Marine phytoplankton play an important role in the global carbon cycle via the biological carbon pump (e.g., IPCC, 2013) and contribute about 50% to the global primary production (Field et al., 1998). Over the past 30 years, ocean color remote sensing has revolutionized our understanding of marine ecosystems and biogeochemical processes by providing continuous global estimates of surface chlorophyll a concentration (chl-a, mg m⁻³), a proxy for phytoplankton biomass (e.g., McClain, 2009). However, chl-a alone does not provide a full description of the complex nature of phytoplankton community structure and function. Phytoplankton have different morphological (size and shape) and physiological characteristics (growth and mortality rates, nutrient uptake kinetics, temperature, and light requirements) as well as different biogeochemical and ecological functions (e.g., silicification, calcification, nitrogen fixation, aggregation and sinking rates, lipid production, energy transfer; e.g., Le Quéré et al., 2005). Phytoplankton community structure is thus important to many fundamental biogeochemical processes, including: nutrient uptake and cycling, energy transfer through the marine food web, deep-ocean carbon export, and gas exchange with the atmosphere. Phytoplankton community composition also has important consequences for fisheries (e.g., fish recruitment) and specific species (Harmful Algal Blooms, HABs; a list of all abbreviations is given in Table 1) can directly impact human health (e.g., Cullen et al., 1997).

The ability to observe the spatial-temporal distribution (including phenology) and variability of different phytoplankton groups is a scientific priority for understanding the marine food web, and ultimately predicting the ocean's role in regulating climate and responding to climate change on various time scales. Thus, identifying the drivers of phytoplankton composition on global and regional scales is required to assess climate ecosystem interactions and to increase our understanding of the role of the ocean's biodiversity for marine ecosystem service provision. Coasts are especially vulnerable to major human threats caused by harmful algal blooms, eutrophication, hypoxia, and other processes deteriorating water quality. High resolution data on phytoplankton diversity is urgently needed for many socio-economic applications (e.g., fisheries, aquaculture, and coastal management, see IOCCG, 2009).

Some fishery models (e.g., Jennigs et al., 2008) already utilize information on phytoplankton biomass derived from ocean color satellites, however information on size and taxonomic composition from satellite is highly desirable to improve stock assessments (IOCCG, 2009). To better represent the variable biogeochemical state of the ocean, Earth System, and climate models (including those used in the IPCC assessments) have increasingly included a larger amount of biological complexity in their ocean biogeochemistry modules. To simplify the representation of the vast planktonic diversity, plankton have been grouped into plankton functional types according to their biogeochemical functions (e.g., Le Quéré et al., 2005). Biogeochemical models now commonly include 3–10 plankton functional types (e.g., Bopp et al., 2013; Laufkötter et al., 2015), with a few models including up to 100 or more types (Follows et al., 2007; Dutkiewicz et al., 2015; Masuda et al., 2017). Since in situ observations on plankton biogeography and abundance are scarce and many vast oceanic regions are too remote to be routinely monitored, biogeochemical modelers rely on surface ocean estimates of phytoplankton composition from satellite observations to evaluate model simulations and help to develop and validate their models. Increased biological realism in these models has been suggested as a mean to reduce the large uncertainty in future projections of net primary production, and carbon export (Bopp et al., 2013; Laufkötter et al., 2015). Information on global phytoplankton community composition from ocean color satellites is therefore highly desirable for Earth system model development and the quantification of key processes related to present and future global biogeochemical cycles. Particularly for the quantification of carbon fluxes in the world's ocean, high quality remote sensing data on phytoplankton community composition are a first priority (see science plan of the EXPORT project, Siegel et al., 2016).

Thus, continuous, global-scale, high-resolution satellite ocean color products that go beyond bulk chl-a and provide information on phytoplankton diversity is urgently needed to improve near-real time and forecasting models for marine services facilitating the above-mentioned applications. User requests for satellite data on phytoplankton diversity as an essential ocean/climate variable is providing impetus for its incorporation into international climate change initiatives and mission (capability) planning. In this article the current state of the art regarding algorithms, their validation and application is reviewed, then the gaps to meet user requirements are discussed, and finally detailed recommendations for future medium and long term actions are provided.

