Remote Sensing of Environment 171 (2015) 58–74

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Remote Sensing of Environment

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Assessing phytoplankton community composition from hyperspectralmeasurements of phytoplankton absorption coefficient andremote-sensing reflectance in open-ocean environments

Julia Uitz a,⁎, Dariusz Stramski b, Rick A. Reynolds b, Jean Dubranna a,1

a Sorbonne Universités, UPMC Univ Paris 06, CNRS, Observatoire Océanologique de Villefranche (OOV), Laboratoire d'Océanographie de Villefranche (LOV), 181 Chemin du Lazaret,06 230 Villefranche-sur-Mer, Franceb Marine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093-0238, USA

⁎ Corresponding author at: Laboratoire d'Océanographdu Lazaret, 06230 Villefranche-sur-Mer, France.

E-mail address: julia.uitz@obs-vlfr.fr (J. Uitz).1 Present address: MINES ParisTech, PSL Research Un

Energy Centre (O.I.E.), F-06904, Sophia-Antipolis, France.

http://dx.doi.org/10.1016/j.rse.2015.09.0270034-4257/© 2015 Elsevier Inc. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 19 December 2014Received in revised form 18 September 2015Accepted 26 September 2015Available online 24 October 2015

Keywords:Ocean opticsRemote-sensing reflectanceAbsorption coefficientPhytoplankton pigmentsPhytoplankton diversity

This study assesses the ability of hyperspectral opticalmeasurements to discriminate changes in the compositionof phytoplankton communities in open-ocean non-bloom environments. A large set of in situ near-surface mea-surements, comprising phytoplankton pigment determinations and hyperspectral optical data of phytoplanktonabsorption coefficient, aph(λ), and remote-sensing reflectance, Rrs(λ), are used in the analysis. Measurementswere collected in different ecological provinces in the Pacific and Atlantic Oceans with chlorophyll-a concentra-tions ranging from about 0.02 to 1.5 mg m−3. Hierarchical cluster analysis was applied to measured spectra ofaph(λ) and Rrs(λ) and the second-derivative spectra of these optical variables. The resulting optical-based classi-fications of the examined stations compared favorably (similarity index ≥ 0.73) with a classification of phyto-plankton community composition calculated from pigment measurements. Similarities between pigment-based and optically-based classifications were better for the optical data of aph(λ) than Rrs(λ), with only slightimprovements resulting from the use of the second derivative spectra as opposed to the non-differentiated spec-tra. An Empirical Orthogonal Function (EOF) analysis was applied to the optical spectra to examine the correla-tion of dominant modes of variability with several bio-optical and biogeochemical properties. This analysissupports the notion that the performance of the optical approach is strongly associated with the effects of differ-ences in pigment composition, cell size, and intracellular pigment concentration among different phytoplanktoncommunities on the optical properties of the ocean.

© 2015 Elsevier Inc. All rights reserved.

1. Introduction

Marine phytoplankton are fundamentally important to global car-bon cycling and its role in climate. Phytoplankton communities in theocean are characterized by large taxonomic diversity which strongly in-fluences their role in ecosystem and biogeochemical functioning. Thecomposition and size structure of phytoplankton communities aremajor determinants of biogeochemical fluxes, especially by regulatingthe photosynthetic efficiency of carbon fixation (e.g., Bouman, Platt,Sathyendranath, & Stuart, 2005; Cermeño, Estévez-Blanco, Marañon, &Fernandez, 2005; Uitz, Huot, Bruyant, Babin, & Claustre, 2008), thetransfer of primary production to higher trophic levels (e.g., Margalef,1965; Kiørboe, 1993), and the export of carbon to the deep oceans(e.g., Legendre & Le Fevre, 1995; Guidi et al., 2009). Monitoring the

ie de Villefranche, 181 Chemin

iversity, Observation, Impacts,

distribution and dynamics of phytoplankton communities on differenttime and space scales within the world's ocean is therefore of substan-tial interest. Such capabilities are also important because the structureof phytoplankton communities is expected to alter as a result of envi-ronmental and climate changes, which may have profound yet un-known consequences to the functioning of the biological carbon pumpwith feedbacks on the climate system (Bopp, Aumont, Cadule, Alvain,& Gehlen, 2005; Cermeño et al., 2008; Li, McLaughlin, Lovejoy, &Carmack, 2009; Boyd et al., 2013).

Optical sensors deployed on various platforms (in situ, aircraft, satel-lites) offer significant potential to observe phytoplankton communitiesover extended temporal and spatial scales. The development of oceancolor-based algorithms for discriminating different phytoplankton spe-cies or assemblages fromsatellite remote sensinghas been an area of ac-tive research over the last decade (see Nair et al., 2008; Brewin et al.,2011, and references therein; IOCCG, 2014). Some of these algorithmsrely on statistical relationships between community composition andphytoplankton biomass expressed in terms of chlorophyll-a and diag-nostic pigment concentrations (i.e., so-called “abundance-based” ap-proaches). Others utilize available multi-spectral information on the

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absorption or scattering coefficient for identifying phytoplanktongroups based on distinct optical properties (i.e., so-called “spectral-based” approaches). The potential of the spectral-based approachesmay have, however, been hampered because of the limited number ofwavebands available on past and current satellite ocean color sensors.

Recent technological advances have led to the emergence of opticalsensors providing measurements with high spectral resolution. Thesehyperspectral sensors can collect measurements at a series of contigu-ous narrowwavebands of light, hence providingmore spectral informa-tion useful for resolving spectrally unique seawater constituents thanmultispectral sensors operating at a limited number of discretewavebands. One may expect an increased use and role of hyperspectralapproaches for marine biogeochemical applications with further ad-vances in hyperspectral technology and its implementation on a varietyof platforms (Chang et al., 2004; Dickey, Lewis, & Chang, 2006; Lee et al.,2015).

Several studies have suggested that it is feasible to derive informa-tion on phytoplankton community composition from hyperspectral op-tical measurements because some phytoplankton pigments havespecific spectral signatures that may be distinguished from the bulk op-tical signal. Most previous studies focused on examining data ofhyperspectral phytoplankton absorption coefficients for assessing theconcentration of certain phytoplankton pigments (Bidigare, Morrow,& Kiefer, 1989; Hoepffner & Sathyendranath, 1993; Moisan, Moisan, &Linkswiler, 2011), identifying harmful species (Millie et al., 1997;Stæhr & Cullen, 2003) or mixed populations (Kirkpatrick, Millie,Moline, & Schofield, 2000), and determining cell size (Ciotti, Lewis, &Cullen, 2002; Devred et al., 2006; Organelli, Bricaud, Antoine, & Uitz,2013). A few studies also utilized hyperspectral data of remote-sensing reflectance for detecting waters rich in dinoflagellate Kareniabrevis (Craig et al., 2006) or distinguishing Phaeocystis globosa bloomsfrom diatom blooms in coastal areas (Lubac & Loisel, 2007).

A recent study by Torrecilla, Stramski, Reynolds, Millan-Nunez, andPiera (2011) assessed the utility of hyperspectral optical measurementsfor discriminating different phytoplankton assemblages in open oceanwaters under non-bloom conditions. Such conditions are characterizedby co-existence of many phytoplankton species and taxonomic groupswith no dominance of particular species or group, and thus are morechallenging than blooms dominated by a single species. The study ofTorrecilla et al. (2011)was based on hierarchical cluster analysis and in-cluded analysis of ordinary and derivative spectra of phytoplankton ab-sorption coefficient and remote-sensing reflectance. As a proof-of-concept this method was applied to a small dataset (9 oceanographicstations) of pigment determinations used in conjunction withhyperspectral measurements of particulate and phytoplankton absorp-tion coefficients as well as modeled hyperspectral data of remote-sensing reflectance. The results suggested that the second derivativespectra of both the hyperspectral phytoplankton absorption andremote-sensing reflectance are most promising for discriminating dif-ferent phytoplankton assemblages, with the best discrimination provid-ed by phytoplankton absorption. Similar results were obtained with alarger set of data from the eastern Atlantic Ocean (Taylor et al., 2011).The derivative analysis was also shown to offer a powerful tool forextracting information fromhyperspectral optical data in previous stud-ies (e.g., Bidigare et al., 1989; Millie et al., 1997; Louchard et al., 2002;Taylor et al., 2011; Organelli et al., 2013).

Using a larger set of field data that includes measurements from dif-ferent oceanic basins, we follow an approach similar to that of Torrecillaet al. (2011) to (i) evaluate the potential of hyperspectral optical datafor deriving information on phytoplankton community composition invarious open ocean environments under non-bloom conditions; (ii)compare the usefulness of hyperspectral remote-sensing reflectanceand phytoplankton absorption coefficients; and (iii) provide insightsto an understanding of environmental conditions that may have favor-able or detrimental effects on the performance of the hyperspectral ap-proach. Ourwork is based on the analysis of a large set ofmeasurements

collected in a variety of biogeographic provinces within the South Pacif-ic and Atlantic Oceans. This dataset includes concomitant measure-ments of phytoplankton pigments, hyperspectral phytoplanktonabsorption coefficients, and hyperspectral remote-sensing reflectance,to which we applied a hierarchical cluster analysis. The pigment-basedclassification is used as a reference for discriminating different groupsof phytoplankton in the dataset. The skills of the optically-based classi-fication are evaluated against such reference.We also applied an empir-ical orthogonal function (EOF) approach, which is valuable for revealingdominant modes of variations in the spectra of particulate absorption(Garver, Siegel, & Mitchell, 1994) or remote-sensing reflectance(Sathyendranath, Prieur, & Morel, 1989; Lubac & Loisel, 2007; Craig,Jones, Li, Lazin, & Horne, 2012), and can also be useful for relating themodes of variability to various biogeochemical and bio-opticalparameters.

