Assessment of ocean color data records from MODIS-Aqua in the western Arctic Ocean

12 Pages·2015·3.8 MB·English

by  Joaquín E. Chaves

Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎
ContentslistsavailableatScienceDirect
Deep-Sea Research II
journal homepage: www.elsevier.com/locate/dsr2
Assessment of ocean color data records from MODIS-Aqua
in the western Arctic Ocean
Joaquín E. Chavesa,b,n, P. Jeremy Werdella, Christopher W. Proctora,b, Aimee R. Neeleya,b,
Scott A. Freemana,b, Crystal S. Thomasa,b, Stanford B. Hookera
aNASAGoddardSpaceFlightSpaceCenter,Code616,Greenbelt,MD20771,USA
bScienceSystemsandApplications,Inc.,Lanham,MD20706,USA
a r t i c l e i n f o a b s t r a c t
A broad suite of bio-optical and biogeochemical observations collected during the NASA-funded
Keywords: ICESCAPEexpeditionstothewesternArcticOceanin2010and2011wasusedtovalidateoceancolor
Oceancolor satellitedataproductsin thisregion,whichisundergoingfastecological changesinthe contextofa
Satelliteremotesensing changingclimate.Satellite-to-insitumatch-upsfortheMODISinstrumentonboardAqua(MODISA)were
ArcticOcean evaluatedusingstandardNASAempiricalandsemi-analyticalalgorithmstoestimatechlorophyll-a(C),
a
Spatialdistribution spectral marine inherent optical properties, and particulate organic carbon (POC). Results for the
Semi-analyticalalgorithms empiricalalgorithmswerecomparedwiththosefromthesemi-analyticalGeneralizedInherentOptical
Chlorophylla Property (GIOP) algorithm. The findings presented here showed that MODISA C estimates were
POC a
positively biased relative to in situ measurements, in agreement with previous studies that have
CDOM
evaluatedoceancolorretrievalsintheArcticOcean.Thesebiaseswerereproducedusingbothsatellite
MODIS1 andinsitumeasuredremotesensingreflectances,R (λ),indicatingthatestimationerrorsarederived
rs
from the application of the empirical algorithm and not by the observed radiometry. This disparity
appearstobecausedbycontributionsofhighspectralabsorptionfromchromophoricdissolvedorganic
matter (CDOM), which is a well-documented feature of Arctic Ocean waters. The current MODISA
empiricalalgorithm(OC3M)appearstoattributeCDOMabsorptionintheblueregionofthespectrumto
phytoplanktonabsorption.Incontrast,GIOPshowedsignificantimprovementoverOC3MC estimates
a
by effectively discriminating between phytoplankton and CDOM absorption. Additionally, executing
GIOPwithanexpandedsetofspectralbandsderivedfrominsituradiometry,insteadofjustsixMODISA
bands, further improved the performance of absorption estimates. These findings reinforce previous
suggestionsthatsemi-analyticalapproacheswillprovidemorereliabledatarecordsforArcticstudies
thanexistingempiricalmethods.POCestimatesshowednoclearbiasrelativetoinsitumeasurements,
suggestingthattheempiricalalgorithmbetterrepresentshighlatitudeoceanswithregardstothebio-
opticalsignatureofsuspendedparticulatestocks.
&2015ElsevierLtd.Allrightsreserved.
1. Introduction andecologyoftheChukchiandBeaufortseas.Recentlydocumented
changesinsummericecover(Comisoetal.,2008),accompaniedby
The Impacts ofClimate change ontheEco-Systemsand Chem- a transition from thick multiyear ice to a seascape increasingly
istryoftheArcticPacificEnvironment(ICESCAPE)Projecttookplace dominated by thinner, first-year ice (Comiso, 2011; Kwok et al.,
duringtheborealsummerin2010and2011,undertheauspicesof 2009) have brought discerniblemodifications tothe phenologyof
theNationalAeronauticsandSpaceAdministration(NASA;https:// the Arctic Ocean region, such asearlier occurrences of the annual
www.espo.nasa.gov/icescape/).Themultidisciplinaryproject'smain phytoplanktonblooms(Kahruetal.,2011)andanoverallincreasein
objective was to improve our understanding of the impact of oceannetprimaryproduction(NPP)(ArrigoandvanDijken,2011;
naturalandanthropogenicclimatechangeonthebiogeochemistry Bélangeretal.,2013;Pabietal.,2008;Petrenkoetal.,2013).Climate
modelspredictthaticecoverchangesmayaccelerateinthefuture,
withthepossiblescenarioofanice-freeArcticduringthesummer
monthsbeforetheendofthecentury(Hollandetal.,2006;Wang
nCorrespondingauthorat:NASAGoddardSpaceFlightSpaceCenter,Code616,
and Overland, 2009). The ICESCAPE program integrated a multi-
Greenbelt,MD20771,USA.
