influence of phytoplankton pigment composition on remote ... · without substantial cyanobacterial...

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Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass Stefan G.H. Simis a, , Antonio Ruiz-Verdú b , Jose Antonio Domínguez-Gómez b , Ramón Peña-Martinez b , Steef W.M. Peters c , Herman J. Gons a a Netherlands Institute of Ecology (NIOO-KNAW), Centre for Limnology, Rijksstraatweg 6, 3631 AC Nieuwersluis, The Netherlands b Centre for Hydrographic Studies (CEDEX), Paseo Bajo de la Virgen del Puerto 3, E-28005, Madrid, Spain c Institute for Environmental Studies (IVM), Vrije Universiteit, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands Received 27 March 2006; received in revised form 29 July 2006; accepted 9 September 2006 Abstract An extensive field campaign was carried out for the validation of a previously published reflectance ratio-based algorithm for quantification of the cyanobacterial pigment phycocyanin (PC). The algorithm uses band settings of the Medium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, and should accurately retrieve the PC concentration in turbid, cyanobacteria-dominated waters. As algae and cyanobacteria often co-occur, the algorithm response to varying phytoplankton composition was explored. Remote sensing reflectance and reference pigment measurements were obtained in the period 20012005 in Spain and the Netherlands using field spectroradiometry and various pigment extraction methods. Additional field data was collected in Spain in May 2005 to allow intercalibration of spectroradiometry and pigment assessment methods. Two methods for extraction of PC from concentrated water samples, and in situ measured PC fluorescence, compared well. Reflectance measurements with different field spectroradiometers used in Spain and the Netherlands also gave similar results. Residual analysis of PC predicted by the algorithm showed that overestimation of PC mainly occurred in the presence of chlorophylls b and c, and phaeophytin. The errors were strongest at low PC relative to Chl a concentrations. A correction applied for absorption by Chl b markedly improved the prediction. Without such a correction, the quality of the PC prediction still increased markedly with estimates N 50 mg PC m 3 , allowing monitoring of the cyanobacterial status of eutrophic waters. The threshold concentration may be lowered when a high intracellular PC:Chl a ratio or cyanobacterial dominance is expected. Below the limit, predicted PC concentrations should be considered as the highest estimate. We evaluated that remote sensing of both PC and Chl a would allow assessment of cyanobacterial risk to water quality and public health in over 70% of our cases. © 2006 Elsevier Inc. All rights reserved. Keywords: Remote sensing; Phytoplankton; Cyanobacteria; Phycocyanin; Chlorophyll; MERIS 1. Introduction The management of inland water quality is a growing concern wherever anthropogenic eutrophication affects water bodies. Potentially toxic cyanobacterial blooms pose a health threat and are detrimental to the economic and environmental value of many lakes and reservoirs. Regular monitoring of water bodies is necessary to provide timely warnings in case of cyanobacterial bloom. Remotely sensed water quality products have the potential to provide high spatial and temporal coverage, while conventional sampling methods lack both. Detection of cyanobacterial biomass can be based on the absorption feature of the pigment phycocyanin (PC), used for light harvesting and only present in considerable concentrations in cyanobacteria. The absorption signal of most modifications of cyanobacterial PC is strongest around 615 nm (Bryant, 1981) and can be detected from reflectance data of eutrophic, turbid water bodies (Dekker et al., 1991; Gons et al., 1992; Jupp et al., 1994; Kutser et al., 2006). A number of reflectance spectra, collected for this study, from which the PC absorption feature can be observed have been plotted in Fig. 1. This figure also shows the location of wavebands used in pigment retrieval Remote Sensing of Environment 106 (2007) 414 427 www.elsevier.com/locate/rse Corresponding author. Tel.: +31 294 239327; fax: +31 294 232224. E-mail address: [email protected] (S.G.H. Simis). 0034-4257/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2006.09.008

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Page 1: Influence of phytoplankton pigment composition on remote ... · without substantial cyanobacterial growth were also included. Many of the lakes are subject to eutrophication, while

t 106 (2007) 414–427www.elsevier.com/locate/rse

Remote Sensing of Environmen

Influence of phytoplankton pigment composition onremote sensing of cyanobacterial biomass

Stefan G.H. Simis a,⁎, Antonio Ruiz-Verdú b, Jose Antonio Domínguez-Gómez b,Ramón Peña-Martinez b, Steef W.M. Peters c, Herman J. Gons a

a Netherlands Institute of Ecology (NIOO-KNAW), Centre for Limnology, Rijksstraatweg 6, 3631 AC Nieuwersluis, The Netherlandsb Centre for Hydrographic Studies (CEDEX), Paseo Bajo de la Virgen del Puerto 3, E-28005, Madrid, Spain

c Institute for Environmental Studies (IVM), Vrije Universiteit, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands

Received 27 March 2006; received in revised form 29 July 2006; accepted 9 September 2006

Abstract

An extensive field campaign was carried out for the validation of a previously published reflectance ratio-based algorithm for quantification ofthe cyanobacterial pigment phycocyanin (PC). The algorithm uses band settings of the Medium Resolution Imaging Spectrometer (MERIS)onboard ENVISAT, and should accurately retrieve the PC concentration in turbid, cyanobacteria-dominated waters. As algae and cyanobacteriaoften co-occur, the algorithm response to varying phytoplankton composition was explored. Remote sensing reflectance and reference pigmentmeasurements were obtained in the period 2001–2005 in Spain and the Netherlands using field spectroradiometry and various pigment extractionmethods. Additional field data was collected in Spain in May 2005 to allow intercalibration of spectroradiometry and pigment assessmentmethods. Two methods for extraction of PC from concentrated water samples, and in situ measured PC fluorescence, compared well. Reflectancemeasurements with different field spectroradiometers used in Spain and the Netherlands also gave similar results. Residual analysis of PCpredicted by the algorithm showed that overestimation of PC mainly occurred in the presence of chlorophylls b and c, and phaeophytin. The errorswere strongest at low PC relative to Chl a concentrations. A correction applied for absorption by Chl b markedly improved the prediction.Without such a correction, the quality of the PC prediction still increased markedly with estimates N50 mg PC m−3, allowing monitoring of thecyanobacterial status of eutrophic waters. The threshold concentration may be lowered when a high intracellular PC:Chl a ratio or cyanobacterialdominance is expected. Below the limit, predicted PC concentrations should be considered as the highest estimate. We evaluated that remotesensing of both PC and Chl a would allow assessment of cyanobacterial risk to water quality and public health in over 70% of our cases.© 2006 Elsevier Inc. All rights reserved.

Keywords: Remote sensing; Phytoplankton; Cyanobacteria; Phycocyanin; Chlorophyll; MERIS

1. Introduction

The management of inland water quality is a growingconcern wherever anthropogenic eutrophication affects waterbodies. Potentially toxic cyanobacterial blooms pose a healththreat and are detrimental to the economic and environmentalvalue of many lakes and reservoirs. Regular monitoring of waterbodies is necessary to provide timely warnings in case ofcyanobacterial bloom. Remotely sensed water quality products

⁎ Corresponding author. Tel.: +31 294 239327; fax: +31 294 232224.E-mail address: [email protected] (S.G.H. Simis).

0034-4257/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.rse.2006.09.008

have the potential to provide high spatial and temporalcoverage, while conventional sampling methods lack both.

Detection of cyanobacterial biomass can be based on theabsorption feature of the pigment phycocyanin (PC), used forlight harvesting and only present in considerable concentrationsin cyanobacteria. The absorption signal of most modificationsof cyanobacterial PC is strongest around 615 nm (Bryant, 1981)and can be detected from reflectance data of eutrophic, turbidwater bodies (Dekker et al., 1991; Gons et al., 1992; Jupp et al.,1994; Kutser et al., 2006). A number of reflectance spectra,collected for this study, from which the PC absorption featurecan be observed have been plotted in Fig. 1. This figure alsoshows the location of wavebands used in pigment retrieval

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Fig. 1. Rrs(k) spectra representing the variation in spectral shape andmagnitude ofreflectance data encountered in the dataset.MERIS bands used to obtain PCRAD areindicated by vertical bars (centered at 620, 665, 708.75 and 778.75 nm). The trougharound 625 nm is primarily caused by pigments PC and Chl a. The trough around675 nm is attributed to Chl a. Spectra from L. 1' Albufera (Spain) correspondedwith pigment measurements PCFL=120–728 and Chl a 29–439 mgm−3. PCFL andChl ameasurements for the Rosarito spectra displayed here were both in the 35–80mg m−3 range. PCFT and Chl a measurements of the displayed L. IJsselmeersamples ranged 20–329 and 26–109 mg m−3, respectively.