2. Collection of field data

2.1. Study areas

The present analysis makes use of data collected during two ocean-ographic cruises. The French cruise BIOSOPE (BIogeochemistry and Op-tics SOuth Pacific Experiment) onboard R/V L'Atalante was conductedfrom 17 October to 15 December, 2004 in the eastern South PacificOcean (Claustre, Sciandra, & Vaulot, 2008). Measurements were takenalong an east-to-west transect from the Marquesas Islands toConcepcion (Chile). The study area covered a broad range of ecologicalenvironments with varying bio-optical properties, including mesotro-phic waters in the vicinity of the Marquesas Islands, ultra-oligotrophicwaters within the South Pacific Subtropical Gyre, and highly productivewaters in the Chilean upwelling region. Fig. 1 shows the location of thestations where the field data used in this study were collected,superimposed on a monthly composite of chlorophyll-a concentrationdetermined from ocean color imagery as a general indication of the tro-phic regime. Stations MARQ, HNL, EGY, UPX, and UPW were sampledseveral times each. An overview of oceanographic conditions as wellas biogeochemical and bio-optical characteristics of this study area isprovided in Claustre et al. (2008).

The German cruise ANT-XXVI/4 onboard R/V Polarstern covered theperiod from 7 April to 17 May, 2010, and measurements were takenalong a south-to-north transect in the Atlantic Ocean from Punta Arenas(Chile) to Bremerhaven (Germany). The stations were located in di-verse environments such as thehighly productivewaters off the Patago-nian coast, the oligotrophic waters of the South Atlantic Gyre, andmesotrophic waters in the vicinity of Cape Verde Islands (Fig. 1). Al-though not being global, our dataset comprisingmeasurements collect-ed during the BIOSOPE and ANT-XXVI/4 cruises covers multipleecological regimes with large differences in particle assemblages, phy-toplankton communities, and associated optical properties.

The data consist of hyperspectral measurements of phytoplanktonabsorption coefficient (67 spectra) and remote-sensing reflectance (43spectra) with corresponding determinations of algal pigments. To sup-port our analysis, we considered two additional biogeochemical proper-ties related to particle abundance: the concentration of particulateorganic carbon (POC) and the drymass concentration of suspended par-ticulate matter (SPM). For the purpose of consistency with remote-sensing reflectance of the ocean which characterizes oceanic surfacelayer, our analysis considers exclusively the near-surface data of phyto-plankton pigments, absorption coefficients, POC, and SPM (i.e., the datacollected between the surface and 10 m depth).

2.2. Phytoplankton pigments, POC, and SPM

Discrete seawater samples for the determination of phytoplanktonpigmentswere collected using a CTD-rosette equippedwith 12-L Niskinbottles. The samples were vacuum filtered through 25-mm diameter

Fig. 1.Geographic location of the sampling stations considered in the present study for the (a) BIOSOPE and (b) ANT-XXVI/4 cruises. The station locations are superimposed over estimatesof chlorophyll-a concentration derived from satellite data of ocean color which represent averaged monthly composites spanning each cruise period. Different colors of station symbolsindicate the station groups determined from the hierarchical cluster analysis based on phytoplankton pigment-based size classes (shown in Fig. 3). Ocean color data were obtained fromGlobColour, which merges NASA SeaWiFS and MODIS as well as ESA MERIS data (http://hermes.acri.fr/GlobColour/).

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Whatman GF/F glass-fiber filters. Filtered volumes varied depending onthe expected amount of particulate matter (1–5.6 L for BIOSOPE and1–6.5 L for ANT-XXVI/4). The filters were flash-frozen and stored initial-ly in liquid nitrogen and then at−80 °C until analysis in the laboratory.Pigments were extracted in methanol and analyzed with a High Perfor-mance Liquid Chromatography (HPLC) method following a modifiedversion of the procedure of Van Heukelem and Thomas (2001) as de-scribed in Ras, Claustre, and Uitz (2008).

Seawater samples for POC and SPM measurements were alsotaken from the Niskin bottles following CTD-rosette casts. Suspendedparticles for POC analysis were collected by filtration onto pre-combusted 25-mm GF/F filters (Whatman). The filtration volume wasadjusted at each station depending on the particle load (1–8.4 L forBIOSOPE and 1–7.6 L for ANT-XXVI/4). The filters were then transferredinto sterile glass vials, dried at 55 °C, and stored until post-cruise analy-sis in the laboratory. POC concentrationswere determined by high tem-perature combustion of sample filters via standard CHN analysisconsistent with JGOFS protocols (Knap, Michaels, Close, Ducklow, &Dickson, 1996).

For SPM, seawater was filtered onto pre-washed, pre-combusted(450 °C for 5 h), pre-weighed 25-mm GF/F filters (Whatman) or47-mm polycarbonate filters of 0.4 μm pore size (Poretics). Similarto POC, the filtration volume varied among the stations. After filtration,the filters and filter edges were rinsed with deionized waterto remove residual sea salt. The filters were dried at 55 °C, andstored until post-cruise analysis. In the laboratory, the filters weredried again at 55 °C and weighed to measure the dry mass concen-tration of SPM with a micrometric balance (MT5, Mettler-Toledo).Triplicate (for most BIOSOPE stations) and duplicate (for ANT-XXVI/4stations) samples were measured for each station and averaged toobtain the desired POC and SPM concentrations. More detailed

description of POC and SPM determinations can be found in Stramskiet al. (2008).

2.3. Spectral absorption coefficients of particles and phytoplankton

Seawater samples for the determinations of the particulate, ap(λ),and phytoplankton, aph(λ), absorption spectra were filtered ontoWhatman 25-mm GF/F filters (λ is the wavelength of light in vacuo).The filtration volume varied between 1 L and 11.2 L depending on thestation. For the BIOSOPE cruise, the ap(λ) spectra were measured witha filter-pad transmittance method (Mitchell, 1990; Mitchell, Kahru,Wieland, & Stramska, 2002) using a spectrophotometer (Lambda 19,Perkin Elmer) equipped with a 6-cm integrating sphere (Labsphere).Specifically, the measurements were performed in the transmittanceset-up inwhich both the sample and blank filterswere placed at the en-trance port of the integrating sphere. The baseline spectrumrepresenting the particle-free reference was measured on a blank filterpre-soaked in seawater that had beenfiltered through a 0.2-μmpolycar-bonate membrane filter (Whatman Nuclepore). The spectra were mea-sured within the 300–800 nm spectral range at 2-nm increments. Thefinal values of optical density (OD) for the samples were corrected forbaseline and a spectrally-independent offset by adjusting to zero inthe near-infrared spectral region (the so-called null-point correction).The OD values were also corrected for the pathlength amplification ef-fect (β-factor) according to the procedure described in Bricaud andStramski (1990) for the stations in the Chilean upwelling (UPW1–3and UPX1–2) and Allali, Bricaud, and Claustre (1997) for all other sta-tions. Bricaud, Babin, Claustre, Ras, and Tieche (2010) provide a descrip-tion of the spectral phytoplankton absorption coefficients of theBIOSOPE cruise.

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A filter-pad method but with a different geometric configuration ofmeasurement was used for the ANT-XXVI/4 samples. Measurements ofap(λ) were made in the spectral region 300–850 nm (at 1-nm incre-ments) using a Lambda 18 spectrophotometer (Perkin Elmer) equippedwith a 15-cm diameter integrating sphere, with the filters placed insidethe sphere. Themost significant advantages of this setup are that themea-surements of particulate absorption exhibit high stability and reproduc-ibility of measurements taken on both the sample and blank filters,and are subject to a very small or negligible scattering error (Maske &Haardt, 1987; Babin & Stramski, 2002; Röttgers & Gehnke, 2012).The measurements and correction for pathlength amplification followedthe protocol for inside-sphere configuration of measurement asrecommended in Stramski, Reynolds, Kaczmarek, Uitz, and Zheng (2015).

For the samples collected on both cruises the measurements of ab-sorption by non-algal particles, aNAP(λ), were obtained by rescanningthe ap(λ) filters in the spectrophotometer after chemical extraction ofpigments using methanol (Kishino, Takahashi, Okami, & Ichimura,1985). For each sample and each type of spectrophotometric measure-ment, i.e., ap(λ) and aNAP(λ), two to three replicate spectral scans ofOD were made. Individual scans were averaged to produce one ODspectrum from which the final ap(λ) and aNAP(λ) coefficients (in unitsof m−1) were calculated. For the BIOSOPE cruise, an exponential fitwas applied to aNAP(λ) spectra to remove some residual features of pig-ment absorption,whichwere observable in spite of the chemical extrac-tion (see Bricaud et al., 2010). For the ANT-XXVI/4 cruise this procedurewas unnecessary. Finally, the spectral phytoplankton absorption coeffi-cients, aph(λ), were obtained as the difference ap(λ) − aNAP(λ). The 1-nm resolution of the resulting aph(λ) spectra from the ANT-XXVI/4cruise was reduced to 2-nm increments for the sake of consistencywith the BIOSOPE spectra.

2.4. Spectra of remote-sensing reflectance

The spectral remote-sensing reflectance, Rrs(λ), is defined as theratio of the upwelling (i.e., photons traveling along the vertical fromnadir toward zenith direction) water-leaving radiance, Lw(λ), to thedownward plane irradiance, Ed(λ, z = 0+), where both quantities arejust above sea surface, i.e., at z = 0+. For both the BIOSOPE and ANT-XXVI/4 cruises, the desired quantities were derived from radiometricmeasurements where Ed(λ, z = 0+) was measured with a sensormounted on the ship's deck, and Lw(λ) was calculated from measure-ments of upwelling radiance collected at a depth of 0.2mbelow the sur-face, Lu(λ, z=0.2). In brief, the underwater measurements of Lu(λ, z=0.2) were made with a calibrated hyperspectral profiling radiometer(HyperPro, Satlantic, Inc.) mounted on a surface float and operated ata distance of ~100 m from the vessel. The measured Lu(λ, z = 0.2)was propagated to the sea surface using a method that estimates thespectral diffuse attenuation coefficient from spectral ratio of measuredradiance (ProSoft software, Satlantic, Inc.) to obtain Lu(λ, z = 0−) justbelow the surface. The water-leaving radiance above the sea surface,Lw(λ), was then determined from Lu(λ, z = 0−) using the ratio Lw(λ)/Lu(λ, z=0−) = 0.54, which accounts for the transmittance of radianceacross thewater–air interface and the n2-law for radiancewhere n is therefractive index of water (Mueller, Fargion, & McClain, 2003). Finally,the desired quantity of Rrs(λ) for both cruises was obtained as a ratioof Lw(λ) to Ed(λ, z=0+) in the spectral region 300–750 nm at 2-nm in-crements, which is consistent with the spectral resolution of absorptiondata used in this study.