E-mailaddress:[email protected](J.E.Chaves). disciplinaryapproach to enhance existing field data sets aimed at
http://dx.doi.org/10.1016/j.dsr2.2015.02.011
0967-0645/&2015ElsevierLtd.Allrightsreserved.
Please citethis article as:Chaves,J.E., etal., Assessmentofoceancolordata recordsfromMODIS-Aquainthewestern Arctic Ocean.
Deep-SeaRes.II(2015),http://dx.doi.org/10.1016/j.dsr2.2015.02.011i
2 J.E.Chavesetal./Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎
supporting synthesis efforts to better understand the impacts of extent optically different, and those differences are linked to
climatechangeontheecologyandbiogeochemistryoftheChukchi systematicvariabilityincommunitystructureandbiogeochemical
andBeaufortSeas(e.g.,Arrigoetal.,2012). function(Loiseletal.,2010;Saueretal.,2012;Szetoetal.,2011).
Ocean color satellite instruments provide valuable tools for Semi-analytical algorithms (SAAs) provide an alternative to the
advancing our understanding of the effects that these predicted band ratio approaches described above (IOCCG, 2006; Werdell et
changes would have on the Arctic Ocean. The remoteness of the al., 2013a). SAAs use combinations of empiricism and radiative
westernArcticandthescarcityofinsituresearchplatformssuitable transfertheorytosimultaneouslyestimateconcentrationsofIOPs
forworkingyearroundinthisseasonallyice-coveredenvironment for C , non-algal particles, and CDOM, thereby eliminating the
a
highlight the need for achieving climate-quality data records of need for the bio-optical assumption and conceptually improving
ecologicallyrelevantparametersfromautonomous,remote-sensing the performance of C retrievals, particularly in areas where
a
platforms such as satellite instruments. The NASA Sea-viewing historicallytraditionalapproacheshavelagged.Initialassessments
Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution of IOP time-series from SAAs in the Arctic Ocean have shown
Imaging Spectroradiometer onboard Aqua (MODISA), for example, potentialforimproving remotesensing C retrievals intheArctic
a
provideestimatesofthenearsurfaceconcentrationofchlorophyll-a (Bélanger et al., 2007; Ben Mustapha et al., 2012). Only very
(C ,mgm(cid:1)3)andothermarineecologicalparameterssuchasma- recently have the broad datasets necessary for this task been
a
rineinherentopticalproperties (IOPs;thespectralabsorptionand assembled as the direct result of programs such as ICESCAPE,
scattering properties of water column constituents and seawater Malina,andTakuvik(Antoineetal.,2013;Matsuokaetal.,2013b;
itself).Time-seriesofC andIOPscanbevaluabletoolsforassessing Zhengetal.,inpress).
a
the changes in the ecology of Arctic Ocean in the context of a Ourcontributionstothisemergingbodyofworkonoceancolor
changingclimate.Bothcanbeused,forexample,toinferthetiming remote-sensingoftheArcticaretwofoldandallwithinthecontext
andsuccessionofphytoplanktonbloomsandtogenerateestimates of defining new requirements for advanced satellite instruments
of NPP (Behrenfeld et al., 2005; Henson et al., 2010). Satellite with increased spectral resolution (e.g., the NASA Pre-Aerosols,
instruments such as SeaWiFS and MODISA, therefore, provide Clouds, and ocean Ecosystems (PACE) mission scheduled for
biogeochemicaldatarecordsofpotentialinterestforArcticstudies launch in 2020 (PACE Mission Science Definition Team, 2012)).