415S.G.H. Simis et al. / Remote Sensing of Environment 106 (2007) 414–427

algorithms described below. The first attempts at quantifyingPC from spectral reflectance were semi-empirical baseline(Dekker, 1993) and band ratio (Schalles & Yacobi, 2000)algorithms, targeting the absorption feature caused by thepresence of PC and chlorophyll a (Chl a) in the 620–625 nmregion. Variable PC:Chl a ratios are not accounted for by thesealgorithms, and current satellite sensors with global coverage donot offer all required wavebands.

The Medium Resolution Imaging Spectrometer (MERIS)onboard the ENVISAT mission is the first sensor to offer acombination of several narrow wavebands to target both Chl aand accessory pigment absorption in the red spectral region, at aspatial resolution (260 m across track) sufficient for medium-sized water bodies, and with a satisfactory signal-to-noise ratio.Algorithms based on ratios of reflectance in the red and near-infrared (NIR) spectral region can be used to retrievephotosynthetic pigment concentrations in turbid inland waterbodies, where often the most severe water quality problems areexperienced.

For optical remote sensing of the concentration of the mainphotosynthetic pigment chlorophyll a, the ratio of a NIR band(around 700–710 nm) over a band near the red absorptionmaximum of Chl a at 675 nm (Mittenzwey et al., 1992) has beensuccessfully applied to a wide range of turbid water bodies forN10mg Chl am−3 (Gons, 1999; Gons et al., 2000). The band ratiotargeting Chl a absorption thus serves as an indicator for phyto-plankton biomass (algae and cyanobacteria) in productive waterbodies, and can be used with MERIS imagery (Gons et al., 2005).

Recently, a semi-analytical, nested band ratio-based algo-rithm for the retrieval of PC was proposed, using only MERIS

bands (Simis et al., 2005; refer to Eqs. (5A) and (5B) below).This PC algorithm attributed the absorption signal derived fromthe 620-nm band to two absorption features that are dominant incyanobacteria-rich waters: the absorption peak of PC and theshoulder to red Chl a absorption located around 623–628 nm(Bidigare et al., 1990; Ficek et al., 2004; Sathyendranath et al.,1987). The absorption by Chl a in this waveband was obtainedfrom a nested band ratio of bands at 708.75 and 665 nm,subsequently using a fixed conversion factor describing the invivo absorption of Chl a between 620 and 665 nm. In this way,the absorption at 620 nm could be corrected for Chl a and theremaining absorption was attributed to PC. Absorption bydissolved substances, suspended sediments, and detrital mattercould contribute a nearly flat background absorption to the620 nm band. Spectrally neutral absorption by these substancesis not expected to be a major influence on the retrieval ofabsorption through a band ratio-based algorithm, as long as theused bands are located sufficiently close together, and thereflectance spectrum has a high amplitude. However, phyto-plankton pigments that absorb light in the red bands but not inthe NIR band are prone to have an influence on the estimation ofabsorption at 620 nm, and inherently, on the estimation of PC.This effect should be particularly clear at relatively low PCconcentrations.

All chlorophyllous pigments and their degradation productsabsorb light in the red spectral region. The discussed PC retrievalalgorithm currently only corrects for absorption by Chl a. Formost pigments, absorption around 620 nm only amounts to afraction of their absorption at longer wavelengths. However, thediatom pigments chlorophyll c1 and c2 [Chl c] have absorptionmaxima on both sides of the 620-nm band (Ficek et al., 2004;Jeffrey et al., 1997). Chl c is therefore likely to lead tooverestimations of the PC concentration as it is not corrected forby the algorithm. The pigment chlorophyll b [Chl b] is anothermajor accessory photosynthetic pigment in inland water bodies,mainly present in green algae and prochlorophytes. Chl b has abroad absorption maximum around 650 nm and a minor featurearound 600 nm (Ficek et al., 2004; Jeffrey et al., 1997;Sathyendranath et al., 1987) and could lead to overestimates ofthe absorption in both the 665-nm and 620-nm wavebands.Degraded chlorophyll formsmay also contribute to absorption inthese bands. During the development of the PC algorithm,overestimations of the PC concentration were indeed observedmainly at mixed-phytoplankton sites, but could not be attributedto any particular phytoplankton composition due to lack ofsufficient data (Simis et al., 2005).

This paper reports on the influence of the presence of majorphytoplankton groups on the retrieval of PC by the mentionedreflectance band ratio-based algorithm. Field spectroradiometricand pigment data were gathered from a range of water bodies inSpain and the Netherlands with a varying share of PC-containingcyanobacteria in the phytoplankton. Errors in PC retrieval wereregressed against the presence of marker pigments for differentalgal groups. The aim of the study was to identify situationswhere the PC algorithm for MERIS bands can be applied, andsituations where additional information is needed because ofinterference by red-absorbing algal pigments. The results may be

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used in the spectral design of future sensors, to define theboundary conditions for operational monitoring of cyanobac-teria using remote sensing, and to aid the development ofimproved or novel algorithms from (hyper)spectral reflectancedata.

2. Method

2.1. Study sites

An overview of sampling sites and the instrumentation thatwas used at each site is given in Table 1. The Spanish waterbodies were visited in the period 2001–2005 and represent arange of 57 lakes and reservoirs from all parts of the country. Thewater bodies ranged widely in trophic state (observed Chl a range0.5–305 mg m−3), surface area (b1–103 km2), depth (0.9–104 m, measured at the sampling location), and transparency(secchi disk depth 0.2–9.6 m). Many of the deeper lakes werevertically stratified, so care was taken to take samples that wererepresentative of the layer up to the first optical depth, themaximum penetration depth of 90% of the remotely sensed light(Gordon & McCluney, 1975). In turbid lakes (secchi disk depthb1 m), samples were taken from the surface layer. Monospecificblooms of algae or cyanobacteria are common in several of thewater bodies from spring onward, but several deep and clear lakeswithout substantial cyanobacterial growth were also included.Many of the lakes are subject to eutrophication, while serving afunction of drinking water supply and irrigation resource.

In the Netherlands, Lake IJsselmeer and Lake Ketelmeerwere visited 6 times for 2-day cruises in 2004 and 2005. Thisturbid, eutrophic lake system is the largest (1190 km2)freshwater body of Western Europe and exhibits a seasonalphytoplankton distribution where cyanobacteria are moreabundant towards the end of summer, but substantial biomasscan be present in diatoms, green algae, prochlorophytes, andcryptophytes. Cyanobacteria, mainly species of AphanizomenonandMicrocystis can build up bloom patches at the water surfaceof L. IJsselmeer during calm summer days, but the shallow lake(average depth 4.4 m) is usually well mixed by wind whichcauses resuspension of settled organic material and sedimentsand low transparency (average secchi disk depth 1.0 m). It iscommon to find large horizontal differences in phytoplanktonpigment composition. The smaller L. Ketelmeer serves as theinput of nutrient-rich river water to L. IJsselmeer and ischaracterized by high suspended sediment loading and thepresence of diatom species.