3. Data analysis

3.1. Phytoplankton size classes based on diagnostic pigments

The chlorophyll-a pigment is found in all photosynthetic algae andcyanobacteria (except prochlorophytes which contain the divinyl formof chlorophyll-a) and thus is commonly used as a proxy for

phytoplankton biomass. As a measure of the concentration ofchlorophyll-a, we use the concentration of the total chlorophyll-a(Chl) which is defined as the sum of the concentrations of monovinylchlorophyll-a, divinyl chlorophyll-a, chlorophyllide-a, and the allome-tric and epimeric forms of chlorophyll-a. In contrast to chlorophyll-a, ac-cessory pigments vary among phytoplankton taxa or groups (Jeffrey,Mantoura, & Wright, 1997; Roy, Llewellyn, Egeland, & Johnsen, 2011)and can be used to assess the composition of phytoplankton communi-ties. In this study we use the diagnostic pigment (DP) approach of Uitz,Claustre, Morel, and Hooker (2006), based on the work of Claustre(1994) and Vidussi, Claustre, Manca, Luchetta, and Marty (2001), to es-timate the respective contributions of three phytoplankton size classes,i.e., picophytoplankton fpico, nanophytoplankton fnano, and micro-phytoplankton fmicro, to the chlorophyll-a biomass. This approach uti-lizes the concentrations of the following seven diagnostic pigments:fucoxanthin (Fuco), peridinin (Peri), 19′-hexanoyloxyfucoxanthin(Hex), 19′-butanoyloxyfucoxanthin (But), alloxanthin (Allo), totalchlorophyll-b (chlorophyll-b + divinyl chlorophyll-b; TChlb), and zea-xanthin (Zea). These pigments are selected as biomarkers of specificphytoplankton taxa which are then assigned to one of the three sizeclasses depending on the average cell size of the various microorgan-isms (see Table 1 in Vidussi et al., 2001). The fraction of each pigment-derived size class in the total Chl is calculated as follows:

f micro ¼ 1:41 Fucoþ 1:41 Perið Þ=ΣDP ð1Þ

f nano ¼ 0:60 Alloþ 0:35 Butþ 1:27 Hexð Þ=ΣDP ð2Þ

f pico ¼ 0:86 Zeaþ 1:01 TChlbð Þ=ΣDP ð3Þ

where ΣDP is the sum of theweighted concentrations of the seven diag-nostic pigments:

ΣDP ¼ 1:41 Fucoþ 1:41 Periþ 0:60 Alloþ 0:35 Butþ 1:27 Hexþ 0:86 Zeaþ 1:01 TChlb: ð4Þ

The coefficients used in Eqs. (1) to (4) represent the average ratiosbetween the concentrations of total chlorophyll-a and each diagnosticpigment, and were obtained by multiple regression analysis performedon a global HPLC-derived pigment database (Uitz et al., 2006). Somelimitations of this approach have been recognized; for example, certainpigmentsmay be found inmore than one size class (Vidussi et al., 2001;Uitz et al., 2006). Nevertheless, this approach enables describing simul-taneously the taxonomic composition and approximate size structure ofphytoplankton community (Bricaud, Claustre, Ras, & Oubelkheir, 2004;Ras et al., 2008; Uitz et al., 2009) because picophytoplankton essentiallyinclude prokaryotes (cyanobacteria) and small eukaryotic species,nanophytoplankton prymnesiophytes, and microphytoplankton dia-toms and dinoflagellates.

Brewin et al. (2010) modified the diagnostic pigment approach ofVidussi et al. (2001) and Uitz et al. (2006) to account for the presenceof picoeukaryotes in ultra-oligotrophic environments. Devred,Sathyendranath, Stuart, and Platt (2011) also proposed a modificationthat corrects for the presence of fucoxanthin not only in themicrophytoplankton class but also possibly in the nanophytoplanktonclass. For our dataset the modifications of Brewin et al. (2010) andDevred et al. (2011) induce small changes in the relative contributionsof the three size classes to the total Chl, fpico, fnano, and fmicro. Thesesmall changes have little to no impact on the results of the classificationof the stations (data not shown), and therefore our analysis considersfpico, fnano, and fmicro calculated only from the original method of Uitzet al. (2006).

We use the pigment-based size index (SI) of Bricaud et al. (2004) asan approximate indicator of the dominant cell size of phytoplankton

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communities. SI is calculated as:

SI ¼ 1� f pico þ 5� f nano þ 50� f micro ð5Þ

where the values 1, 5, and 50 are approximate central values of cell size(in units of μm) for the picophytoplankton, nanophytoplankton, andmicrophytoplankton classes, respectively. We also calculated the sumof the concentrations of photoprotective carotenoids (zeaxanthin,diadinoxathin, alloxanthin, α-carotene, and β-carotene), PPC, and usethe ratio PPC/Chl as an index of the relative contribution ofphotoprotective pigments to total algal biomass.

3.2. Analysis of optical spectra

Prior to analysis, the measured spectra of aph(λ) and Rrs(λ) weresmoothed by applying successively a cubic spline function with breakpoints separated by 10 nm and a running mean with an 8-nmwindow.This combination of smoothing filters removed the measurement noiseefficiently while preserving the meaningful spectral features (such asminima, maxima, and changes in slope and curvature) of aph(λ) andRrs(λ) with essentially no adverse effect on the wavelength positionand shape of these features. For final statistical analyses of our opticaldata, we considered the smoothed spectra of aph(λ) and Rrs(λ) withinthe spectral range 430–630 nm. This selection of spectral range wasmade to emphasize the effects of accessory pigments while minimizingthe effects of chlorophyll-a, for example the dominant effect on absorp-tion in the red band around 675 nm, as well as to minimize the effect ofcolored dissolved organic matter (CDOM) on Rrs(λ) associated with in-creasing absorption at the short-wavelength end of visible spectrumand into the UV region.

The statistical analyses of optical data in our study are focused on ex-ploring the utility of changes in the spectral shape of aph(λ) and Rrs(λ),which are potentially driven by phytoplankton pigments. In order tofocus the analysis on the spectral shapes, the aph(λ) and Rrs(λ) spectrawere normalized to the integral of the relevant spectra calculated with-in the selected spectral range 430–630 nm:

aph;n λð Þ ¼ aph λð Þ=Z 630

430aph λð Þdλ ð6Þ

Rrs;n λð Þ ¼ Rrs λð Þ=Z 630

430Rrs λð Þdλ: ð7Þ

Our analysis also considers the second derivative spectra of aph,n(λ)and Rrs,n(λ), denoted by a″ph,n(λ) and R″rs,n(λ) respectively. Thea″ph,n(λ) and R″rs,n(λ) spectra were estimated using a finite differenceapproximation with a band resolution, Δλ, equivalent to the spectralresolution of the aph,n(λ) and Rrs,n(λ) spectra (i.e., 2 nm).

Torrecilla et al. (2011) sought to optimize several parameters chosenfor the analysis of optical spectra used as input for the cluster analysis(i.e., spectral range of the optical dataset, smoothing filter window,band resolution for second derivative calculation). This was done withan aim of maximizing statistical indices of similarity betweenpigment-based and optics-based cluster classifications (see Section 3.3for details on similarity indices). Results of sensitivity analyses withthe current dataset (not shown) indicate, however, that the selectionof these spectral parameters on the sole basis of statistical criteria asso-ciated with similarity indices is influenced by several factors that mayvary among datasets (e.g., number and diversity of stations within thedataset, noise associated with optical measurements). Therefore in thepresent study,we selected spectral parameters on a basis ofmechanisticunderstanding of optical and photophysiological properties of variablesinvolved in the analysis in order to make our approach more robust,general, and applicable to other datasets in future studies.

3.3. Hierarchical cluster analysis

Similarly to the approach used by Torrecilla et al. (2011), a hierarchi-cal cluster analysis (HCA) was used for grouping the oceanographic sta-tions on the basis of data representing different variables (or objects)collected at these stations. Specifically, we applied the HCA to thedataset consisting of three pigment-derived data values, i.e., fpico, fnano,and fmicro, for each station. The resulting pigment-based classificationserved as a reference to evaluate four different optical classificationsof stations, each based on a different set of optical spectra, i.e., aph,n(λ),a″ph,n(λ), Rrs,n(λ), and R″rs,n(λ), within the spectral range 430–630 nm.Various clustering methods based on different algorithms are available(see Legendre & Legendre, 2012). Here we applied the HCA usingthe complete linkage algorithm based on initial calculation of thecosine distance between the objects. The cosine distancewas calculatedas 1 − cos θ, where θ is the angle between two objects. As indicated inTorrecilla et al. (2011), the cosine distance has the advantage of beinginsensitive to the procedure for normalizing the optical spectra and,therefore, emphasizes mainly differences in the spectral shape, andnot magnitude. The results of the HCA are represented graphically inthe form of a cluster tree (or dendrogram). A diagram representingthe linkage distance along the dendrogram is produced with the clustertree and examined to identify the optimal level of partitioning the data,i.e., the optimal number of clusters, in the tree. Here we analyze all clus-ter trees using a total number of four clusters. This threshold has beenselected from the examination of the diagram associated with theremote-sensing reflectancedendrogram,which yields the smallest opti-mal number of clusters.Wemade the choice of cutting all the examineddendrograms at a similar level of partitioning resulting in four clusters.This facilitates the comparison and interpretation of the HCA resultsthat are all based on the same reasonable number of pigment-basedand optically-based clusters. Because the number of four clusters isnot excessively large, this approach aids at achieving a biologically rea-sonable interpretation of the clusters.