Diversity of phytoplankton, often represented by species richness and evenness, can be characterized in multiple dimensions (e.g., taxonomic, phylogenetic, morphological, or functional diversity, among others). This diversity is staggeringly large and even within a species there are often a large range of ecotypes with different environmental niches, life stages and/or morphological, and physiological characteristics (e.g., Bouman et al., 2006). For almost all purposes scientists tend to cluster species into groups specific to the purposes of their research. For instance, climate scientists and marine biogeochemists define phytoplankton functional types (PFT) based on their biogeochemical functions (e.g., diatoms as silicifying PFT). Based on satellite products, we here refer to any clustering of species (and ecotypes) as “Phytoplankton Groups” (PG). PG defined based on taxonomic criteria are referred to as phytoplankton types (PT), and PG defined based on their size range are referred to as phytoplankton size classes (PSC).

Satellite ocean-color remote sensing is unsurpassed in its ability to characterize the state of the surface ocean biosphere at high temporal and spatial scales. Beyond chl-a, increasing efforts have been invested internationally over the last two decades to develop ocean color algorithms to retrieve information on phytoplankton composition and size structure (see recent summary in IOCCG, 2014 and list of global approaches applied to satellite data in Table 2). These developments provide an opportunity to yield new operational satellite products. Ocean color algorithms to assess phytoplankton diversity make use of information originating from phytoplankton abundance, cell size, bio-optical properties (such as pigment composition, absorption, and backscattering characteristics) to differentiate PG (Table 2, Figure 1 left). The abundance based approaches of Uitz et al. (2006), Brewin et al. (2010), Brewin et al. (2015), and Hirata et al. (2011) use satellite chl-a as input to derive PSC or PT based on empirical relationships linking in situ marker pigments to chl-a which are determined using high precision liquid chromatography (HPLC). Abundance-based approaches use satellite chl-a as input and by that exploit the largest signal in water leaving radiance to extract variability due to PG out of chl-a. This is then a simple calculation and can be applied easily to chl-a products from different sensors. However, they cannot predict atypical associations and may not hold in a future ocean.

Another class of algorithms relies on spectral features in reflectance, absorption, and/or backscattering spectra caused by the variation in phytoplankton structure and pigment composition (Brown and Yoder, 1994; Subramaniam et al., 2002; Alvain et al., 2005, 2008; Westberry et al., 2005; Ciotti and Bricaud, 2006; Devred et al., 2006, 2011; Hirata et al., 2008; Bracher et al., 2009; Kostadinov et al., 2009, 2016; Mouw and Yoder, 2010; Fujiwara et al., 2011; Bricaud et al., 2012; Moore et al., 2012; Sadeghi et al., 2012a; Li et al., 2013; Roy et al., 2013; Ben Mustapha et al., 2014; Werdell et al., 2014). Spectral-based approaches exploit as much of the backscattered spectrum observed by satellite as necessary to extract the signatures of specific PG to ocean color. Generally, these methods are computationally much more expensive and require specific adaptations for each sensor. However, these algorithms rely on much less empirical relationships than the abundance based approaches and are based on physical principles (radiative transfer). Differences exist on the different satellite inputs (e.g., radiance, absorption, backscattering) and the underlying principles (for a comprehensive overview Mouw et al., 2017). Another approach incorporates various environmental parameters to predict PT based on their ecological preferences (Raitsos et al., 2008; Palacz et al., 2013). This method uses artificial neural networks to link the different biological and physical data sets. While the approach of Raitsos et al. (2008) was regionally developed for the North-Atlantic, the approach by Palacz et al. (2013) is not purely based on remote sensing data but also requires a coupling to a dynamic plankton model.