2.2. Radiometric measurements

Remote sensing reflectance Rrs(λ) was defined here as theMERIS level-2 standard product ‘normalized water-leavingreflectance’ (Montagner, 2001):

RrsðkÞ ¼ ½qw�N ð0þ; kÞ ¼ kLwð0þ; kÞ=Edð0þ; kÞ ð1Þ

where Lw(0+,λ) is water-leaving radiance corrected for diffuse

sky light reflected at the water surface, Ed(0+,λ) is downward

irradiance, depth 0+ points to the situation just above the watersurface and wavelength dependence is denoted by λ. Thesequantities can all be obtained from shipboard measurements. Inthat case, Lw(0

+,λ) is calculated from

Lwð0þ; h;u; kÞ ¼ Lwuð0þ; h;u; kÞ−½rðhÞLskyð0þ; h;u; kÞ� ð2Þ

where Lwu(0+,θ,φ,λ) is water-leaving radiance uncorrected for

reflectance of downwelling light at the water surface; thiscorrection is given by the bracketed term. Sky radiance isdenoted Lsky(0

+,θ,φ,λ) and measured at an angle θ away fromthe zenith axis. For small instrument acceptance angle there isnegligible dependence of Lwu on viewing angle θ (the sameangle from zenith as for Lsky, but mirrored on the horizontalplane) and azimuth angle φ (away from the sun's azimuth), aslong as θb42°, and for φ ranging 90°–135° (Morel & Gentili,1993; Tyler, 1960). The measurement geometry was kept withinthese limits and sun zenith angles N60° were avoided in all cases.The skylight correction factor r(θ) can be approached by aconstant value for calm water surfaces, or obtained as a functionof wind speed and cloud cover (Mobley, 1999). The irradianceEd(0

+,λ) can be acquiredwith a cosine collector, or bymeasuringthe radiance Ld from an intercalibrated spectrally neutral diffuseplate. Different field spectroradiometers were used to acquire theSpanish and Dutch datasets, and each differed in the way theradiance quantities in Eqs. (1) and (2) were measured. Details onthe different optical configurations, instrument characteristicsand measurement parameters can be found in Table 2. For bothmeasurement methods, a minimum of 3 Rrs spectra wasproduced. After removal of invalid Rrs spectra due to the captureof sun glint or variable cloud cover during the measurementcycle, remaining spectra were averaged.

2.3. Pigment analysis

Samples for pigment analysis were always taken from thesurface water layer in shallow, turbid lakes, and from the firstoptical depth layer (Gordon & McCluney, 1975) in verticallystratified likes. For phycobilin pigments there is no standardmethod of extraction. Two different methods were used for celldisruption and extraction of PC and phycoerythrin (PE), andconcurrent measurements are available in the current dataset fortheir intercalibration. In addition, for samples taken and analysedin Spain, PC fluorescence was measured in situ, providing athird PC reference to the spectroradiometric approach.

The first method for phycobilin extraction was based onrepeated freezing and thawing cycles of samples. PC concentra-tions resulting from this method will be referred to as PCFT. Theprocedure was based on the work by Sarada et al. (1999) and wasadapted for the current study from Simis et al. (2005). In short:fresh water samples stored at 0 °C for b48 h were concentratedby high-speed centrifugation, suspended in a phosphate buffer ofpH 6.7, and frozen and thawed nine times at −20 °C and roomtemperature, respectively, while kept in the dark. Note that thephosphate buffer used here was of pH 6.7 rather than pH 7.4 asused before (Simis et al., 2005), which resulted in up to 28%higher extraction yield from cultured Limnothrix sp. and

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Table 1Summary of sampled lakes and reservoirs

Location name Position Depth Surface area Elevation Chl a Secchi depth Visitsa Samples Spectroradiometry samples Pigment samples

Lat. Lon. m (avg.) km2 m a.s.l. mg m−3

(avg.)m (avg.) Total PR-650 ASD-FR HPLC PC

d.dd d.dd FT MG FL

The Netherlands 6 290 289 0 203 201 75 0L. IJsselmeer 52.73 5.39 4.4 1190 0 43.9 0.8 6 256 256 0 187 186 68 0L. Ketelmeerb 52.6 5.72 3.0 35 0 12.5 1.0 6 34 33 0 16 15 7 0

Spain 89 193 23 170 169 16 122 168Aguilar R. 42.81 −4.31 14.1 18 942 6.6 1.4 2 4 0 4 4 0 4 4Alarcón R. 39.6 −2.16 17.4 69 806 3.5 2.4 2 4 0 4 4 0 4 4L. l'Albufera 39.34 −0.35 0.9 24 1 304.9 0.2 4 20 7 19 16 0 14 15Alcántara R. 39.73 −6.61 47.0 103 218 4.4 3.5 1 2 0 2 2 0 2 2Alcorlo R. 41.02 −3.03 27.0 7 920 1.6 2.1 1 1 0 1 1 0 1 1Almendra R. 41.24 −6.28 71.3 79 730 13.2 2.7 2 3 0 3 3 0 3 3Los Arroyos R. 40.59 −4.05 n/a b1 845 24.8 0.0 1 1 1 0 1 1 1 0El Atazar R. 40.91 −3.49 49.5 10 870 4.5 7.2 3 4 0 4 4 0 4 3Beniarrés R. 38.81 −0.36 14.0 2 318 57.0 1.0 1 1 0 1 1 0 1 1Bornos R. 36.82 −5.72 13.1 22 104 6.2 0.9 1 2 0 2 2 0 2 2Brovales R. 38.35 −6.7 n/a 1 303 53.8 0.6 1 1 1 0 1 0 1 1Buendía R. 40.4 −2.77 40.0 80 712 0.5 9.6 1 1 0 1 1 0 1 1Burguillo R. 40.43 −4.57 30.5 9 729 18.9 2.1 2 4 0 4 4 0 3 4El Campillo Lgn. 40.32 −3.50 n/a b1 538 37.2 0.7 1 2 0 0 2 2 2 0Canelles R. 42.01 0.64 104.0 16 506 1.0 6.0 1 4 0 4 4 0 0 4Castrejón R. 39.83 −4.3 n/a 5 425 144.8 0.5 1 1 0 0 1 1 1 0Castro R. 39.81 −3.75 n/a b1 559 48.2 0.3 1 1 0 0 1 1 1 0Cernadilla R. 42.02 −6.47 38.0 13 889 3.1 3.3 1 1 0 1 1 0 1 1Cijara R. 39.34 −4.93 28.5 74 428 1.4 5.2 1 2 0 2 2 0 2 2Contreras R. 39.61 −1.53 27.0 27 669 1.6 2.6 1 2 0 2 2 0 2 2Cortes R. 39.25 −0.93 15.0 4 326 1.5 4.0 1 1 0 1 1 0 1 1Ebro R. 42.98 −4.04 4.9 62 838 52.8 0.9 2 3 0 3 2 0 3 3Entrepeñas R. 40.51 −2.73 25.0 34 718 2.1 4.5 1 1 0 1 1 0 1 1Finisterre R. 39.65 −3.65 n/a 5 686 14.7 0.7 1 1 0 0 1 1 1 0Giribaile R. 38.09 −3.49 26.0 23 346 2.9 2.3 1 1 0 1 1 0 1 1

(continued on next page)

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Table 1 (continued)

Location name Position Depth Surface area Elevation Chl a Secchi depth Visitsa Samples Spectroradiometry samples Pigment samples

