used in bio-optical models. In the Equatorial Pacific, the seasonal cycles of [Chl], Sf, acdm(443) and Scdm, as well as the relationships between these parameters, were strongly affected by the 1997-98 El Niño/La Niña event.
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Phytoplankton plays an important role in the global carbon cycle via the fixation of inorganic carbon during photosynthesis. However, the efficiency of this ``biological pump of carbon'' strongly depends on the nature of the phytoplankton. Monitoring spatial and temporal variations of the distribution of dominant phytoplankton groups at the global scale is thus of critical importance. Recently, an algorithm has been developed to detect the major dominant phytoplankton groups from anomalies of the marine signal measured by ocean color satellites. This method, called PHYSAT, allows to identify nanoeucaryotes, Prochlorococcus, Synechococcus and diatoms. In this paper, PHYSAT has been improved to detect an additional group, named phaeocystis-like, by analyzing specific signal anomalies in the Southern Ocean during winter months. This new version of PHYSAT was then used to process daily global SeaWiFS GAC data between 1998 and 2006. The global distribution of major phytoplankton groups is presented in this study as a monthly climatology of the most frequent phytoplankton group. The contribution of nanoeucaryotes-dominated waters to the global ocean varies from 45 to 70% depending on the season, whereas both diatoms and phaeocystis-like contributions exhibit a stronger seasonal variability mostly due to the large blooms that occur during winter in the Southern Ocean. Three regions of particular interest are also studied in more details: the Southern Ocean, the North Atlantic, and the Equatorial Pacific. The North Atlantic diatom bloom shows a large interannual variability. Large blooms of both diatoms and phaeocystis-like are observed during winter in the Southern Ocean, with a larger contribution from diatoms. Their respective geographical distribution is shown to be tightly related to the depth of the mixed-layer, with diatoms prevailing in stratified waters. Synechococcus and Prochloroccocus prevail in the Equatorial Pacific, but our data show also sporadic diatoms contributions in this region during La Niña. The observed seasonal cycle and interannual variability of phytoplankton groups in the global ocean suggest that the PHYSAT archive is suitable to study the impact of climate variability on the structure of marine ecosystems.
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Recent ocean warming and subsequent sea ice decline resulting from climate change could affect the northward shift of the ecosystem structure in the Chukchi Sea and Bering Sea shelf region. The size structure of phytoplankton communities provides an index of trophic levels that is crucial to understanding the mechanisms underlying such ecosystem changes and their implications for the future. This study proposes a new ocean color algorithm for deriving this characteristic by using the region's optical properties. The size derivation model (SDM) estimates the phytoplankton size index FL on the basis of size-fractionated chlorophyll-a (chl-a) using the light absorption coefficient of phytoplankton, aph(lambda), and the backscattering coefficient of suspended particles including algae, bbp(lambda). FL was defined as the ratio of algal biomass attributed to cells larger than 5 mum to the total. It was expressed by a multiple regression model using the aph(lambda) ratio, aph(488)/aph(555), which varies with phytoplankton pigment composition, and the spectral slope of bbp(lambda), gamma, which is an index of the mean suspended particle size. A validation study demonstrated that the SDM successfully derived an FL value of 69 % within an error range of ± 20 % for unknown data. The spatial distributions of FL for the cold August of 2006 and the warm August of 2007 were compared to examine application of the SDM to satellite remote sensing. The results suggested that phytoplankton size was responsive to changes in sea surface temperature. Further analysis of satellite-derived FL values and other environmental factors can advance our understanding of ecosystem structure changes in the shelf region of the Chukchi and Bering Seas.
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Photosynthetic production of organic matter by microscopic oceanic phytoplankton fuels ocean ecosystems and contributes roughly half of the Earth's net primary production. For 13 years, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission provided the first consistent, synoptic observations of global ocean ecosystems. Changes in the surface chlorophyll concentration, the primary biological property retrieved from SeaWiFS, have traditionally been used as a metric for phytoplankton abundance and its distribution largely reflects patterns in vertical nutrient transport. On regional to global scales, chlorophyll concentrations covary with sea surface temperature (SST) because SST changes reflect light and nutrient conditions. However, the ocean may be too complex to be well characterized using a single index such as the chlorophyll concentration. A semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chlorophyll a concentration, absorption by CDM and particulate backscattering. First, we show that observed long-term, global-scale trends in standard chlorophyll retrievals are likely compromised by coincident changes in CDM. Second, we partition the chlorophyll signal into a component due to phytoplankton biomass changes and a component caused by physiological adjustments in intracellular chlorophyll concentrations to changes in mixed layer light levels. We show that biomass changes dominate chlorophyll signals for the high latitude seas and where persistent vertical upwelling is known to occur, while physiological processes dominate chlorophyll variability over much of the tropical and subtropical oceans. The SeaWiFS data set demonstrates complexity in the interpretation of changes in regional to global phytoplankton distributions and illustrates limitations for the assessment of phytoplankton dynamics using chlorophyll retrievals alone.