Products obtained from the PG algorithms (Table 2) are typically dominance (Brown and Yoder, 1994; Alvain et al., 2005; Moore et al., 2012; Ben Mustapha et al., 2014), presence or absence of a certain PT (Westberry et al., 2005; Werdell et al., 2014), fraction or concentration of chl-a of the three PSC (Devred et al., 2006, 2011; Uitz et al., 2006; Hirata et al., 2008, 2011; Kostadinov et al., 2009, 2016; Brewin et al., 2010, 2015; Fujiwara et al., 2011; Li et al., 2013; Roy et al., 2013) or a size factor characterizing the contribution of pico- (or micro-) phytoplankton to the phytoplankton community (Ciotti and Bricaud, 2006; Mouw and Yoder, 2010; Bricaud et al., 2012). Currently, only the products OC-PFT (Hirata et al., 2011) and PhytoDOAS (Bracher et al., 2009; Sadeghi et al., 2012a) enable the simultaneous determination of chl-a for several PT. PhytoDOAS retrieves the imprints of absorption characteristics of specific phytoplankton groups among all other atmospheric and oceanic absorbers from top of atmosphere data of the hyperspectral satellite sensor SCIAMACHY (Scanning Imaging Absorption Spectrometers for Atmospheric Chartography). All other satellite-based PG algorithms (Table 2) have been applied to water-leaving reflectance data from multispectral sensors [e.g., SeaWiFS (Sea-viewing Wide Field-of-view Sensor), MERIS (Medium Resolution Imaging Spectrometer), MODIS (Moderate Resolution Imaging Spectroradiometer)].

To be able to detect unexpected changes in phytoplankton community composition, satellite PG data based on exploiting the spectral signatures, and based on limited empirical assumptions are preferred. In the few last years, radiative transfer models (RTM) have been used to develop and assess the sensitivity of analytical (spectral) PG retrievals or to find suitable spectral characteristics necessary for ocean color sensors to retrieve PG. Werdell et al. (2014) and Wolanin et al. (2016) used the GIOP (Generalized Inherent Optical Property) model software (Werdell et al., 2013) to invert reflectance spectra (either water-leaving or top of atmosphere), and Wolanin et al. (2015) used the coupled ocean-atmosphere RTM SCIATRAN (Rozanov et al., 2014) to test the sensitivity of a PT retrieval (PhytoDOAS). Evers-King et al. (2014) and Xi et al. (2015) used the ocean RTM HydroLight (Sequoia Scientific.) to specifically model the variation of composition of PSC or certain (dominant) PT, respectively, and assessed the potential of retrievals in different water types. Werdell et al. (2014) optimized the inversion scheme of GIOP to finally retrieve absence or presence of Noctiluca miliaris from MODIS data, while Wolanin et al. (2016) used this method to identify optimal band placements for multi- and hyper-spectral satellite data for successful retrievals of certain PT. The results of this study indicate that four additional bands (381, 473, 532, and 594 nm) for the Ocean and Land Colour Instrument (OLCI) would potentially enable absorption-based quantitative retrievals of diatoms, cyanobacteria, and coccolithophores. Recent methods have been developed to retrieve PG from in situ hyperspectral algal or particulate absorption coefficients, and validated using in situ measurements (Moisan et al., 2013; Organelli et al., 2013; Zhang et al., 2015). As absorption coefficients can be estimated from satellite measurements using inverse bio-optical models, this opens the way to applications of these methods to satellite data.

Some PG algorithms (most of the ones listed in Table 2) have been inter-compared at the global scale: firstly using in situ PSC (derived from HPLC pigments) in terms of dominance (Brewin et al., 2011) and secondly, under the 2nd Satellite PFT Algorithm International Intercomparison Project: http://pft.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 inter-comparison 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. A global in situ dataset of HPLC and optical properties is being developed to further evaluate these algorithms. This initiative organized and held breakout sessions at the International Ocean Color Symposia (IOCS) in 2013 and 2015 and at a specific expert International Ocean-Colour Coordinating Group (IOCCG) and National Aeronautics and Space Administration (NASA)-sponsored workshop in 2014, which focused on PG algorithms development, validation, and user needs. For each meeting the outcome resulted in a written summary of recommendation for community actions and the planning of future activities (see IOCS, 2013; Bracher et al., 2015a; IOCS, 2015) which also form the baseline for Section Recommendations toward Operational Products of Phytoplankton Diversity from Space.