Lat. Lon. m (avg.) km2 m a.s.l. mg m−3

(avg.)m (avg.) Total PR-650 ASD-FR HPLC PC

d.dd d.dd FT MG FL

Guadalcacín R. 36.66 −5.73 29.5 11 102 1.0 3.6 1 2 0 2 2 0 2 2Guadalén R. 38.17 −3.47 22.0 11 350 2.5 1.0 1 1 0 1 1 0 1 1Guadalteba R. 36.96 −4.83 21.0 8 362 106.5 1.3 1 2 0 2 2 0 2 2Guajaraz R. 39.8 −4.09 n/a 2 605 12.0 0.7 1 1 1 0 1 1 1 0Iznájar R. 37.27 −4.34 44.3 25 421 32.2 2.5 2 4 0 4 3 0 4 4Jándula R. 38.24 −3.95 56.0 10 360 2.9 5.5 1 2 0 2 2 0 2 2Molino de la Hoz R. 40.53 −3.94 n/a b1 632 53.6 0.0 1 1 1 0 1 1 1 0Navalcán R. 40.04 −5.12 n/a 9 370 53.0 0.6 1 3 3 3 3 0 3 3Negratín R. 37.57 −2.9 28.5 25 638 1.1 4.4 1 2 0 2 2 0 2 2Pedrezuela R. 40.76 −3.62 n/a 4 828 21.6 0.0 1 1 1 0 1 1 1 0Picadas R. 40.33 −4.25 n/a b1 1040 9.0 2.8 1 1 1 0 1 1 1 0Pinilla R. 40.94 −3.73 13.1 4 1089 24.3 1.6 3 4 0 4 4 0 4 4El Porcal-1 Lgn. 40.30 −3.53 n/a 1 535 38.4 0.6 1 1 0 0 1 1 1 0El Porcal-2 Lgn. 40.30 −3.52 n/a b1 534 39.7 0.6 1 1 0 0 1 1 1 0El Porcal-3 Lgn. 40.31 −3.52 n/a b1 531 n/a 0.5 1 1 0 0 0 1 0 0Rialb R. 41.96 1.22 14.2 18 430 8.7 2.0 1 4 0 4 4 0 0 4Riaño R. 42.96 −5.05 31.5 21 1100 10.6 3.4 1 2 0 2 2 0 2 2Ricobayo R. 41.61 −5.94 32.9 54 684 5.9 3.2 2 4 0 4 4 0 4 4Rosarito R. 40.1 −5.3 7.9 13 307 61.9 0.7 12 49 2 48 35 0 2 44San Juan R. 40.38 −4.33 43.5 6 580 13.1 5.0 1 2 0 2 2 0 2 2L. Sanabria 42.12 −6.72 47.0 4 998 1.3 5.4 1 2 0 2 2 0 2 2Santa Teresa R. 40.61 −5.61 34.0 24 886 3.5 3.0 1 2 0 2 2 0 2 2Santillana R. 40.72 −3.84 11.0 11 894 41.3 1.0 2 4 0 3 4 1 4 3La Serena R. 38.9 −5.19 31.8 40 352 2.9 4.3 2 4 0 4 4 0 4 4Terradets R. 42.07 0.89 5.4 3 372 0.9 0.6 1 2 0 2 1 0 0 1Tremp R. 42.21 0.95 22.3 8 501 2.4 4.6 1 4 0 3 4 0 0 4Ullívarri R. 42.93 −2.59 13.7 15 547 3.3 4.0 1 2 0 2 2 0 2 2Valdecañas R. 39.8 −5.5 29.6 62 315 15.8 2.7 3 5 0 4 4 0 4 5Valmayor R. 40.55 −4.05 24.5 7 831 28.8 1.7 3 6 2 4 6 2 6 4Valparaíso R. 41.98 −6.29 25.0 11 833 5.0 3.0 2 3 0 3 2 0 2 3Valuengo R. 38.31 −6.66 n/a 2 297 37.3 0.6 1 2 2 0 2 0 2 2Vega de Jabalón R. 38.76 −3.79 n/a 3 639 41.0 0.7 1 1 1 1 1 0 1 1

Depth refers to echo soundings at sampling stations. Symbols and abbreviations: n/a— no data; avg.— average; m.a.s.l.— metres above sea level; lat.— latitude; lon.— longitude; FT— freeze/thaw cycles method;MG — mechanical grinding method; FL — in situ fluorescence; R. — reservoir; L. — Lake; Lgn. — Lagoon.aVisits spanning 2 days counted as 1. bIncludes samples of lower River IJssel.

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Fig. 2. Comparison of remote-sensing reflectance [Rrs(MERIS channel, bandcenter, band with)] derived from the instrumentations used in Spain (ASD-FR) andThe Netherlands (PR-650). Only reflectances for the bands used in Eqs. 3 and 5 areplotted. Themeasurementswere carried out on a number of Spanishwater bodies inMay 2005. Regression results are shown by the solid line and dashed 95%confidence limits.

Table 2Details on field spectroradiometric configurations used to collect Rrs spectra inSpain and The Netherlands

Dataset The Netherlands Spain

Instrument PR-650 ASD-FRManufacturer Photo Research, Inc.

Chatsworth, CA, USAAnalytical Spectral Devices, Inc.Boulder, CO, USA

Light collector Lens Lens on glass fibre (Ø 0.17 cm)Acceptance angle 1° 8°Spectral interval 4 nm 1.4 nmFull-width half-

maximum8 nm 3 nm

Spectral range 380–780 nm 350–1000 nmViewing angle θ 42° 40°Azimuth angle φ 90° 135°Skylight correction

factor r(θ)0.029 Function of wind speed,

cloud cover (Mobley, 1999)Reflectance panel

(for Ed)100% (white)Spectralon

25% (gray) Spectralon

Exposure time Dynamic ManualSensor saturation

prevention95% Neutral densityfilter

Shorten exposure time

Exposures permeasurement

10 20

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accordingly a specific absorption coefficient for PC in the 620-nm band [aPC⁎ (620)] that was lowered to 0.007 m2 mg−1.

The second method for phycobilin pigment extraction wasbased on mechanical grinding (PCMG) of samples concentratedon glass fibre filters (Whatman GF/F) and resuspended inglycerol (Quesada & Vincent, 1993; Wyman & Fay, 1986a,b).After collection, filters were frozen in liquid nitrogen and laterstored at −80 °C. The filters were thawed, cut into small pieces,and one volume of glycerol was added to the filter remainsplaced in a centrifuge tube. A close-fitting Teflon pestleconnected to an electric drill was used to homogenize thesample in the tube at 500–1000 rpm while avoiding the sampleto heat up. The samples were kept in the dark for approximately2 h after which 9 volumes of distilled water were added to induceosmotic shock. The samples were briefly subjected to anotherround of homogenization in the pestle-tube combination.

After extraction with either the PCFT or PCMG method, thephycobilin pigment concentrations were computed from theabsorption spectra of supernatants of centrifuged samples,according to the equations published in Bennett and Bogorad(1973). The PCMG extraction was repeated on the pelletedmaterial and the results summed to give the final concentration,after correction for the initially filtered sample volume.

Finally, the in situ PC quantification by fluorescence (PCFL)was performed using a Minitracka II PA Fluorometer ModelHB202 (Chelsea Instruments Ltd., Surrey, UK) with excitationcentred at 590 nm (35 nm band width) and recorded emissionaround 645 nm (35 nm band width). The instrument wasfactory-calibrated in the 0.03–100 mg m−3 range using PC(Sigma Chemicals) dissolved in pH 7 phosphate buffer.

2.4. HPLC pigments

Pigments that could be extracted with organic solvents wereanalysed using gradient HPLC based on the protocols in Jeffrey

et al. (1997). Samples collected and analysed in the Netherlandswere concentrated on Schleicher and Schuell GF6 filters, frozenin liquid nitrogen and stored in a −80 °C freezer. Subsequentlyfilters and cells were disrupted in a bead beater, adding 90%acetone as a solvent. After centrifugation, the pigment content ofthe supernatant was separated using a reversed-phase column(Waters Nova-pak C18 column; Waters Millennium HPLCsystem), to which gradient mixing pumps delivered threemobile-phase solvents: methanol/ammonium acetate, 90%acetonitril and 100% ethyl acetate (Rijstenbil, 2003 andreferences therein). Pigments were identified against commer-cially available standards (DHI Water and Environment,Hørsholm, Denmark) using a fluorescence detector (Waters474 Scanning Fluorescence Detector) and a photodiode arrayabsorption detector (Waters 996 Photodiode Array Detector).The Spanish samples were treated in a similar way, except thatsamples were concentrated onWhatman GF/F filters and kept inliquid nitrogen until analysis, cell disruption and pigmentextraction was achieved by sonication followed by 24-h extraction at 4 °C, and the column was equipped with aWaters Spherisorb ODS-2 and HP Agilent 1050 diode arraysystem. The Spanish samples were spiked with a knownconcentration of canthaxanthin to calibrate the instrumentresponse. The concentrations of the following pigments wereacquired for all samples in both countries: Chl a, Chl b,phaeophytin, peridinin, neoxanthin, violaxanthin, alloxanthin,lutein, zeaxanthin, and fucoxanthin.