ontemporalandspatialscalesthatcannotbeachievedbyconven- First, we evaluated the quality of standard MODISA ocean color
tional aircraft and in situ sampling strategies in that region. data records in the western Arctic Ocean using in situ data coll-
Furthermore, SeaWiFS and MODISA operate in sun-synchronous, ected during the ICESCAPE campaigns as ground truth. Doing so
polar orbits that produce global coverage every two days with a assessed the quality of standard (current operational) MODISA-
nadirspatialresolutionof1.1km2. derived products for use in Arctic climate studies and reiterated
Oceancolorsatelliteinstrumentsprovideestimatesofremote- the need for advanced algorithm approaches that simultaneo-
sensing reflectances (R (λ), sr(cid:1)1) after atmospheric correction. usly account for phytoplankton, non-algal particles, and CDOM
rs
Standard NASA ocean color data processing uses globally tuned (Bélanger et al., 2007; Ben Mustapha et al., 2012). Second, we
λ
algorithmstorelateR ( )tosecondarygeophysicaldataproducts, explored the advantages of incorporating additional wavelengths
rs
suchasC ,manyofwhichremainempiricalinformandfunction intosemi-analyticalinversionalgorithms.Doingsoreinforcedthe
a
(McClain, 2009). The standard C algorithms for SeaWiFS and potentialbenefitsofadvancedinstrumentsystemswithincreased
a
MODISA(OC4andOC3M,respectively;O'Reillyetal.,1998)relate spectral resolution for Arctic studies. We acknowledgethat ICES-
λ
ratios of blue-to-green R ( ) to C based on statistical relation- CAPE provides only a temporally-limited snapshot of Arctic bio-
rs a
shipsobservedusingaglobalinsitudataset(WerdellandBailey, geochemistry and MODISA was only one of several operational
2005).Insomeinstances,theaccuracyofthoseestimateshasbeen satellitemissionsduringthefieldcampaigns.Giventheaforemen-
foundtounderperformwhenassessedatregionalscales.Inhigh- tioned goals, however – and the complementary studies in this
latitudeoceans,lowerpigmentspecificspectralabsorptioncoeffi- special issue that benefit from an evaluation of MODISA data
cientshavebeeninvokedtoexplaintheunderestimationofC by records – our highly focused results contribute to the growing
a
global ocean color algorithms (Cota et al., 2003; Dierssen and foundation of Arctic remote sensing research. We chose MODISA
Smith,2000;Mitchell,1992;MitchellandHolm-Hansen,1991).In for our analyses since it was best performing NASA ocean color
the Arctic, those biases have been found to be non-uniform sensor operational at the time of both ICESCAPE field campaigns
throughout the basin, but with consistent patterns of under and (Franzetal.,2008).
overestimationcircumscribedtospecificregions.IntheLabrador
Sea,theOC4algorithmwasfoundtounderestimateC by50%over
a
mostofitsdynamicrange(Cotaetal.,2003),whileinthewestern 2. Materialandmethods
ArcticOcean(i.e.,BeaufortandChukchiseas)andtheArcticregion
oftheAtlantic,thebiashasbeenconsistentlytowardsoverestima- 2.1. Insitudatacollection
tion (Ben Mustapha et al., 2012; Matsuoka et al., 2007;Stramska
etal.,2003;WangandCota,2003).Atypicallyhighabsorptionby Field observations (Fig. 1) were conducted during two ICES-
chromophoricdissolvedorganicmatter(CDOM)fromriverinputs CAPEfieldcampaigns,fromJune15toJuly22,2010,andfromJune
tothis ArcticregionhasbeensuggestedasthemaincauseforC 25toJuly29,2011,onboardtheUSCoastGuardCutterHealy.The
a
overestimation from satellite radiometry (Brunelle et al., 2012; field observations in this study were carried out from the Arctic
Matsuokaetal.,2007).AttemptsatdevelopingregionallytunedC SurveyBoat(ASB),whichwasdeployedofftheHealyalmostdaily
a
for the Arctic (Cota et al., 2004; Wang and Cota, 2003) have had when sea and ice conditions permitted. The ASB is a 10m alu-
mixed successes (Ben Mustapha et al., 2012; Matsuoka et al., minumlandingcraftthatprovidedasuitableplatformformaking
2007). accurate measurements of the underwater light field away from
RegionalempiricalC algorithmsfortheArctic,andotherocean thewaterdisturbedbythecutterandclosertotheiceedges.
a
regions, are based on linear corrections built upon the global, Tworeplicatesurfacewatersamplesforanalysisofphytoplankton
empirically-derivedalgorithms,andthusrestuponthesamebio- pigments,POC,andspectralabsorptionbyparticles(a(λ);m(cid:1)1)were
p
opticalassumptionthattheIOPsofnaturalwaterscovarywiththe collected using clean, 10L polyethylene containers at each station.
concentration of C (Gordon and Morel,1983; Siegel et al., 2002, Surface samples foranalysis of CDOM absorption (a(λ); m(cid:1)1) were
a g
2005). All ocean basins deviate from this ideal and are to some collected using clean, combusted (4501C, 4h) 500mL amber glass
Please citethis articleas: Chaves,J.E.,etal., Assessmentofocean colordata recordsfromMODIS-Aquainthewestern Arctic Ocean.