To quantify the similarity between the pigment-based classificationand any one of the four optically-based classifications, we used the RandIndex (RI; Rand, 1971). This index is a measure of agreement betweentwo dendrograms based on how pairs of objects are classified. The RIcan assume a value between 0 and 1, with 1 indicating that all pairs ofobjects are clustered in an analogous manner in both dendrograms.Computing the RI requires that both classifications under considerationhave the same number of clusters.

3.4. Empirical orthogonal function analysis

An empirical orthogonal function (EOF) analysis has proven useful forinterpreting optical spectra from marine environments (Sathyendranathet al., 1989; Toole & Siegel, 2001; Lubac & Loisel, 2007; Craig et al.,2012; Taylor, Taylor, Dinter, & Bracher, 2013; Bracher et al., 2015). TheEOF analysis decomposes a dataset into a set of linear orthogonal(i.e., uncorrelated) functions while preserving the variance of the originaldata. The functions ormodes of oscillation (in thepresent case variation inthe spectral shape of absorption or reflectance data) are each associatedwith an amplitude factor or principal component (PC). The modes areranked according to the amount of variance in the original dataset forwhich they account, with the first mode capturing the largest portion ofvariance.

We applied the EOF analysis to four sets of optical spectra, aph,n(λ),a″ph,n(λ), Rrs,n(λ), and R″rs,n(λ), within the range 430–630 nm. Prior tothis analysis these four datasets were modified. The mean spectrumfor each of these datasets was first calculated and this mean spectrumwas then subtracted from each individual spectrum to yield a new setof spectra. Each EOF mode, although orthogonal with one another, maynot necessarily reflect one specific factor or process responsible forvariation in the data (Toole & Siegel, 2001; Lubac & Loisel, 2007). Toprovide insight into the sources of variability associated with each

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mode, we examined the correlation coefficients between the amplitudefactor of each mode and several biogeochemical and bio-opticalparameters.

4. Results and discussion

4.1. Chlorophyll-a concentration, POC, SPM and phytoplankton pigment-based clusters

The distribution of surface chlorophyll-a concentration (Chl) duringthe BIOSOPE and ANT-XXVI/4 cruises is presented in Fig. 2 for the sta-tions considered in this study (i.e., stations for which data of aph(λ)and/or Rrs(λ) are also available). As seen in Fig. 2 the overall range ofvariability in Chl in our dataset is determined by the BIOSOPE data.Whereas Chl varied over an ~80-fold range for BIOSOPE, theANTXXVI/4 data exhibit an ~10-fold range. The BIOSOPE stationsMARQ1–4 were located in the mesotrophic waters surroundingMarquesas Islands with Chl ranging from 0.16 to 0.37 mg m−3

(Fig. 2a). The Chl values dropped substantially from this region alongthe west-to-east transect to the center of the South Pacific SubtropicalGyre where extremely low values were observed (~0.02 mg m−3 atultra-oligotrophic stations GYR2–5). Further eastward, Chl increasedand reached values of ~0.07 mg m−3 at the eastern border of thegyre and maximum values in the Chilean upwelling system(0.8–1.1 mg m−3 at stations UPX1–2 and 1.0–1.5 mg m−3 at stationsUPW1–3). For the ANT-XXVI/4 cruise (Fig. 2b), Chl was high in the Pat-agonian shelf region with values of ~1 mg m−3 (station 265). North-ward of this region, Chl decreased and reached minimum levels in the

Fig. 2. Values for the concentrations of chlorophyll-a (Chl), particulate organic carbon (POC), astations during (a) the BIOSOPE and (b) the ANT-XXVI/4 cruises. Stations are ordered as a fuANT-XXVI/4. Different colors of symbols correspond to the station groups determined from(shown in Fig. 3).

South Atlantic Gyre (b0.04 mg m−3 for stations 277–278). Betweenthe northern border of the South Atlantic Gyre (station 281) and station295 located north of the Strait of Gibraltar, Chl varied between 0.08 and0.15 mg m−3. Further northward, Chl increased from moderate(0.37 mg m−3 at station 296) to maximum values west of the coast ofBrittany in the North Atlantic (1.5 mg m−3 at station 299).

Fig. 2 also shows the distribution of surface POC and SPM along thecruise tracks, which provide complementary information to Chl interms of bulk characteristics of the suspended particulate matter. Simi-lar to Chl, the range of variability in our dataset is determined by theBIOSOPE cruise. The POC varies over a 22-fold range from 12 to268 mg m−3 and the SPM over a 27-fold range from 22 to599mgm−3. Both parameters showminimum levels in the oligotrophicwaters of the South Pacific Subtropical Gyre (stations GYR5 and St14)and maximum levels in the Chilean upwelling at station UPW1. ThePOC / SPM ratio always exceeded a value of 0.24, indicating a dominantcontribution of organic particles to the particle assemblages at all sta-tions (Woźniak et al., 2010).

The cluster tree resulting from theHCA applied to the dataset of fpico,fnano, and fmicro is presented in Fig. 3a. This cluster tree identifies fourclusters of stations assuming a linkage distance slightly above 0.2.Based on the examination of the linkage distance diagram (Fig. 3b), alarger number of clusters (up to 7) could be considered but such classi-fication would be less amenable to meaningful interpretation from anecological point of view. However, as mentioned earlier, the consider-ation of four pigment-based clusters appears to be a reasonable choicefor our analysis and was chosen to be consistent with the cluster analy-sis of optical data as described below. Fig. 3a indicates that the stations

nd suspended particulate matter (SPM) measured at near-surface depths at all samplingnction of longitude from west to east for BIOSOPE and latitude from south to north forthe hierarchical cluster analysis based on phytoplankton pigment-based size classes

Fig. 3. (a) Dendrogramobtained from the hierarchical cluster analysis for all the BIOSOPE and ANT-XXVI/4 stations examined in this study using as input the fraction contributions of threepigment-based phytoplankton size classes to the total Chl; picophytoplankton, fpico, nanophytoplankton, fnano, and microphytoplankton, fmicro. (b) Corresponding diagram of the linkagedistance as a function of node number. The same color scheme for identifying station clusters is used in subsequent Figs. 4–8.

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characterized by dominant contribution of microphytoplankton to thetotal algal biomass (i.e., the Chilean upwelling stations UPW1–3from BIOSOPE and the stations 297 and 298 from ANTXXVI/4) form asingle cluster which we refer to as cluster M (green color). The secondcluster (cluster N; red color) includes the stations dominated bynanophytoplankton (MARQ1–4, EGY2–4, and St17–20 from BIOSOPE,and 265–266 and 295–296 from ANTXXVI/4). Four stations (UPX1–2from BIOSOPE and 267 and 268 from ANTXXVI/4) are unique in asense that the largest similar contributions to Chl are associated withboth the micro- and the picophytoplankton classes. These stations aregrouped in cluster MP (gray color). All remaining stations from both

Fig. 4. Ternary diagram illustrating the fraction contributions of the three phytoplanktonpigment-based size classes, fpico, fnano, and fmicro, to the total Chl for all the stations consid-ered in this study.

cruises are dominated by picophytoplankton and were located in oligo-trophic andmesotrophic waters. These stations are grouped in cluster P(blue color).

Fig. 4 is a ternary diagramof the fractional contributions of pigment-based picophytoplankton, fpico, nanophytoplankton, fnano, andmicrophytoplankton, fmicro, classes to the total chlorophyll-a concentra-tion. The mean and range of values for several biogeochemical and bio-optical parameters associatedwith each of the four pigment-based clus-ters are summarized in Table 1. The pattern of data points within theternary diagram is highly consistent with the results depicted by thepigment-based cluster tree (Fig. 3a). Specifically, the green data pointsin the ternary diagram correspond to the stations within the cluster Mdominated by microphytoplankton. These stations are characterizedby fmicro N 0.46, fnano b 0.45, and fpico b 0.15 (see also Table 1) and werelocated in the Chilean upwelling (BIOSOPE stations UPW1–3) and atthe northern end of the Atlantic transect (ANTXXVI/4 stations 297 and298). The red data points correspond to the cluster N dominated bynanophytoplankton with fnano N 0.46, fmicro b 0.33, and fpico b 0.34.These data are associatedwith the BIOSOPE stations located in the vicin-ity of theMarquesas Islands (MARQ1–4) and at the southeastern borderof the South Pacific Subtropical Gyre (EGY2–4 and St17–20), and withthe ANT-XXVI/4 stations off the Patagonian shelf (265 and 266) andthe Portuguese and Spanish coasts (295 and 296). The blue data pointscorrespond to the cluster P dominated by picophytoplankton withfpico N 0.45, fnano b 0.46, and fmicro b 0.23. These data represent most ofthe BIOSOPE stations located north of and within the South Pacific Sub-tropical Gyre (HNL1–2, GYR2–5, and St01–15), and one station off theChilean upwelling (St21). A large portion of the ANT-XXVI/4 transectalso falls within the cluster P, including the stations from 269 situatedat 36.9° S to 294 at 26.1° N as well as station 299 at the northernmostend of the transect. Finally, the gray points correspond to the clusterMPwith 0.36 b fpico b 0.54, 0.33 b fmicro b 0.41, and the smallest contribu-tion of nanophytoplankton with 0.10 b fnano b 0.31. It is worth notingthat the pattern of these gray data points within the ternary diagramsuggests the possibility of two distinctly separate clusters, each

Table 1Themean values and the range of selected biogeochemical and bio-optical parameters for each of the four pigment-based clusters considered in our analysis. The biogeochemical and bio-optical parameters are listed in the first column. The number of stations grouped within each cluster is also shown in parentheses.