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A large data set containing coincident in situ chlorophyll and remote sensing reflectance measurements was used to evaluate the accuracy, precision, and suitability of a wide variety of ocean color chlorophyll algorithms for use by SeaWiFS (Sea-viewing Wide Field-of-view Sensor). The radiance-chlorophyll data were assembled from various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop (SeaBAM) and is composed of 919 stations encompassing chlorophyll concentrations between 0.019 and 32.79 mugL-1. Most of the observations are from Case I nonpolar waters, and ~20 observations are from more turbid coastal waters. A variety of statistical and graphical criteria were used to evaluate the performances of 2 semianalytic and 15 empirical chlorophyll/pigment algorithms subjected to the SeaBAM data. The empirical algorithms generally performed better than the semianalytic. Cubic polynomial formulations were generally superior to other kinds of equations. Empirical algorithms with increasing complexity (number of coefficients and wavebands), were calibrated to the SeaBAM data, and evaluated to illustrate the relative merits of different formulations. The ocean chlorophyll 2 algorithm (OC2), a modified cubic polynomial (MCP) function which uses Rrs490/Rrs555, well simulates the sigmoidal pattern evident between log-transformed radiance ratios and chlorophyll, and has been chosen as the at-launch SeaWiFS operational chlorophyll a algorithm. Improved performance was obtained using the ocean chlorophyll 4 algorithm (OC4), a four-band (443, 490, 510, 555 nm), maximum band ratio formulation. This maximum band ratio (MBR) is a new approach in empirical ocean color algorithms and has the potential advantage of maintaining the highest possible satellite sensor signal:noise ratio over a 3-orders-of-magnitude range in chlorophyll concentration.
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Many efforts are currently oriented toward extracting more information from ocean color than the chlorophyll a concentration. Among biological parameters potentially accessible from space, estimates of phytoplankton cell size and light absorption by colored detrital matter (CDM) would lead to an indirect assessment of major components of the organic carbon pool in the ocean, which would benefit oceanic carbon budget models. We present here 2 procedures to retrieve simultaneously from ocean color measurements in a limited number of bands, magnitudes, and spectral shapes for both light absorption by CDM and phytoplankton, along with a size parameter for phytoplankton. The performance of the 2 procedures was evaluated using different data sets that correspond to increasing uncertainties: (1) measured absorption coefficients of phytoplankton, particulate detritus, and colored dissolved organic matter (CDOM) and measured chlorophyll a concentrations and (2) SeaWiFS upwelling radiance measurements and chlorophyll a concentrations estimated from global algorithms. In situ data were acquired during 3 cruises, differing by their relative proportions in CDM and phytoplankton, over a continental shelf off Brazil. No local information was introduced in either procedure, to make them more generally applicable. Over the study area, the absorption coefficient of CDM at 443 nm was retrieved from SeaWiFS radiances with a relative root mean square error (RMSE) of 33%, and phytoplankton light absorption coefficients in SeaWiFS bands (from 412 to 510 nm) were retrieved with RMSEs between 28% and 33%. These results are comparable to or better than those obtained by 3 published models. In addition, a size parameter of phytoplankton and the spectral slope of CDM absorption were retrieved with RMSEs of 17% and 22%, respectively. If these methods are applied at a regional scale, the performances could be substantially improved by locally tuning some empirical relationships.
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Using a sampling grid of 67 stations, the influence of basin-wide and subbasin-scale circulation features on phytoplankton community composition and primary and new productions was investigated in the eastern Mediterranean during winter. Taxonomic pigments were used as size class markers of phototroph groups (picophytoplankton, nanophytoplankton and microphytoplankton). Primary production rates were computed using a light photosynthesis model that makes use of the total chlorophyll a (Tchl a) concentration profile as an input variable. New production was estimated as the product of primary production by a pigment-based proxy of the f ratio (new production/total production). For the whole eastern Mediterranean, Tchl a concentration was 20.4 mgm-2, and estimated primary and new production were 0.27 and 0.04 gCm-2d-1, respectively, when integrated between the surface and the depth of the productive zone (1.5 times the euphotic layer). Nanophytoplankton and picophytoplankton (determined from the pigment-derived criteria) were the dominant size classes and contributed to 60 and 27%, respectively, of Tchl a, while microphytoplankton contributed only to 13%. Subbasin and, to a certain extent, mesoscale structures (cyclonic and anticyclonic gyres) were exceptions to this general trend. Anticyclonic gyres were characterized by low Tchl a concentrations (18.8+/-4.2mgm-2, with the lowest value being 12.4 mgm-2) and the highest picophytoplankton contribution (40% of Tchl a). In contrast, cyclonic gyres contained the highest Tchl a concentration (40.3+/-15.3mgm-2) with the highest microphytoplankton contribution (up to 26% of Tchl a). Observations conducted at a mesoscale in the Rhode gyre (cyclonic) region show that the core of the gyre is dominated by microphytoplankton (mainly diatoms), while adjacent areas are characterized by high chlorophyll concentration dominated by picophytoplankton and nanophytoplankton. We estimate that the Rhodes gyre is a zone of enhanced new production, which is 9 times higher than that in adjacent oligotrophic areas of the Levantine basin. Our results confirm the predominance of oligotrophic conditions in the eastern Mediterranean and emphasize the role of subbasin and mesoscale dynamics in driving phytoplankton biomass and composition and, finally, biogeochemical cycling in this area.