2.5. PC retrieval from radiometry

Rrs(λ) data at either 1 or 4-nm intervals (from ASD-FR andPR-650, respectively) were transformed to MERIS band

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Fig. 4. Comparison of PC values derived from the instrumentations used inSpain (ASD-FR) and The Netherlands (PR-650). The measurements werecarried out on a number of Spanish water bodies in May 2005. The boxed areaexcludes points from L. l'Albufera where cyanobacterial biomass wasexceptionally high. Solid regression line and dashed 95% confidence limitsrepresent the regression results through all plotted points.

420 S.G.H. Simis et al. / Remote Sensing of Environment 106 (2007) 414–427

resolution. Current MERIS band settings are, denoted Rrs

(MERIS channel, midpoint, band width): Rrs(6, 620, 10), Rrs(7,665, 10), Rrs(9, 708.75, 10), and Rrs(12, 778.75, 15). Weightedaveraging (weight depending on the overlap with the MERISband) was used to derive the bands from the 4-nm resolutiondata. Band Rrs(778.75) derived from PR-650 data was based onthe 771.25–780 nm range as 780 nm was the uppermeasurement limit of the instrument. For 1-nm data thewavelength ranges, rounded to the nearest nm, were averaged.

Calculation of the backscattering coefficient [bb] from Rrs

(778.75) followed Gons et al. (2005):

bbð778:75Þ ¼ 1:61Rrsð778:75Þ= f0:082−½0:6Rrsð778:75Þ�g ð3ÞThe PC algorithm from Simis et al. (2005) that is given below

assumes that bb is spectrally neutral over the used wavelengthrange, that absorption in the 665-nm band can be attributed toChl a and water [aw(λ)], and absorption in the 620-nm band towater, Chl a, and PC.

Absorption by phytoplankton pigments [aph(λ)] is obtainedfrom the ratio of two reflectance bands, in fact a ratio of theGordon reflectance model (Gordon et al., 1975) for both bands,which results for bands λ=620 or 665 nm in:

aphðkÞ ¼ pðkÞ−1 � ðf½Rrsð709Þ=RrsðkÞ��½awð709Þ þ bb�g−awðkÞ−bbÞ ð4Þ

where p(λ) is an empirical factor to compensate for a weakerabsorption signal retrieved from radiometric data compared toabsorption measurements with a spectrophotometer, as wasfound during the development of the PC algorithm (Simis et al.,2005). Substitution of aw(620)=0.281, aw(665)=0.401, andaw(708.75)=0.727 from Buiteveld et al. (1994), and aPC⁎ (620)=0.007 m2 mg−1, p(620)=0.84, and p(665)=0.68 from Simis

Fig. 3. Comparison of backscattering values derived from the instrumentationsused in Spain (ASD-FR) and The Netherlands (PR-650). Concurrentmeasurements were carried out on selected Spanish water bodies in May2005. The boxed area excludes points from L. l'Albufera where cyanobacterialbiomass was exceptionally high. The solid regression line and dashed 95%confidence limits represent the regression results through all plotted points.

et al. (2005) gives the PC algorithm that is validated in thispaper:

aphð665Þ ¼ 1:47� ðf½Rrsð709Þ=Rrsð665Þ��½0:727þ bb�g−0:401−bbÞ

ð5aÞ

PCRAD ¼ 170� ððf½Rrsð709Þ=Rrsð620Þ��½0:727þ bb�g−bb−0:281Þ−½e� aphð665Þ�Þ

ð5bÞwhere ε is used to derive absorption by Chl a at 620 nm from thepigment absorption at 665 nm, which is assumably dominated

Table 3Results for linear regression between PC reference measurement methods,between PCRAD and PC reference methods, and between PCRAD and PCREF, theaverage of reference PC measurements

Figure y-method x-method Country n a b r2 p

7A PCMG PCFT NL 73 1.18 1.55 0.96 b0.01S 15 1.13 28.01 0.85 b0.01

7B PCMG PCFL S 106 0.89 17.82 0.59 0.4898A PCRAD PCFT NL 200 0.68 29.17 0.77 b0.001

PCFT S 8 0.34 241.93 0.00 0.034PCMG NL 75 0.58 29.37 0.74 0.450PCMG S 114 1.09 25.8 0.65 0.049PCFL S 162 0.99 15 0.53 0.164

8B PCRAD PCREF NL 202 0.63 29.75 0.75 b0.001PCREF S 171 1.18 −3.23 0.77 0.117PCREF NL + S 373 1.06 9.23 0.74 b0.001PCREF NL + S 373 1.09 0 a b0.001

Legend: n=data points; regression model parameters y=ax+b; NL = samplesfrom The Netherlands, S = Spanish samples; bold-face p-values are significantat 99% confidence after applying Bonferroni's correction.a Intercept forced to zero.

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Table 4Multiple regression results of residual errors of PCRAD versus PCREF withintercept forced to zero (see text)

Country PCREFb50 mg m−3 PCREFN50 mg m−3

NL + S NL S NL + S NL S

n 234 141 93 101 56 45Multiple R2 0.366 0.564 0.205 0.084 0.069 0.152Adjusted R2 0.352 0.551 0.160 0.046 −0.004 0.067SE of estimate 14.90 9.37 15.00 45.50 41.69 49.43whole-model p 0.000 0.000 0.001 0.073 0.442 0.150β Chl b 0.499 0.723 0.296 0.219 −0.002 0.299β Fucoxanthin 0.210 0.202 0.304 −0.057 −0.193 0.011β Phaeophytin 0.131 0.065 −0.095 0.146 −0.006 0.130β Alloxanthin −0.026 −0.139 0.115 −0.093 −0.106 −0.087β Peridinin −0.041 n/a 0.020 n/a n/a n/a

The residuals were regressed against the marker pigments Chl b (green algae),peridinin (dinoflagellates), alloxanthin (cryptophytes), and fucoxanthin (diatoms),as well as the Chl degradation product phaeophytin. The analysis was performed forall samples simultaneously, for subsets by country, and for the PCREF range split at50 mg m−3. Significant (N99% confidence) p and β values are printed in bold face.Prior to analysis, outliers (residuals more than ±2 times standard deviation) wereremoved. Symbols and abbreviations: NL— Samples from The Netherlands; S—Spanish samples; n — number of valid cases; SE — standard error.

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by Chl a. From optimization of PCRAD versus extracted PC, itwas found that ε=0.24 for cyanobacteria-dominated waters(Simis et al., 2005).

2.6. Data screening and analysis

Concurrent spectroradiometric measurements with the ASD-FR and PR-650 instruments were available for 13 samples from4 Spanish lakes, visited in May 2005. Linear regression analysiswas used to evaluate the correlation between data subsets of thespectroradiometric products Rrs, bb, and PC (Figs. 2–4). Thereference PC methods (PC extraction and in situ fluorescence)were also compared for samples where more than one techniquewas used (Fig. 7, Table 3). The availability of the different datatypes for each of the water bodies is given in Table 1. For allregression results presented in this study, results marked assignificant passed Bonferroni's correction at 99% confidencelevel unless stated otherwise.

Fig. 5. Boxplots of PCREF and Chl a concentrations as well as the PC

HPLC and phycobilin pigment data were subjected to aprincipal component analysis (PCA) to identify marker pig-ments for phytoplankton composition (Fig. 6). Two samplesfrom the Guadalteba reservoir featured a high-biomass dino-flagellate bloom with extremely high values of peridinin (11and 47 mg m− 3). As these high peridinin values obscuredthe relations between the other pigments in the pigmentmatrix, they were omitted from PCA and further regressionanalyses.