Cluster M (5 stations) Cluster MP (4 stations) Cluster N (16 stations) Cluster P (44 stations)

Mean Range Mean Range Mean Range Mean Range

fpico 0.10 0.05–0.15 0.45 0.37–0.54 0.24 0.08–0.34 0.60 0.45–0.77fnano 0.26 0.14–0.45 0.19 0.1–0.31 0.56 0.46–0.72 0.28 0.17–0.45fmicro 0.64 0.46–0.81 0.36 0.33–0.41 0.20 0.11–0.33 0.11 0.03–0.23SI 33.4 25.41–41.27 19.26 18.27–21.69 12.90 8.43–18.93 7.64 3.49–13.35Chl/SI 0.04 0.03–0.06 0.04 0.02–0.05 0.02 0.01–0.05 0.01 0–0.11PPC/Chl 0.28 0.21–0.36 0.24 0.21–0.29 0.46 0.12–0.7 0.89 0.19–1.48aph(440) / aph(676) 2.14 2.01–2.26 2.33 2.08–2.48 3.25 2.36–4.11 4.16 2.28–6.55aph(440) / ap(440) 0.89 0.85–0.93 0.82 0.76–0.88 0.84 0.75–0.9 0.75 0.4–0.94Chl 1.33 1.07–1.52 0.71 0.34–1.15 0.24 0.06–1.01 0.12 0.02–1.48POC 208 154–268 118 78–155 60 22–136 41 12–257SPM 355 192–599 268 128–348 136 52–331 84 22–419

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consisting of two stations that are consistent with two sub-clusters thatcan be distinguished on the pigment-based cluster tree (see Fig. 3a).Two of these four stations are from the BIOSOPE cruise (UPX1–2 inthe Chilean upwelling) with large fpico (0.49–0.54) and fmicro

(0.35–0.41). The other two stations are from ANTXXVI/4 (stations 267and 268) with somewhat lower fpico (0.37–0.42) and fmicro (0.33), butlarger fnano (0.24–0.31).

4.2. Spectra of phytoplankton absorption coefficient and remote-sensingreflectance

The spectra of normalized phytoplankton absorption coefficient,aph,n(λ), and the derivative spectra, a″ph,n(λ), for the BIOSOPE and ANT-XXVI/4 cruises are presented in Fig. 5. All aph,n(λ) spectra exhibit typicalfeatures of phytoplankton absorption, including major maxima ofchlorophyll-a at ~440 nm (~450 nm for divinyl chlorophyll-a) and~675 nm. In terms of spectral shape, the aph,n(λ) spectra show large var-iability between the four pigment-based clusters and relatively lowwithin-cluster variability. The aph,n(λ) coefficients exhibit an increasein the blue spectral region from the lowest values for the pigment-

Fig. 5. (a) Normalized spectra of phytoplankton absorption coefficient, aph,n(λ), and(b) the second derivative spectra, a″ph,n(λ), for all the BIOSOPE and ANT-XXVI/4 stations.

based cluster M (green curves) through intermediate values for clusterN (red curves) and the highest values for cluster P (blue curves). The op-posite trend among the clusters is observed for the spectral values ofaph,n(λ) in the red band of chlorophyll-a. This overall pattern is indica-tive of flattening of phytoplankton absorption spectra associated witha transition from cluster P through cluster N to cluster M, which is con-sistent with the so-called package effect in phytoplankton cells. Thepackage effect is controlled by the size of cells and intracellular pigmentconcentration (Duysens, 1956; Morel & Bricaud, 1981; Sathyendranath,Lazzara, & Prieur, 1987). With an increase in cell size, intracellular pig-ment concentration, or both, the magnitude of chlorophyll-specific ab-sorption coefficient tends to decrease and the absorption spectrabecome flatter. This latter effect is observed in the data displayed inFig. 5a, where the spectra corresponding to cluster M (i.e., the domi-nance of larger cells) are flatter compared with other spectra. Thehighest blue-to-red absorption ratio (i.e., the weakest package effect)is observed in the spectra belonging to cluster P, which again is consis-tent with the expectation based on the dominant cell size ofpicophytoplankton-dominated data. We note that the contributions ofaccessory pigments, especially photoprotective carotenoids, can alsoplay a role in determining the spectral shape of aph,n(λ). For example,an enhanced absorption in the blue in the spectra from cluster P canbe partly associatedwith the presence of picoplankton-specific caroten-oids such as zeaxanthin.

The absorption bands of most carotenoids overlap in the blue spec-tral region (see, for example, Figure 1 in Bricaud et al., 2004), and thedata of aph,n(λ) exhibit multiple spectral features within the450–500 nm range (Fig. 5a) These features are amplified in the deriva-tive spectra, a″ph,n(λ) (Fig. 5b). It might be possible to identify the spec-tral signatures of pigment biomarkers associated with differentphytoplankton groups from the aph,n(λ) or a″ph,n(λ) spectra. For exam-ple, the spectra associated with the pigment-based cluster P showdistinct absorption features at ~450 nm and ~490 nmwhich are consis-tent with the presence of zeaxanthin (Fig. 5a). The peak centered at~450 nm cannot be distinctly identified for the a″ph,n(λ) spectra becauseit shows a small shift in wavelength between stations (Fig. 5b). Incontrast, the 490-nm feature is well pronounced and characteristicof all a″ph,n(λ) spectra associated with cluster P. The aph,n(λ) anda″ph,n(λ) spectra associated with the clusters N and MP (red andgray curves, respectively) possess a shape that is generally interme-diate between the first two types (M and P) of spectra. This is likelybecause the N and MP clusters are related to a broader diversity ofphytoplankton assemblages and ecological environments.

Similarly to aph,n(λ), the normalized spectra of remote-sensing re-flectance, Rrs,n(λ), exhibit large variability in shape (Fig. 6a). The spectraassociatedwith the pigment-based clustersMand P define the envelopeof variability in Rrs,n(λ) observed in our dataset. Most Rrs,n(λ) spectra as-sociated with picophytoplankton-dominated stations (cluster P) showrelatively high values in the blue, especially for λ b 440 nm, and a

Fig. 6. (a) Normalized spectra of remote-sensing reflectance, Rrs,n(λ), and (b) the secondderivative spectra, R″rs,n(λ), for all the BIOSOPE and ANT-XXVI/4 stations.

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decrease from the blue toward long-wavelength portion of the visiblespectrum. Such large changes in ocean reflectance across the visiblespectrum are typical for oligotrophic environmentswith low concentra-tions of phytoplankton and covarying biogenic matter. In contrast,the Rrs,n(λ) spectra associated with the pigment-based cluster M,corresponding to stations in the Chilean upwelling waters wheremicrophytoplankton dominate, exhibit significantly lower values inthe blue and lower blue-to-green ratio. This feature is typical of environ-ments with higher phytoplankton concentration and covarying mate-rials (Gordon & Morel, 1983). The spectra associated with cluster Malso show elevated values in the 500–600 nm range, consistent with in-creased scattering by particles.

For the pigment-based cluster M that includes the Rrs,n(λ) spectrafrom the BIOSOPE stations UPW1–3 (note that the reflectance data arenot available for two ANT-XXVI/4 stations belonging to this cluster),the variability in the spectral shape of Rrs,n(λ) among the stations isvery small (Fig. 6a). This is not surprising because the three stationsUPW1–3 share the same location (see Fig. 1). For the other clustersthe within-cluster variability of Rrs,n(λ) is more pronounced. For exam-ple, some spectra associatedwith the cluster N have similar shape to thespectra associated with cluster P. These spectra are from the BIOSOPEstations EGY2–5 and St17–19 with moderate values of Chl. In addition,the Rrs,n(λ) spectra measured at the ANT-XXVI/4 station 265 from clus-ter N and at the BIOSOPE stations UPX1–2 from clusterMP are similar tothe Rrs,n(λ) spectra from cluster M. These Rrs,n(λ) spectra were all mea-sured in waters rich in algal biomass and suspended particulate matter(see Fig. 2).

Fig. 6b displays the corresponding second derivative spectra,R″rs,n(λ). The R″rs,n(λ) spectra associatedwith cluster P and some spectraassociated with cluster N (BIOSOPE stations EGY2–5 and St17–19) arecharacterized by large peaks at ~450 nm and ~515 nm emphasizingthe large spectral variability observed within the blue region in theRrs,n(λ) spectra. In contrast, the R″rs,n(λ) spectra associated with clusterM and two spectra associated with cluster MP (BIOSOPE stationsUPX1–2) showmarked troughs and peaks at ~570 nmand ~600 nm re-spectively, which reflect significant curvature in Rrs,n(λ) at these green–yellow wavelengths.

4.3. Optically-based clusters

Figs. 7 and 8 depict the results of the HCA applied to the optical dataof phytoplankton absorption coefficient and remote-sensing reflec-tance. As in the previous figures, the color code indicates thepartitioning consistent with the four clusters based on pigment compo-sition (Fig. 3).

Regarding the classification obtained from the aph,n(λ) spectra(Fig. 7a), the first cluster includes mostly the stations in theChilean upwelling (BIOSOPE stations UPW1–3 and UPX1–2) and atthe northern end of the Atlantic transect (ANT-XXVI/4 station297–298). All five stations dominated by microphytoplankton(pigment-based cluster M) and three stations with large contributionsby both microphytoplankton and picophytoplankton (cluster MP) be-long to this cluster, suggesting that the presence ofmicrophytoplanktonas important contributor to the phytoplankton assemblage likely pro-duces a distinct optical signature in these aph,n(λ) spectra. This clusteralso comprises four stations from different environments with a domi-nance of nanophytoplankton (ANT-XXVI/4 stations 265 and 266) andpicophytoplankton (BIOSOPE station St21 and ANT-XXVI/4 station299). The second aph,n(λ)-based cluster includes only two BIOSOPE sta-tions (GYR2 and GYR4) characterized by the largest values of aph,n(λ) inthe blue and the lowest values in the 540–640 nm spectral region. Bothstationswere located in extremely clear ultra-oligotrophic waters with-in the core of the South Pacific Subtropical Gyre, which are dominatedby picophytoplankton. The third cluster comprises most stations thatwere classified through the pigment analysis as picophytoplankton-dominated. Only two out of thirty-seven stations in this absorption-based cluster were included in pigment-based cluster N. Finally, thefourth aph,n(λ)-based cluster includes mostly the nanophytoplankton-dominated stations. Twelve out of sixteen stations in this cluster wereclassified within the pigment-based cluster N. We note that the linkagediagram in Fig. 7b indicates that five clusters may be considered. Yet, asindicated above, for the purpose of interpretation of our dataset andconsistency in analysis we retained four clusters for all classifications.