Deep-SeaRes.II(2015),http://dx.doi.org/10.1016/j.dsr2.2015.02.011i
J.E.Chavesetal./Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎ 3
obtainedby
(cid:1) (cid:3) (cid:1) (cid:3)
a λ ¼2:303A λ =L ð4Þ
g
where L is the pathlength in meters. Absorbance data were
corrected for baseline offsets by subtracting the average absor-
bance value between 690 and 700nm. The spectral absorption
coefficient of dissolved plus non-algal detrital material, a (λ), is
dg
thengivenby
(cid:1) (cid:3) (cid:1) (cid:3)
a ðlÞ¼a λ þa λ ð5Þ
dg g d
λ λ
Currently,thecontributionsofa ( )anda ( )cannotbeseparated
d g
in the satellite algorithm paradigm as they maintain the same
spectralshapes(IOCCG,2006).
POCwasdeterminedonaPerkinElmer2400elementalanalyzer,
followingthe protocols of the Joint Global Ocean Flux Study (Knap
et al., 1996). The instrument was calibrated using acetanilide
(C H NO)standardsweighedataprecisionof0.001mg.Forquality
8 9
assessment, National Institute of Standards and Technology (NIST)
referencematerial(BuffaloRiverSediment,NIST8704)wasanalyzed
along with samples. The root mean squared percentage error
Fig.1. Mapoftheareaofstudy.SymbolsdenoteArcticSurveyBoat(ASB)stations (RMSPE)forreferencematerialdeterminationswere9.7%and4.4%,
carriedoutduringthe2010and2011ICESCAPEcampaigns. forthe2010and2011samples,respectively.
λ
The radiometric quantities upwelling radiance (L ( );
u
bottles. Determinations of Ca, other photosynthetic and accessory μWcm(cid:1)2nm(cid:1)1sr(cid:1)1) and downwelling irradiance (Ed(λ); μW
pigments, POC, and a(λ), were performed on material retained on cm(cid:1)2nm(cid:1)1) were measured at 19 spectral bands between 300
p
25mm, 0.7mm pore size Whatman GF/F filters under low vacuum and900nmusinghand-deployedC-OPSandSuBOPSsubmersible
(o50kPa). Phytoplankton pigments were determined using high profilingradiometers(BiosphericalInstruments,Inc.;Hookeretal.,
performanceliquidchromatography(HPLC)followingtheprocedures 2010;Morrowetal.,2010),respectively,duringthe2010and2011
ofVanHeukelemandThomas(2001),asfurtherdescribedinHooker campaigns following NASA Ocean Optics Protocols (Mueller and
etal.(2005). Austin, 1995). A radiometer to measure surface solar irradiance
Theopticaldensityofparticulatematter(ODf(λ);unitless)was (Es(λ); μWcm(cid:1)2nm(cid:1)1) in corresponding spectral bands was
measuredonaPerkinElmerLambda35spectrophotometerusing mountedatopatelescopingmastwithaclearviewoftheskyon
mas2p)ewctarsalmraenagseuroefd30w0i–th80c0alnipme.rsT.hTeoclmeaeraasnucreeatrheeaoOfDtfh(λe)fioltfenro(Anf-; sbro(cid:1)a1r)datnhdeAspSeBc(tHraolodkieffru,s2e01a0tt)e.nRuematoiotnesceonesfifincgiernetflsefcotranEcde(λs)((RKrds((λλ));;
pigmentedmaterials(a (λ);m(cid:1)1),thefilterswereextractedusing m(cid:1)1)werecalculatedfromtheradiometricprofilesviathemeth-
d
95% methanol and rescanned (Kishino et al., 1985). The total odsdescribedinWerdellandBailey(2005),andreferencestherein.
particulateandde-pigmentedabsorptioncoefficientswerecalcu- A linear exponential fit was applied to radiance and irradiance
latedby profiles to calculate the spectral diffuse attenuation coefficients.
ap=d(cid:1)λ(cid:3)¼2:303n(cid:1)ODf;p=d(cid:1)λ(cid:3)(cid:1)ODbs(cid:1)λ(cid:3)(cid:1)ODnull(cid:1)λ(cid:3)(cid:3)=(cid:1)βl(cid:3) ð1Þ Ttohobseelocwoe-fsfiucrifeancetsvwaelureesu,swedhitcohpwroepraegathteenradtriaannscmesitatneddiarrcardoisasntchees
where β is the pathlength amplification correction set to 2 air-sea interface (Mueller and Austin, 1995). Quality control and
(unitless), and OD (λ) is the optical density of the blank GF/F assurance applied to these apparent optical properties (AOPs) is
bs
filter.Thepathlength,l(m),isgivenby furtherdescribedinWerdellandBailey(2005).