To date, the majority of existing PG satellite retrieval approaches have relied on HPLC pigment data to derive in situ PG data: for developing and validating algorithms large in situ PT (e.g., Alvain et al., 2005; Hirata et al., 2011; Soppa et al., 2014; Swan et al., 2016) and PSC (e.g., Uitz et al., 2006; Brewin et al., 2010) data sets have been complied, complemented by the global pigment data set compiled under the MAREDAT project (Peloquin et al., 2013) and recent submissions to public data bases: e.g., SEABASS (http://seabass.gsfc.nasa.gov/), BODC (http://www.bodc.ac.uk), LTER Network Data Portal (https://portal.lternet.edu/nis/home.jsp), PANGAEA Data Publisher for Earth & Environmental Science (https://www.pangaea.de). Among all available in situ PG data sets, HPLC-phytoplankton pigment data contain the largest number of observations resulting in the greatest spatial coverage with standardized quality control protocols (Hooker et al., 2012). However, size fractionated in situ data of chl-a serve as a more direct validation data set for assessing satellite retrievals on PSC (e.g., Brewin et al., 2014). The long-term and spatially extended Continuous Plankton Recorder (CPR) data sets have been used for constructing and evaluating ecological algorithms focusing on larger phytoplankton (Raitsos et al., 2008). The CPRs, especially with the recent global data effort (Global Alliance of CPR Surveys, http://www.globalcpr.org/), may provide a unique platform on taxonomic information to modern satellite sensors for several oceanic regions around the globe. Inline (coupled) flow cytometry and microscopy techniques have been developed and enable a more precise classification of the phytoplankton groupings than HPLC marker pigments (e.g., Sosik and Olson, 2007). In addition, phytoplankton group specific Inherent Optical Properties (IOPs, i.e., absorption and backscattering) determined in the field have been used as algorithm inputs for several spectral approaches (Ciotti and Bricaud, 2006; Bracher et al., 2009; Mouw and Yoder, 2010; Fujiwara et al., 2011; Sadeghi et al., 2012a). Hyperspectral IOP measurements when obtained via continuous measurements (e.g., Boss et al., 2013) can help validating satellite-derived PG by increasing the number of match-ups, assessing variability within a satellite pixel, and quantifying the uncertainties in the two-step satellite methods (i.e., from water-leaving reflectance to IOP to PG).

Satellite PG time series data have already been used to assess regionally and globally the variability and trend of phytoplankton community composition (e.g., Brewin et al., 2012; Sadeghi et al., 2012b) and PG phenology (Alvain et al., 2013; Soppa et al., 2016a) also linking to environmental variables. In addition, satellite PG data were used to assess globally particulate organic carbon export (Mouw et al., 2016), for detection of regional HAB events (e.g., Kurekin et al., 2014), the estimation of recruitment of juvenile fish (Trzcinski et al., 2013) and for inferring globally oceanic emissions of volatile organic compounds (Arnold et al., 2009; Booge et al., 2016).

All ocean color data are limited in coverage to sun-light, cloud and ice-free conditions, and only deliver information on the surface ocean (first optical depth which is 4.6 times shallower than the euphotic depth). Therefore, for many applications, additional methods have to be used to resolve variability and trends of phytoplankton community structure and abundance. Satellite-derived algorithms are increasingly compared not only to in situ data but to global model output from Earth System Models. Starting over two decades ago, biogeochemical models began incorporating multiple PT (e.g., Baretta et al., 1995; Le Quéré et al., 2005; Gregg and Casey, 2007) mainly to incorporate their biogeochemical relevance. As a first step models incorporated a “diatom” group given their importance in the silica cycle, but also given their potential important role in carbon export compared to other PT (e.g., Chai et al., 2002). As models became more sophisticated and started to simulate nitrogen biogeochemistry, many added a “diazotroph” class. Given the biogeochemical importance of these groups of phytoplankton, the modeling community refers to them as PFT (see Figure 1 right). This classification is the closest to ocean color PT products as defined above. Though less common, models can also group phytoplankton in terms of size: the model of Ward et al. (2012) includes 25 size classes of phytoplankton. The advantage of such an approach is that it can use empirical allometric relationships of key growth parameters (e.g., maximum growth rates). Such a model output is more compatible with ocean color PSC products.