The cluster classification obtained from the analysis of the a″ph,n(λ)spectra (Fig. 7c) is to significant degree similar to that obtained withthe aph,n(λ) spectra. The first a″ph,n(λ)-based cluster includes mostnanophytoplankton-dominated stations. The second cluster includesall the stations dominated by microphytoplankton and also thestations with large contributions by both microphytoplankton andpicophytoplankton. In addition, this cluster includes a few stations char-acterized by dominance of nanophytoplankton. The next cluster con-sists of two BIOSOPE stations (GYR3 and St07) whose a″ph,n(λ) spectrashow peaks of maximummagnitude at 480 nm. Finally, the last clusterincludes most stations at which the algal assemblagewas dominated bypicophytoplankton.

In general, although some discrepancies are observed, there is a rea-sonably good agreement between the absorption- and pigment-basedclassifications as indicated by relatively high values of the similarityindexRI (Table 2). These values indicate that 78% and80% of the stationsare classified similarly to the reference pigment-based classificationwhen the HCA is applied to the aph,n(λ) and a″ph,n(λ) data, respectively.

Fig. 8a and b shows the classification obtained by applying the HCAto the Rrs,n(λ) spectra and the corresponding diagram of linkage dis-tance along the dendrogram. As indicated earlier, this diagram wasused to determine that the optimal number of clusters from the pointof view of biological and optical interpretation of our dataset is four.The HCA applied to the Rrs,n(λ) and R″rs,n(λ) spectra produce similar re-sults (Fig. 8a and c). In the R″rs,n(λ)-based classification (Fig. 8c), the firstcluster includes the Chilean upwelling stations UPX1–2 and UPW1–3and the Patagonian shelf station 265 (Chl = 1 mg m−3). Although sta-tions UPW1–3 and UPX1–2 share common characteristics in terms ofphytoplankton community composition (relatively large contributionof microphytoplankton), they are included in two different pigment-based clusters, M and MP. In addition, station 265 shows a different

Fig. 7. Dendrogram obtained from the hierarchical cluster analysis using (a) the aph,n(λ) spectra and (c) the a″ph,n(λ) spectra as input. (b) Diagram of the linkage distance as a function ofnode number corresponding to the aph,n(λ)-based dendrogram.

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phytoplankton composition than the Chilean upwelling stations asit belongs to the pigment-based cluster N. The second cluster basedon R″rs,n(λ) consists exclusively of picophytoplankton-dominated sta-tions from oligotrophic waters. The third cluster is a mixed groupthat includes stations with various phytoplankton assemblagesfrom pigment-based clusters N, MP, and P and moderate Chl(0.22–0.53 mg m−3) and POC values (64–113 mg m−3). The fourthcluster comprises twelve stations, seven of which are dominated by

nanophytoplankton and five by picophytoplankton. Overall the classifi-cations obtained from the Rrs,n(λ) and R″rs,n(λ) spectra yield qualitativelyreasonable results compared to the reference pigment-based classifica-tion. The RI values suggest that 73% and 77% of the stations are classifiedconsistently with the reference pigment-based classification when theHCA is applied to the Rrs,n(λ) and R″rs,n(λ) data, respectively (Table 2).For testing purposes, we also applied the HCA to the four sets of opticalspectra using only the 41 stations for which concurrent data of both

Fig. 8. Dendrogram obtained from the hierarchical cluster analysis using (a) the Rrs,n(λ) spectra and (c) the R″rs,n(λ) spectra as input. (b) Diagram of the linkage distance as a function ofnode number corresponding to the Rrs,n(λ)-based dendrogram.

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aph(λ) and Rrs(λ) are available. The results are essentially unchanged forthe two optical variables regardless of the number of stations includedin the analysis.

Summarizing results obtained with the HCA, we conclude that theapplication of this type of analysis to the ordinary and second derivativespectra of aph,n(λ) and Rrs,n(λ) enables good discrimination between“end-member” phytoplankton assemblages that are associated withprofoundly different ecological regimes within the open ocean. One ofthese end-member assemblages is dominated by picophytoplankton(which corresponds to the pigment-based cluster P) and the other isdominated by microphytoplankton (cluster M). The intermediate

situations with nanophytoplankton-dominated assemblages (clusterN) are better discriminated on the basis of the aph,n(λ) or a″ph,n(λ) spec-tra than on the basis of the Rrs,n(λ) or R″rs,n(λ) spectra. We also foundthat the cluster analysis of the Rrs,n(λ) and R″rs,n(λ) spectra can yield amixed group of stations which is independent of pigment compositionand cell size structure of phytoplankton community. According to thestatistical indices (Table 2), the degree of similarity between thepigment-based and the optically-based cluster partitioning of the sta-tions increased by only a few percent when the second derivative spec-tra of aph,n(λ) and Rrs,n(λ) are used as input to the HCA instead ofordinary spectra.

Table 2Valuesof theRand Index (RI) quantifying the similaritybetween dendrograms based on the pigment data andeach of the four considered optical datasets, i.e., the or-dinary spectra of phytoplankton absorption coefficientand remote-sensing reflectance, aph,n(λ) and Rrs,n(λ)respectively, and the second derivative spectra of phy-toplankton absorption coefficient and remote-sensingreflectance, a″ph,n(λ), and R″rs,n(λ) respectively.

Optical spectra RI

aph,n(λ) 0.78a″ph,n(λ) 0.80Rrs,n(λ) 0.73R″rs,n(λ) 0.77

Fig. 9. Results of the EOF analysis applied to the aph,n(λ) spectra. (a) Spectral shape of thefirst two EOFmodes. (b) Correlation coefficients between the amplitude factor of the firstEOFmode and selected biogeochemical and bio-optical parameters as indicated along thehorizontal axis. (c) Same as panel (b) but for the second EOFmode. The dashed lines indi-cate the 95%-significance level of the correlation coefficients.

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4.4. Spectral variability in optical data from EOF analysis

Although the cluster analysis revealed a high degree of similarity be-tween the classifications derived from the hyperspectral optical dataand the reference pigment-based classification (Figs. 3, 7 and 8 andTable 2), the results of these two types of classifications are not identi-cal. This indicates that the shape of the optical spectra is affected notonly by phytoplankton community composition but also other factors.In this section we present results from an EOF analysis to identify thevariables and/or processes that influence the shape of optical spectra,which can aid in understanding the usefulness of such optical data fordiscriminating phytoplankton groups. For each set of optical spectra,we focus our discussion on the first fewmost significantmodes of spec-tral variation and interpret these modes in terms of correlation withseveral biogeochemical and bio-optical parameters. Specifically, weconsider the pigment-based size indices fpico, fnano, and fmicro, thatcombine information about the diagnostic pigment proportions(i.e., taxonomic composition) and cell size structure of phytoplanktoncommunity. These indices are expected to be major drivers of the vari-ability in the spectral shape of phytoplankton absorption coefficientthat, in turn, influences remote-sensing reflectance. The degree of pack-age effect in algal cells is a major determinant of the spectral shape ofthe phytoplankton absorption coefficient. We use the aph(440)/aph(676) ratio as an index of flattening of the spectra of phytoplanktonabsorption coefficient, which can be caused by the package effect andalso a decrease in the ratio of accessory pigments absorbing in theblue to chlorophyll-a that is the single most important absorbingcompound in the red. In order to account for the influences of cell size,intracellular pigment concentration, and relative contribution ofphotoprotective carotenoids to algal biomass, we consider the parame-ters of SI, Chl/SI, and PPC/Chl, respectively. In addition, we consider theconcentrations of three diagnostic pigments (Zea, Hex, and Fuco),which are also expected to affect the spectral shape of optical properties.Finally, we use the ratio aph(440)/ap(440) as an estimate of the contri-bution of phytoplankton to the total pool of particles and the three prox-ies of the bulk mass concentration of particle pools, i.e., concentrationsof chlorophyll-a (Chl, a proxy for phytoplankton), particulate organiccarbon (POC, a proxy for organic particles), and suspended particulatematter (SPM, a proxy for all particles). These parameters are anticipatedto affect the spectral variability of the remote-sensing reflectancewhichis driven not only by phytoplankton but also by non-algal opticallysignificant constituents.

4.4.1. EOF analysis of phytoplankton absorption coefficientThe EOF analysis of the aph,n(λ) dataset indicates that the first three

modes of EOF account for 97.5% of the total variance (89.9%, 5.7%, and1.9%, respectively). The spectral curves of the first two modes aredisplayed in Fig. 9a. The first mode assumes values with opposite signand hence indicates a negative correlation between normalized phyto-plankton absorption in the blue (b500 nm) and green (N500 nm)

spectral regions. This result likely reflects an increase in aph,n(λ) valuesin the blue and decrease in the green spectral region associatedwith a transition from the pigment-based cluster M (mainlymicrophytoplankton) to clusters P (mainly picophytoplankton) and N(mainly nanophytoplankton) with a hinge point around 500 nm (seeFig. 5a). As discussed earlier, we interpret these features as resulting pri-marily from the package effect that causes flattening of the normalizedabsorption spectra in phytoplankton communities dominated by largecells (e.g., Morel & Bricaud, 1981). This may also result from an increasein the contribution of certain picophytoplankton-specific carotenoidsthat enhance absorption in the blue spectral region.