PCRAD was compared with reference PC methods for the fulldataset and subsets separated by country (Fig. 8A). The correlationbetween the radiometric approach and the reference measurementswas analysed using paired t-tests (Table 4). PCRAD was alsocompared to the average of the available reference PC measure-ments for each sample, denoted PCREF (Fig. 8B). It is noted thataveraging reference PC measurements is not optimal, as in severalcases extraction of phycobilin pigments clearly failed (zero valuesfor PCMG, discussed below) while cyanobacterial presence wasevident. In those cases choosing the highest extraction value wouldeliminate part of the error caused by low extraction yield. On theother hand, the PCFL method is most likely to yield positive ratherthan negative errors, as it relies on the sensor sensitivity to PCamong fluorescence of other substances (dissolved organic matter,algal pigments). In cases where PCMG extraction failed, a con-current PCFL measurement would probably be closer to the ‘real’PC concentration. However, since combinations of reference PCmethods were not available for all samples and no comparison wasavailable between PCFT and PCFL, it was considered most straight-forward to define PCREF as the average of all available reference PCvalues.

To evaluate prediction errors by the PC algorithm, a regres-sion with zero intercept was fitted through the plot of PCRAD

against PCREF measurements. The residuals of this regressionwere analysed in a multiple linear regression against chloro-phyllous HPLC pigments including phaeophytin, and substi-tuting fucoxanthin for Chl c as the latter was not measured forthe whole dataset. The multiple regression was carried outseparately for subsets of the data split at PCREF=50 mg m− 3.These analyses were made for the dataset in full as well asseparated by country (Table 4).

REF:Chl a ratio in the Spanish (S) and Dutch (NL) water bodies.

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3. Results

The Rrs(λ) derived from ASD-FR and PR-650 measurementscould be compared for a subset of 13 locations at 4 Spanish lakeswhere both instruments were used. The intercalibrationmeasurements were all carried out in May 2005 in Spain and 7samples were from the hypertrophic L. l'Albufera which stoodout in terms of high Rrs(λ) spectra, near-infrared backscattering,and pigment concentrations. These aspects strongly limit therepresentativeness of the calibration set for the full dataset.Regression of the Rrs data in the used MERIS wavebandsyielded acceptable agreement between the used methods (Fig. 2:r2 =0.94; pb0.01; n=56). The Rrs(λ) values obtained with theASD-FR setup were on average 17% higher than PR-650 valuesin all wavebands. These reflectances lead to 30% higherestimates of bb (Fig. 3; r2 =0.94; pb0.01; n=13). ASD-FRdata nevertheless resulted in only 10% higher PCRAD values(Fig. 4; r2 =0.97; pb0.01, significant at 95% confidence;n=13). The correlation coefficients for products Rrs andPCRAD remained high when the 7 samples from L. l'Albuferawere excluded from the regression (r2 =0.87 and 0.97 for bb andPC respectively), but the relationship was no longer significantfor bb. We did not proceed to calibrate the Rrs of one dataset toanother, as the impact of differences in the measurement of Rrs

on the retrieval of PCRAD was limited, and the number andtimespan of observations were highly limited compared to thefull dataset.

Considerable differences in pigment composition werefound between the Dutch and Spanish water bodies. Boxplotsof PCREF (the average of available PC extractions and PCfluorescence results), Chl a, and the ratio of PCREF to Chl ashowed that the sampling covered a wide range of pigmentconcentrations in both countries, with the most even distributionof pigment concentrations and the highest PCREF:Chl a ratios inSpain (Fig. 5). The PCREF:Chl a ratio is a possible indicator ofthe cyanobacterial share in total phytoplankton biomass. Of thesamples taken and analysed in the Netherlands, 84% had a ratioPCREF:Chl a≤1.25, suggesting relatively low intracellular PC:Chl a ratios, a limited share of cyanobacteria in thephytoplankton, or both. In contrast, 57% of the samplescollected and analysed in Spain had a ratio PCREF:Chl a≥1.25.

PCA based on pigment correlations showed good separationof cyanobacterial and green algal pigments in the full dataset aswell as subsets divided by country (Fig. 6). The diatom pigmentfucoxanthin grouped with cyanobacterial presence (phycobilinpigments and zeaxanthin) in Spanish samples while theinfluence of fucoxanthin on pigment variance was weak in the

Fig. 6. PCA factor scores based on pigment correlations for (A) the full dataset, (B)Dutch samples, and (C) Spanish samples. The following pigmentswere included torepresent the indicated phytoplankton groups: alloxanthin — cryptomonads;phycocyanin, phycoerythrin, zeaxanthin — cyanobacteria; fucoxanthin —diatoms; peridinin — dinoflagellates; phaeophytin — not specified; chlorophyllb, neoxanthin, violaxanthin — green algae. Peridinin was not found in Dutchsamples. The variance that was explained by each PCA axis is given in the axislabels. Two samples taken during a high-biomass dinoflagellate bloom inGuadalteba reservoir were omitted from PCA due to very high (11 and 47 mgm−3) peridinin values that dominated the pigment matrix.

Dutch subset. Indeed, fucoxanthin concentrations were gener-ally low in the Dutch samples. Fucoxanthin was alwayscorrelated with phaeophytin which could indicate that thesesamples were influenced by resuspension of sedimented diatom

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cells. Cryptomonads in the phytoplankton community, indicat-ed by the alloxanthin pigment, were weakly representedregarding the full dataset, but prominent in the Dutch subset.Cryptomonad presence was not correlated to PC, even thoughcryptomonads are known to contain low concentrations of thelatter pigment. Two samples from a single high-biomassdinoflagellate bloom were omitted from PCA and subsequentmultiple regression analyses, as they otherwise dominated thepigment matrix. Subsequently, peridinin presence was restrictedto some samples in the Spanish data and did not contributemuch to total variance. The PCA yielded the following markerpigments, to serve as proxies for the phytoplankton groups witha suspected influence on absorption in the red spectral area:fucoxanthin for diatoms, Chl b for green algae and prochlor-ophytes, alloxanthin for cryptomonads, and peridinin fordinoflagellates.

Reference measurements for PC were derived from extrac-tion of the phycobilin pigments through cell disruption (PCFT

and PCMG), or from in situ PC fluorescence (PCFL). Fig. 7shows comparisons of these methods, which were available forsubsets of the data (Table 1). Regression results are given inTable 3. For all comparisons of methods there was a high degreeof scatter. Between PCMG and PCFT high correlation was foundwith on average slightly higher values of PCMG, although thenumber of concurrent measurements was low for Spanish waterbodies (Fig. 7A). Note that the PCMG method can suffer frompoor separation of Chl a from the supernatant due to thepresence of submicron cell debris, which can easily lead to a10% elevated PC quantification (unpublished results). BetweenPCMG and PCFL methods (Fig. 7B) the linear regressionequation was not significant, which is primarily explained by ahigh number of zero PCMG values (plotted at 0.11 mg m−3).These zero values represent cases where the PCMG method wasunsuccessful in extracting any phycobilin pigments while thepigment could be measured following PCFT extraction or by

Fig. 7. Log–log plot of reference PCmeasurements for sampleswheremultiplemethodseparated by country. (B) PCMG extraction method vs. PCFL fluorescence method, fo

fluorescence. Extraction of the pigment from tough, very small,or mucilage-covered cells may be problematic with this method,but we did not find an obvious reason for the poor extractionyield. Unfortunately no PCFL data was available for the Dutchsamples, which would have completed the comparison. As thereference PC quantification methods showed a high degree ofscatter between them and no trends strongly deviating fromunity, we were not able to identify a standard method, and it wasdecided that no intercalibration of used methods would becarried out. Instead, where multiple measurements wereavailable, the average value (denoted PCREF) was used.

Comparison of PCRAD values with reference PC measure-ments (Fig. 8A) only gave a significant correlation (regressionresults in Table 3) between PCRAD and PCFT. The regressionslope of this comparison was low, caused by a substantialnumber of relatively high PCRAD values in the lower PCFT

range. The correlation between PCRAD and PCREF wassignificant for the full dataset and the Dutch subset, but notfor Spanish samples alone. Positive errors of PCRAD comparedto PCREF occurred most frequently at Dutch sites and wereagain concentrated at the lower end of the PCREF range. Ingeneral, the correlation between PCREF and PCRAD improvedwith PCRADN50 mg m−3. Of all reference PC measurements,only PCFL measurements were often higher than the PCRAD

values.An idealized regression (intercept forced through zero) of

PCRAD versus PCREF reveals under- and overestimations ofPCRAD that are not systematic. The regression slope was 1.09(Table 3). The deviation from unity of this slope is relativelylow in comparison to the scatter that was found whencomparing the reference methods for PC quantification, and istherefore interpreted as support for the PC algorithm's currentparameterization. The relative residual errors calculated as(PCRAD−1.09 PCREF) /PCREF were highest at low PCREF

concentrations, especially when Chl a biomass was not as low

s were applied (A)Mechanical grinding (PCMG) vs. Freeze/Thaw (PCFT)method,r samples taken and analysed in Spain. Regression results are listed in Table 3.