Since 2009, marine ecosystem modelers collaborate systematically with the remote sensing community in the MARine Ecosystem Model Intercomparison Project (MAREMIP). MAREMIP fosters the development of models based on PFT. Complementary to the Coupled Model Intercomparison Project Phase 5 and Phase 6 efforts (see http://cmip-pcmdi.llnl.gov/), MAREMP thus specifically targets the inter-comparison of the representation of current and future marine biology in global ocean models, and promotes the interactions between modelers and observationalists and the development of targeted observations. MAREMIP, as well as many single model studies conducted by marine ecosystem modelers worldwide (e.g., Ye et al., 2012; Dutkiewicz et al., 2015), have been using satellite-derived PT products for the evaluation of model performance in terms of plankton biogeography and global biogeochemical cycling (e.g., Hashioka et al., 2013; Vogt et al., 2013; Laufkötter et al., 2015). Initial studies have shown that models and satellite estimates of phytoplankton biogeography diverge, for example (a) in the timing of the phytoplankton bloom (Hashioka et al., 2013), (b) in phytoplankton dominance patterns and the global contribution of diatoms to total phytoplankton biomass (Vogt et al., 2013), and (c) in net primary production (Laufkötter et al., 2015).

Current satellite data sets on phytoplankton composition (PG) are not generally available in a format readily adoptable by a wide user community. Some potential users (e.g., fishery managers) still use chl-a rather than satellite PG data, in part due to a lack of confidence in the PG products, and climate modelers use only a limited fraction of the currently existing products, due to the lack of uncertainty estimates associated with each product, and issues related to the compatibility between model and satellite output. In the following section, we detail the gaps, which need to be addressed if we want to respond to user needs and promote the use of a wider range of new remote sensing products (see summary in Table 3, left columns).

In the following we give recommendations how to fill the gaps identified in the previous chapter. Table 3 summarizes the mid- and long-term actions that are detailed below. Note, that several actions will address several gaps simultaneously. We recommend that the implementation of these actions is done in communication and collaboration between ocean color scientists, observationalists, numerical modelers, and other users. This will ensure that products are aligned to new in situ and satellite observational techniques and fulfill the ever changing needs of the wide range of user communities.

Synoptic observations on phytoplankton diversity, obtained from satellite ocean color data, have the potential to improve models for assessing and predicting climate change and for managing marine services, and they are currently the only means available for high resolution, long-term monitoring of changes in marine ecosystem structure at the regional to global scale. Yet, to meet the requirements of an essential ocean/climate variable (highly accurate and error-characterized) further scientific investment into existing and further developed methods is needed. In particular, the satellite phytoplankton group products should: (i) match those requested by the user communities; (ii) provide quantitative per-pixel uncertainty; (iii) exploit past, current and future hyperspectral remote-sensing; (iv) be tuned for regional applications (including coastal and inland-water regions); and (v) exploit better the various streams of satellite information, from the various sensors in space. Improved understanding of how the optical signatures (inherent optical properties) of phytoplankton groups vary will also aid algorithm development. These actions can only be achieved with coordinated and sustained investment, across national and international agencies, and through interdisciplinary co-operation between satellite algorithm developers, in situ experts and end users (e.g., ecosystem modelers).

AB organized the specific CLEO session, lead its discussion, and the writing team which set up the outline of this manuscript. She wrote the draft version and synoptically implemented the revisions by all other coauthors. HB, VB, SD, AH, MH, TH, SL, JU, MV, and AW planned and participated in the writing session after the workshop and substantially helped to revise several versions of the manuscript. In addition also several experts who could not participate in the discussion and writing sessions provided substantial comments and suggestions to improve the manuscript: ABri, RB, AC, LC, ED, AD, NH, CM, EO, and DR.

The ESA SEOM SY-4Sci Synergy project (No. 400112410/14/I-NB) funded partially the contribution of AB, ABri, RB, SL, and AW. SD acknowledges NASA (NNX13AC34G) for funding. CM acknowledges funding from NNX13AC34G for this effort.