The amplitude factor of the first EOFmode (PC1) is correlated signif-icantly with all the biogeochemical and bio-optical parameters tested in

Table 3Values of the correlation coefficient between the amplitude factors of the first two EOFmodes (PC1and PC2) of the aph,n(λ) and a″ph,n(λ) datasets and the selected biogeochemicaland bio-optical parameters shown in the first column. Statistically insignificant values ofcorrelation coefficient are displayed in gray.

aph,n(λ) aph,n(λ)PC1 PC2 PC1 PC2

fpico –0.64 –0.41 0.70 0.42fnano 0.04 0.62 –0.27 –0.47fmicro 0.80 –0.05 –0.67 –0.11Zea 0.28 –0.28 –0.05 0.55Hex 0.62 0.24 –0.60 –0.17Fuco 0.71 –0.03 –0.52 –0.12SI 0.80 –0.01 –0.70 –0.14Chl / SI 0.62 –0.08 –0.64 0.17PPC / Chl –0.73 –0.03 0.89 0.06aph(440)/aph(676) –0.73 0.23 0.82 –0.17aph(440)/ap(440) 0.49 0.06 –0.45 0.12Chl 0.79 –0.05 –0.72 –0.02POC 0.81 –0.04 –0.72 0.01SPM 0.82 0.01 –0.72 –0.07

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this study, except for fnano (Fig. 9b andTable 3). Consistentlywith the re-sults of the cluster analysis applied to the aph,n(λ) spectra, PC1 showsstrong correlation with the indices of phytoplankton community struc-ture fpico (correlation coefficient r ≈ −0.6), fmicro (r ≈ 0.8), and SI(r≈ 0.8) (Fig. 9b and Table 3). We also observe a strong correlation be-tween PC1 and aph(440)/aph(676) (r≈−0.7), which indicates that theflattening of the aph,n(λ) spectra is a prominent feature of observed var-iability in aph,n(λ). This flattening may result from changes in phyto-plankton cell size (a proxy SI), intracellular pigment concentration (aproxy Chl/SI), and pigment composition (a proxy PPC/Chl). Thesethree factors substantially influence aph,n(λ) variability, as indicated bystrong correlation between PC1 and SI (r ≈ 0.8), Chl/SI (r ≈ 0.6), andPPC/Chl (r ≈ −0.7). In addition, PC1 is significantly correlated withthe concentrations of two diagnostic pigments (Fuco and Hex;r≈ 0.6–0.7) and strongly correlatedwith the proxies of particle concen-tration (Chl, POC, and SPM; r ≈ 0.8). Because aph,n(λ) is by definitioncontrolled exclusively by phytoplankton, a strong correlation observedbetween PC1 and POC or SPM suggests covariation between phyto-plankton biomass and bulk organic and total particle concentrations atthe BIOSOPE and ANT-XXVI/4 sampling stations (see Fig. 2). The resultsof the correlation analysis also suggest that most biogeochemical andbio-optical parameters under consideration are correlated with oneanother.

The second EOFmode of aph,n(λ) (Fig. 9a) reflects a negative correla-tion between normalized absorption around 440 nm where a majormaximum of chlorophyll-a occurs and the 450–500 nm range dominat-ed by accessory pigments. The secondary shoulder observed at ~490 nmmay result from the absorption maxima of Hex and But, and/or Zea(Bricaud et al., 2004), as potentially supported by a significant correla-tion between the amplitude factor of the second mode (PC2) and fnano(r ≈ 0.6) and fpico (r ≈ −0.4) (Fig. 9c and Table 3). This second modealso exhibits low normalized absorption values with limited variability,which is typically observed atwavelengths N520 nm. In contrast to PC1,PC2 showsmoderate yet significant correlation with a limited set of pa-rameters that are all related to phytoplankton community composition(fpico, fnano, Zea, and Hex). The highest correlation is with fnano (r≈ 0.6).Hence, whereas the first EOFmode is driven bymajor changes in phyto-plankton pigment composition and size structure (e.g., shift frommicrophytoplankton- to picophytoplankton-dominated communities),the second mode may reflect variations associated with changes in thecontribution of the nanophytoplankton group. The third EOF mode is aminor component (2%) of total variance of the aph,n(λ).

The EOF analysis applied to the dataset of a″ph,n(λ) spectra indicatedthat the first six modes explain 89.6% of the total variance. The respec-tive contributions of the six modes are 50.2%, 16.3%, 8.9%, 5.7%, 5.2%,

and 3.3%, indicating that the variance is distributed among a largernumber of modes than for aph,n(λ). The spectral shape of these modescannot be readily interpreted in terms of variables or processes becauseof numerous spectral features (data not shown). The correlation analy-sis between the amplitude factors of thefirst two EOFmodes and the se-lected biogeochemical and bio-optical parameters yields results similarto those obtained from the analysis of the aph,n(λ) dataset.

As supported by the EOF analysis, the spectral shape of phytoplank-ton absorption coefficient appears to be controlled primarily by phyto-plankton community composition through the influences associatedwith changes in cell size structure and accessory pigmentation. Absorp-tion by accessory pigments covers a large part of the visible spectrum.Thus one may expect benefits from hyperspectral measurements ofphytoplankton absorption coefficients for identifying spectral signa-tures of specific diagnostic pigments and thereby discriminating differ-ent phytoplankton groups. In particular, derivative analysis is expectedto improve the extraction of subtle spectral features associated with di-agnostic pigments (e.g., Torrecilla et al., 2011; Organelli et al., 2013).However, because most diagnostic pigments are carotenoids whose re-spective bands of absorption overlap within a relatively broad spectralrange between 400 nm and 580 nm (e.g., Bidigare et al., 1989;Hoepffner & Sathyendranath, 1993; Bricaud et al., 2004), their individu-al effects on aph,n(λ) are expected to be confounded to some degree.Thus, the influence of package effect on aph,n(λ) as driven largely bycell size can be generallymore pronounced and identifiable than the op-tical signatures of individual diagnostic pigments. Nevertheless, as thecell size and pigment composition are generally correlated, their respec-tive effects on aph,n(λ) combine and facilitate the interpretation of theaph,n(λ) spectra in terms of phytoplankton diversity. In addition, theEOF analysis of both the aph,n(λ) and a″ph,n(λ) spectra indicates strongcorrelation with most biogeochemical and bio-optical parametersunder consideration, including non-algal related parameters (Fig. 9band Table 3). This is consistent with typically observed covariation be-tween phytoplankton community structure, cell size, pigment composi-tion, algal biomass, environmental conditions (e.g., light-nutrientregime), and bio-optical properties of seawater (Yentsch & Phinney,1989). In general, stratified oligotrophic environments are associatedwith small cyanobacteria that contain zeaxanthin and show little to nopackage effect. In contrast, dynamic high-nutrient conditions arefavorable to diatoms that are capable of forming rapid blooms of largebiomass. Diatoms are characterized by fucoxanthin as their main carot-enoid and are typically large cellswith a high degree of pigment packag-ing (e.g., Ciotti et al., 2002; Devred et al., 2006; Uitz et al., 2008).

When a deviation from the expected trend between accessory pig-mentation, package effect, and cell size occurs, the identification ofspecific phytoplankton groups from the aph,n(λ) or a″ph,n(λ) spectrawill be more complex. This is the case, for example, for theChilean upwelling BIOSOPE stations UPX1–2 where a mixed popula-tion with large contributions of both picophytoplankton andmicrophytoplankton was found (Figs. 3–4). At these stations theaph,n(λ) spectra show a relatively flat shape, similar to that observedat stations from the pigment-based cluster M (Fig. 5). This featuremight result from package effect associated with the presence oflarge cells or may be caused by photoacclimation by which algae ad-just their intracellular pigment concentration to the environmentalgrowth conditions, i.e., irradiance field and/or nutrient concentration.The stations UPX1–2 show high Chl and SPM (Fig. 2) along with therelatively low values of aph(440)/aph(676) of about 2.4–2.5. Suchvalues are indicative of pronounced flattening of the aph,n(λ) spectraassociated with a reduced contribution of photoprotective carotenoidsto Chl (PPC/Chl = 0.21–0.23) and possibly also high intracellular pig-ment concentration (Chl/SI = 0.04–0.05 mg Chl m−3 μm−1). The re-sults from stations UPX1–2 also suggest significant light attenuationwithin the water column, leading to acclimation of phytoplanktoncells to relatively low light levels with subsequent increase in thedegree of package effect within the entire phytoplankton population.

71J. Uitz et al. / Remote Sensing of Environment 171 (2015) 58–74

Another example is illustrated by the ANT-XXVI/4 station 299whichis misclassified by the HCA analysis of aph,n(λ) or a″ph,n(λ) spectrawhen compared to the pigment-based classification (Figs. 3 and 7).Although dominated by picophytoplankton, this station shows aph,n(λ)and a″ph,n(λ) spectra similar to those of stations from clusters MP andN respectively, i.e., characterized by larger cells. Because the values ofChl, POC, and SPM for station 299 are among the highest of the entireAtlantic transect, these data can be subject to low-light acclimationand large degree of package effect of phytoplankton cells. This result issupported by a high value of Chl/SI = 0.11 mg Chl m−3 μm−1, whichis 10-fold larger than typical values for stations from the cluster P (seeTable 1).

An interesting case of optical misclassification involves also station268 from the ANT-XXVI/4 cruise. Whereas this station belongs to thepigment-based cluster MP (Fig. 3), it is grouped with stations frompigment-based cluster N when the aph,n(λ) or a″ph,n(λ) spectra are

Fig. 10. As Fig. 9 but for the Rrs,n(λ) spectra.

used as input to the HCA (Fig. 7). Although station 268 shows dominantcontributions by the picophytoplankton and microphytoplankton clas-ses, the relatively large fnano likely influences the optical properties ofthe phytoplankton assemblage to the extent that aph,n(λ) spectrum be-comes similar to those of stations from pigment-based cluster N. Thissuggests that a given pigment (or group of pigments) may impose theoptical signatures onto the bulk phytoplankton absorption spectrumeven though the specific pigments are not dominant in terms of bio-mass. Another hypothesis that could explain themisclassification of sta-tion 268 based on the aph,n(λ) or a″ph,n(λ) spectra is that two differentgroups of dominant pigments may result in similar bulk phytoplanktonabsorption spectra.