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Fig. 8. Comparison of PC assessment methods. (A) PCRAD plotted against PCFT, PCMG, and PCFL, samples separated by country. (B) PCRAD plotted againstPCREF (the average of available reference methods), separated by country. Note the scale breaks at 200 mg m− 3 in both panels. Regression results are listedin Table 3.

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(Fig. 9A,B). This suggests an influence of other phytoplanktongroups on the quality of PC retrieval in the lower PCconcentration range. At higher (relative) PC concentrations(50–350 mg PC m−3 and PC:Chl a approximately N1.75, visibleas a scattered cluster of samples at the high PC concentration endin Fig. 9B), negative relative errors of PCRAD were visible. At PCconcentrations N350 mg m−3 these negative errors were nolonger visible. Possibly, the negative errors in the intermediaterange are related to a pigment package effect, causing a non-linear relation between pigment concentration and observedabsorption, while the effect is no longer visible at the highestconcentrations due to increased reflectance in the NIR bands,but there is no evidence to support this hypothesis.

Fig. 9. Relative residual errors of the regression model PCRAD=1.09×PCREF, plotteleast squares interpolation of the errors. PCREF and Chl a concentrations of each samrelation to cyanobacterial and total phytoplankton biomass. (A) Results for the 0–8pigment concentration range (note a different gray scale for the relative residual err

To identify the effects of phytoplankton pigment composi-tion on the estimation of PC by the reflectance algorithm (Eq.(5b)), multiple linear regression analyses were carried out on theresidual errors from regression of PCRAD against PCREF withzeroed intercept (calculated from PCRAD−1.09 PCREF). Themultiple regression was carried out against the marker pigmentsfor the main algal groups (identified from PCA) and phaeophy-tin. Table 4 lists the multiple regression results carried out for thefull dataset, the Dutch and Spanish samples separately, and eachpart split into subsets of PCREF higher or lower than 50 mg m−3.Significant overestimations caused by Chl b and fucoxanthinwere found in the lower concentration range, where the residualerrors were relatively high. A positive effect of phaeophytin on

d (shaded areas) as a function of PCREF and Chl a, based on distance-weightedple are plotted as circles to indicate the algorithm performance of each sample in00 mg m−3 PC and Chl a range. (B) Same results, but for the 0–200 mg m−3

ors).

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PCRAD was found to be significant only for the full datasetb50 mg m−3. Only in the Spanish subsets an overestimation ofPCRAD was significantly correlated with fucoxanthin or diatompresence. This corresponds with the differences in the pigmentmatrix that were found between Spain and the Netherlandsthrough PCA, i.e. a weak representation of diatom pigment in theDutch dataset, but co-occurrence of diatom and cyanobacterialpigments in the Spanish subset. Residual PCRAD errors could notbe explained by the regression model for the concentration rangeN50 mg m−3 so the presence of accessory pigments could notexplain the underestimation of some PC values in theintermediate range, as observed from Fig. 9.

4. Discussion

The performance of a semi-analytical, nested reflectanceratio-based algorithm for the quantification of PC (Eq. (5b)) wastested with field spectroradiometric and pigment data collectedin the Netherlands and Spain, in the period 2001–2005. Theinfluence of phytoplankton pigment composition on theperformance of the algorithm was explored using HPLC markerpigments for different phytoplankton groups. Analysis of PC:Chl a ratios and PCA of the HPLC and phycobilin pigmentmatrix suggested that co-occurrence of phytoplankton groupswas common in Dutch samples while cyanobacterial predom-inance occurred in a number of Spanish water bodies. The PCalgorithm results exhibited a positive trend with PC concentra-tions that ranged several orders of magnitude. Closer investiga-tion showed that most PCRAD errors were overestimates thatoccurred in the low (PCREFb50 mg m−3) concentration range.The errors were most evident with Dutch samples and wereprimarily associated with the presence of Chl b, indicating thepresence of green algae or prochlorophytes. Fucoxanthin,indicating diatom presence, and phaeophytin also correlatedwith overestimation of PC in parts of the dataset. It may beassumed that the effect of fucoxanthin represents the influence ofred absorption by Chl c, even though Chl c itself was notmeasured for this dataset.

There is no standardizedmethod for the extraction of PC fromwater samples, while in situ fluorescence probes for PC areincreasingly popular for their ease of use and possibility to usethem in unattended measurement systems. We compared afluorometric approach and two extraction methods in order tocompile the full dataset. The three methods for PC quantificationshowed similar values for concurrent measurements, howeverwith a high degree of scatter. Besides a number of samples forwhich the PCMG method was ineffective, comparison of PCMG

with the other methods suggested no strong methodological biaswithin the current dataset. Nevertheless, PCFL measurementswere not available for the Dutch dataset, so the comparisonremains incomplete. It is noted that the quality of PC extractionfor Dutch sites was improved since the development of the PCalgorithm, thereby reducing the overestimation that waspreviously found (cf. Fig. 6 of Simis et al., 2005). In general,the high degree of scatter between the methods suggest that PCground truth measurements should be used with scrutiny. In vivoPC fluorescence methods are perhaps most promising for routine

measurement of PC, as sample preservation and pigmentextraction steps are not needed. The potential of PC fluorescenceprobes to monitor cyanobacterial biomass has been reported inseveral studies (Asai et al., 2001; Izydorczyk et al., 2005; Leeet al., 1994, 1995).

It was previously found that bulk pigment absorption can beretrieved from turbid water reflectance, seemingly regardless ofsuspended sediments and coloured dissolved organic matter(Simis et al., 2005). Unfortunately, absorption spectra were notavailable for the dataset that was analysed here, so evaluation ofthe empirically defined factors γ and δ to relate aph(665) andaph(620) from the absorption obtained from reflectance ratios(Eqs. (5A) and (5B) respectively) was not possible. Additionally,it was assumed that errors in the PC estimation from fieldspectroradiometric data can be directly related to the presence ofaccessory photosynthetic pigments and degradation products ofChl a. It is noted that the presence of these pigments is notnecessarily the cause of the observed error; this could also be arelated factor such as differences in light scattering or absorptionbetween the phytoplankton groups that the diagnostic pigmentsrepresent. Other errors that are not considered here may yet exist,e.g. those that are inherent to the assumptions that were made inalgorithm development: an assumed negligible influence of thespectral shape of bb, insensitivity to CDOM and suspendedsediments, and the ability to correct for Chl a absorption at620 nm through a constant fraction ε of aph(665). NIR/red bandratio-based algorithms should be relatively insensitive tospectrally neutral (‘white’) errors, when these algorithms areapplied to turbid water bodies that already have a relativelybright reflectance. The influence of both suspended sedimentsand CDOM can be considered spectrally neutral between theused bands. The backscattering may not be neutral between theratioed bands and the 778.75-nm bands, but there is no evidencethat NIR/red band ratios are strongly affected by this whenapplied to turbid water bodies (cf. Gons, 1999; Gons et al.,2000).