4.4.2. EOF analysis of remote-sensing reflectanceThe results from the EOF analysis of the Rrs,n(λ) spectra are depicted

in Fig. 10. The first mode accounts for 96.9% of the variance of thedataset, and is characterized by values of opposite sign in the blue andgreen spectral regions. This result reflects typical changes in oceancolor, which in open oceans are expected to be controlled primarily bychanges in phytoplankton biomass and covarying materials. Typically,the reflectance in the blue decreases relative to reflectance in thegreen as absorption by phytoplankton and covarying materials in-creases. The results of the correlation analysis between the amplitudefactors of the first two EOF modes and the biogeochemical and bio-optical parameters are presented in Fig. 10b–c and Table 4. These resultsare qualitatively similar to those obtained from the EOF analysis of theaph,n(λ) dataset. In particular, PC1 is strongly correlatedwith the indicesof phytoplankton pigment composition and cell size, i.e., r ≈ −0.6 forfpico; r≈ 0.8 for fmicro and SI; r≈ 0.7 for Fuco, Zea, and Hex. PC1 exhibitsthe highest correlation with the proxy of intracellular pigment concen-tration Chl/SI (r≈ 0.9). Thus PC1 shows significant correlation with thesame factors that influence the spectral variability of aph,n(λ), which isconsistent with the notion that the spectral shape of aph,n(λ) hasmajor influence on the spectral shape of Rrs,n(λ) in open-ocean waters.Nevertheless, the spectral shape of Rrs,n(λ) is also known to be affectedby other factors, as supported by a strong correlation between PC1 andChl, POC, and SPM (r≈ 0.9), and a moderate but significant correlationbetween PC1 and aph(440)/ap(440) (r≈ 0.5). This suggests that, for ourdataset, the spectral shape of Rrs,n(λ) is more sensitive to the bulk parti-cle concentration than to the relative contribution of phytoplankton tothe total particle pool.

The second EOF mode of the Rrs,n(λ) spectra accounts for 3% of thevariance of the dataset (Fig. 10a). This mode appears partly governedby the same mechanisms as the first mode, but with an additional dis-tinctive process that produces a decrease with increasing wavelength

Table 4Values of the correlation coefficient between the amplitude factors of the first two EOFmodes (PC1 and PC2) of the Rrs,n(λ) and R″rs,n(λ) datasets and the selected biogeochemicaland bio-optical parameters shown in the first column. Statistically insignificant values ofthe correlation coefficient are in gray.

Rrs,n(λ) Rrs,n(λ)PC1 PC2 PC1 PC2

fpico –0.57 –0.03 0.61 –0.07fnano –0.15 –0.35 0.02 –0.44fmicro 0.78 0.35 –0.70 0.47Zea 0.68 –0.11 –0.65 0.07Hex 0.74 0.19 –0.69 0.29Fuco 0.69 0.44 –0.59 0.52SI 0.78 0.33 –0.72 0.45Chl / SI 0.93 0.15 –0.86 0.34PPC / Chl –0.73 0.04 0.75 –0.13aph(440)/aph(676) –0.76 0.03 0.77 –0.17aph(440)/ap(440) 0.52 –0.22 –0.58 –0.18Chl 0.88 0.37 –0.79 0.53POC 0.88 0.29 –0.81 0.44SPM 0.89 0.28 –0.84 0.42

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in the spectral domain b480 nm. PC2 shows moderate correlation withseveral parameters indicative of phytoplankton community composi-tion (fnano, fmicro, and SI) and concentration (Fuco and Chl) (Fig. 10cand Table 4). The third EOFmode accounts for only b0.1% of the datasetvariance.

The EOF analysis for the R″rs,n(λ) spectra indicated that the first threemodes explain 97% of the dataset variance (86%, 9%, 2%, respectively;data not shown). In contrast to a″ph,n(λ), the variance of the R″rs,n(λ) isdistributed among relatively small number of modes. The results ofthe analysis for R″rs,n(λ) are presented in Table 4. In brief, both PC1 andPC2 are strongly correlatedwithmost parameters, including the indica-tors of phytoplankton community composition, phytoplankton bio-mass, and bulk particle concentration.

The relationship between remote-sensing reflectance and phyto-plankton community composition is theoretically less direct and farmore complicated than that for phytoplankton absorption. Whereasthe spectral phytoplankton absorption coefficient is controlled by phy-toplankton pigment composition, cell size, and intracellular pigmentconcentrations, the spectral remote-sensing reflectance is also influ-enced by the absorption and scattering of phytoplankton and non-phytoplankton optically significant seawater constituents. The EOFanalysis applied to our Rrs,n(λ) dataset shows that most variability inthe spectral shape of Rrs,n(λ) is determined by phytoplankton biomassand covarying constituents, which in turn are driven by environmentalconditions and associated phytoplankton community composition(Fig. 10). Additionally, and in contrast to the analysis of the aph,n(λ)spectra, the second order variability of the spectral shape of Rrs,n(λ) ap-pears influenced by Chl and, in the case of R″rs,n(λ), also by POC and SPM(see the analysis of the second EOF mode of Rrs,n(λ) and R″rs,n(λ) inTable 4). The stations typical of contrasted ecological and opticalenvironments are well classified through the HCA applied to Rrs,n(λ)or R″rs,n(λ) (Fig. 8). This is the case of picophytoplankton-dominatedwaters with low chlorophyll content (e.g., BIOSOPE stations GYR2–5located in the core of the South Pacific Subtropical Gyre) ormicrophytoplankton-dominated waters associated with large phyto-plankton biomass (e.g., BIOSOPE stations UPW1–3 from the Chilean up-welling). The stations grouped together in a mixed cluster throughreflectance spectra (e.g., fourth cluster in Fig. 8a) show moderate tohigh Chl, POC, and SPM, and are geographically grouped in transitionareas (Figs. 1 and 2). Both stations St20 (pigment-based clusterN) and St21 (cluster P) from the BIOSOPE cruise are located betweenthe eastern border of the South Pacific Subtropical Gyre and theChilean upwelling. The ANT-XXVI/4 stations 266 (cluster N), 267–268(cluster MP), and 269 (cluster P) are located between the Patagonianshelf and the South Atlantic Subtropical Gyre. Hence, the classificationof these stations in a single cluster on the basis of Rrs,n(λ) or R″rs,n(λ)may reflect the bio-optical status of waters which shows less definitiverelation to the phytoplankton community composition and is likely in-termediate between the picophytoplankton- and microphytoplankton-dominated stations.

5. Conclusions

In the present study we applied a hierarchical cluster analysis to alarge dataset comprising field measurements of near-surface phyto-plankton pigments, hyperspectral phytoplankton absorption coeffi-cient, and hyperspectral remote-sensing reflectance of the ocean.Similar to previous studies (Taylor et al., 2011; Torrecilla et al., 2011;Organelli et al., 2013), our analysis indicates the potential of thehyperspectral optical data for discriminating phytoplankton popula-tions in terms of different groups, with slightly better results for thephytoplankton absorption coefficient than remote-sensing reflectance,and also slightly better results for the second derivative spectra thanthe ordinary spectra. These results are particularly encouraging becauseour dataset is representative of open ocean non-bloom environmentswithmixed phytoplankton populations and relatively low algal biomass

(Chl within the range 0.015–1.5 mgm−3), which represent highly chal-lenging conditions for the optical assessment of phytoplankton commu-nity composition.

The current challenge ofmonitoring ecosystem response to environ-mental and climate changes requires the widespread use of opticalmethods for assessing variations in phytoplankton diversity over ex-tended temporal and spatial scales. With continuing technological ad-vances and increase in the availability of hyperspectral optical datafrom optical sensors deployed on various types of in situ platformsand planned satellite missions, our results support a real prospect forassessing and monitoring changes in phytoplankton diversity withinthe world's oceans. In addition to providing a means to delineate dis-tinct phytoplankton groups, the hierarchical cluster analysis applied tohyperspectral optical datamay also serve as a diagnostic tool for identi-fying atypical associations of phytoplankton pigments, cell size, and bio-optical status. Such associations are of particular interest as they willchallenge the methods for assessing the composition of phytoplanktonassemblages, whether abundance- or optical-based, multispectral orhyperspectral. Advancing an understanding of these associations andthe conditions under which they prevail deserves special attention infuture studies in order to improve the performance of the methodsand to assess the uncertainties in the indiscriminate use of themethodsunder such conditions.

In this study an empirical orthogonal function analysis was also ap-plied to the hyperspectral optical data. This analysis provided insightsinto the sources of variability in the spectral shape of optical data andthe roles of factors that may help or hinder the performance of thehyperspectral approach to assessing phytoplankton diversity. Interest-ingly, our analysis suggests that the potential of the hyperspectral ap-proach may not be directly related to the capability to extract theoptical signatures of specific diagnostic pigments from the ordinary orderivative spectra of phytoplankton absorption or remote-sensing re-flectance. Instead this potential appears to result primarily from chang-es in phytoplankton community structure, which are accompanied bycovarying influences of package effect and pigment composition onthe spectral shape of phytoplankton absorption coefficient and alsothe influences of algal biomass or trophic status in the case of remote-sensing reflectance.

Acknowledgements

This research was supported by the NASA Biodiversity Program(Grant NNX09AK17G) and the remOcean (“remotely-sensed biogeo-chemical cycles in theOcean”) project funded by the European ResearchCouncil (Grant 246777).We are grateful to Astrid Bracher and her teamfrom the Alfred-Wegener Institute for Polar and Marine Research forkindly inviting us to participate in the ANT-XXVI/4 cruise, and to PierreGernez for his assistance in the fieldwork. We thank the team from theBIOSOPE cruise, including the Chief Scientist Hervé Claustre, JoséphineRas for pigment analysis, Annick Bricaud for providing absorptiondata, andMarlon Lewis for providing reflectance data. The scientists, of-ficers, and crews of R/V L'Atalante and R/V Polarstern are acknowledgedfor providing logistical support and assistance during the fieldwork.Wethank Hervé Claustre and Annick Bricaud for valuable discussions. Wealso thank Elena Torrecilla and two anonymous reviewers for providingconstructive comments on the manuscript.

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