Studies of in vivo pigment absorption, decomposed intomultiple Gaussian curves for each pigment through multipleregression, show that the main chlorophyllous pigments (Chl a,b, c, and phaeophytin) absorb light throughout the 600–675 nmregion (Ficek et al., 2004; Hoepffner & Sathyendranath, 1993;Sathyendranath et al., 1987). Indeed, the present study showsthat Chl b, phaeophytin, and fucoxanthin as a proxy for Chl cwere related to overestimation of PC. Specifically at low PCconcentrations, the absorption by these pigments could increaseabsorption in the 620-nm band significantly. The onlycorrection for chlorophyllous pigment absorption in the originalPC algorithm was for Chl a absorption. The influence of thispigment in the 620-nm band was derived from absorption in the665-nm band, which was fully attributed to Chl a, using a fixedfactor ε (Eq. (5b)). The optimized value of ε for cyanobacteria-dominated sites was set at ε=0.24 (Simis et al., 2005), i.e. lowerthan the value of 0.3–0.4 for Chl a in algal species, and muchlower than the specific in vivo absorption of Chl b with ε=0.5–0.6 and Chl cwith ε=1.7–3.7 (Bidigare et al., 1990; Ficek et al.,2004; Hoepffner & Sathyendranath, 1993; Sathyendranath et al.,1987). Obviously, with increasing concentrations of Chl b and c,

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the ε-correction of 0.24 is increasingly inadequate and thederived PC absorption will be too high.

There are no MERIS bands that specifically target theaccessory chlorophylls, and it is yet unknown whetherhyperspectral information could provide a robust index for thepresence of Chl b and c. A correction for the presence of Chl b,using a Chl b-specific absorption value at 620-nm band of0.05 m2 (mg Chl b)−1, efficiently removed most of theoverprediction in the whole dataset (PCRAD=0.83×PCREF+1.71; r2 =0.90; p=0.002; n=223). This specific absorptionvalue is considerably higher than reported values for in vivo Chlb absorption in this waveband, which range 0.002–0.013 m2

(mg Chl b)−1. This suggests that the presence of Chl b in ourdataset weighs more heavily on the PC prediction than would beexpected from its absorption in the 620–665 nm area alone. Asdiscussed above, other factors that influence PCRAD when Chl bcontaining species are present may account for this difference,such as different scattering behaviour or pigment packaging, or adifferent expression of in vivo Chl a absorption in green algae,as indicated by a higher ε factor reported in various literaturestudies. Absorption spectra should be obtained and analysedalong with the pigment matrix of chlorophyllous and phycobilinpigments to resolve this issue. Additional corrections for Chl c(through the fucoxanthin proxy) or phaeophytin did not improvethe PC prediction even though our multiple regression resultsindicated a significant effect of their presence. Correction for thesepigments had a limited effect because they were present in verylow concentrations in most samples. If the pigment balance ofChlorophyllous pigments and PC in our dataset reflects thesituation in most eutrophic inland water bodies, then detection ofthe contribution of Chl b to the absorption envelope can beconsidered themost important next step in algorithmdevelopment.

Despite the demonstrated influence of Chl b and otheraccessory pigments on the PC predictions, PCRAD values above50 mg m−3 were increasingly reliable. Additionally, with higherPC:Chl a ratios the reliability of PCRAD should extend below50 mg m−3, which is no surprise as background absorption at620 nm should become less important when PC starts todominate the absorption envelope. To be safe, predictions below50 mg m−3 should be taken as the high estimate, i.e. the actualconcentration could be lower, but not likely higher than theestimated value. Evaluating the current dataset, 57% of allsamples had such ambiguous PCRAD values below 50 mg m−3.However, we should also consider the Chl a concentrations ofthese particular samples, since low Chl a concentrations will notgive rise to an immediate need for PC monitoring, from theperspective of water quality management. The World HealthOrganization (WHO) dictates a guideline of approximately50 mg Chl am−3 in cyanobacteria as a moderate health warninglevel (WHO, 2003), which corresponds to PC concentration of50 to 100 mg m−3 for common intracellular PC:Chl a ratios of1–2. A lower guideline Chl a concentration may be given forsome cyanobacterial species that are known to exhibit relativelyhigh toxin levels, e.g. Planktothrix agardhii which at a Chl aconcentration of 12–25 mg Chl a m−3 may reach the dailytolerance intake level for microcystin (WHO, 2003). When noshortage of nitrogen exists, an intracellular PC:Chl a ratio of at

least 1.5 may be expected, so that sites with PC concentrations of18–37 mg m−3 should optimally be distinguishable from siteswith lower PC concentrations. This criterion was not yet metwith the used PC algorithm. Of all samples in our dataset, 29%had a Chl a concentration higher than 12 mg m−3 (regardless ofwhether this was present in cyanobacteria) in combination with aPCRAD value below 50 mg m−3. Therefore, in 29% of our sitesthe cyanobacterial state of a water body would not be indicatedwith confidence while the risk of toxic cyanobacteria is present.In all other cases, the site could either be considered a prioriharmless as the Chl a concentration would be too low to suspectproblems, or the PCRAD estimate would be above the 50 mgm−3

threshold and could be used as an indicator for the cyanobacter-ial biomass. Considering both Chl a and PC estimates thussignificantly increases, up to 71% of sites, the value of remotelysensed information compared to using only Chl a (noinformation on cyanobacterial state) or PC (only 43% of siteshas a reliable estimate).Wewill not discuss the quality of currentChl a algorithm at this point, but Chl a charting from remotelysensed imagery is already carried out for many water bodies,including eutrophic lakes and reservoirs.

Remote sensing of cyanobacterial biomass relies not only onvalidated algorithms for PC and Chl a, but also on the quality ofremotely sensed imagery. Currently only MERIS provides thenecessary spectral bands to estimate both PC and Chl a at a suit-able temporal and spatial resolution. MERIS was initially de-signed to observe the oceans, land, and the coastal zone (Rast etal., 1999). Inland water bodies provide challenges that have notbeen met by the ocean colour community, such as the effects ofadjacent land on the radiance measured at the sensor, and elevatedNIR reflectance from dense phytoplankton blooms, which mayinterfere with atmospheric correction schemes. Our currentfindings warrant further research into the quality of reflectanceproducts for NIR/red band ratio algorithms for turbid inlandwater,both fromMERIS and other (satellite or airborne) sensors, and notonly for Chl a retrieval but also for diagnostic accessory pigments.

As a final consideration it is noted that the physiological stateof cyanobacteria can be such that very little or no PC is produced(Tandeau deMarsac, 1977), or itmay be broken down to serve as anitrogen resource in times of shortage (Bogorad, 1975; Grossmanet al., 1993). In such situationsmapping the PC concentration willyield little information on the cyanobacterial state of a water body,while cyanobacterial biomass and toxin concentrations could stillbe substantial. Also, the ratio of PC to Chl a, which could be usedas an indicator for the share of cyanobacteria in the phytoplankton,is susceptible to variability in the intracellular PC:Chl a ratio ofthe cyanobacteria at the species level, and to variability due tophysiological conditions. Ultimately, remote sensing offers thepossibility to increase the spatial coverage of existing monitoringprogrammes, and to strategically direct conventional monitoringefforts—replacing them should not be the goal.

Acknowledgments

The authors thank the drivers and field technicians at the Centrefor Hydrographic Studies (CEDEX, Spain) and the crew of R/VLuctor (NIOO, The Netherlands). Antonio Quesada (Universidad

Page 14: Influence of phytoplankton pigment composition on remote ... · without substantial cyanobacterial growth were also included. Many of the lakes are subject to eutrophication, while

427S.G.H. Simis et al. / Remote Sensing of Environment 106 (2007) 414–427

Autonoma,Madrid, Spain) is thanked for advice and the use of labfacilities for PC extraction during our data intercalibration efforts.Further, thanks are due to Nicole Dijkman (NIOO) for advice onphytoplankton group representation in the pigment matrix, and toAnaAlonso andCovadongaAlonso (CEDEX) andCobieKleppe-van Zetten (NIOO) for HPLC pigment analysis. Critical readingby three anonymous reviewers helped to improve the manuscript.Funding for SGHS was provided through grant EO-053 from theUser Support Programme managed by the programme officeExternal Research of the Netherlands Organization for ScientificResearch-National Institute for SpaceResearch (NWO-SRON).Aone-month stay for SGHS at CEDEXwas covered by a grant fromthe Schure-Beijerinck Popping fund (KNAW, The Netherlands).This is publication 3936 NIOO-KNAW Netherlands Institute ofEcology, Centre for Limnology.

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