Approximately 90% of what a satellite ocean color instrument
l¼V =A ð2Þ
f f measuresincludesweightedcontributionsfromallwatercolumn
whereVf(m3)isthefilteredvolume(Mitchelletal.,2003;Roesler, constituents more shallow than the e-folding depth for Kd(λ)
1998).TheOD(λ)ofa (750)anda (750)wereassumedtobezero (Gordon and McCluney, 1975).While the optical weighting of
f p d
(OD ). The phytoplankton absorption coefficient, a (λ), is then biogeochemicalparametersremainsdesirableforoceancolordata
null ph
givenby productvalidationactivities,thiscouldnotbeachievedusingour
a (cid:1)λ(cid:3)¼a (cid:1)λ(cid:3)–a (cid:1)λ(cid:3) ð3Þ datasetsinceonlysurfacediscretewatersampleswerecollected
ph p d (e.g., Zaneveld et al., 2005). We expect this inability to optically
Samplesfordeterminationofag(λ)wereGF/Ffilteredatseaand weight our Ca, POC, and IOP samples to impart only minor
stored at 81C in clean, combusted 250mL amber-colored glass variability into our results, as the majority of our stations main-
bottles for delivery to NASA Goddard Space Flight Center for tainede-foldingdepthsforKd(443)between5and10m.
analysis. Prior to a (λ) determination, samples were re-filtered
g
(pre-rinsed0.22μmmembranefilters)inthelaboratoryandabso- 2.2. Satellitedata
λ
rbance spectra (A( ); unitless) were measured against a Milli-Q
purewaterreferenceona2m-pathlength,liquidcorewaveguide MODISA Level 2 data records that encompassed the two
absorptioncellcoupledtoaTidasIfiberopticspectrometer(WPI ICESCAPEcampaignswereacquiredfromtheNASAOceanBiology
Inc.;Milleretal.,2002).Measuredabsorbanceswerecorrectedfor Processing Group (OBPG: http://oceancolor.gsfc.nasa.gov). Using
thedifferenceinrefractiveindicesbetweenthereferenceandthe thesedatarecords,satellite-to-insitumatch-upsforMODISAwere
salt-containingsamples.Alinearcorrectionwasderivedfromthe generated using the operational OBPG validation infrastructure
absorbancesmeasuredonNaClsolutionsofknownsalinityagainst (http://seabass.gsfc.nasa.gov/seabasscgi/search.cgi). Satellite data
a pure water reference. The corresponding spectral absorbance processing and quality assurance for these match-ups followed
correction for a given salinity was subtracted from the obser- Baileyand Werdell (2006). Specifically: (a) temporal coincidence
ved sample spectrum. The spectral absorption coefficients were wasdefinedas73h;(b)satellitevalueswerethefilteredmeanofall
Please citethis article as:Chaves,J.E., etal., Assessmentofoceancolordata recordsfromMODIS-Aquainthewestern Arctic Ocean.
Deep-SeaRes.II(2015),http://dx.doi.org/10.1016/j.dsr2.2015.02.011i
4 J.E.Chavesetal./Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎
(cid:4)
unmasked pixels ina 5(cid:3)5 boxcentered on the in situ target;and C fromO'Reillyetal.(1998;OC3M);
(c) satellite values were excluded when the median coefficient of (cid:4) Ca,a (λ),anda (λ)fromWerdelletal.(2013a,2013b;GIOP).
(cid:4) a ph dg
variation for unflagged pixels within the box exceeded 0.15. These POCfromStramskietal.(2008;hereafterStramskiPOC).
temporalandspatialthresholdsweredevelopedfortheopenocean
andmaynotbeidealforadynamicregionsuchasthewesternArctic,
whereadjacencyeffectsalongseaicemarginsandpixelcontamina- Note these are all data products that NASA produces opera-
tionbyseaiceareknownadditionalsourcesoferror(Bélangeretal., tionally for the ocean color satellites for which they maintain
2007). However, reducing the window of temporal coincidence to responsibility. Both OC3M and the Stramski POC algorithm use
71handthesatelliteboxto3(cid:3)3pixelsdidnotchangethenumber blue-to-green ratios of R (λ) as input into empirical expressions.
rs
of match-ups or significantly alter our results. Regression statistics OC3M uses the greater of R (443)/R (547) and R (488)/R (547),
rs rs rs rs
were calculated using Type II methods; the caption of Table 1 while the Stramski POC algorithm uses R (488)/R (547). The
rs rs
provides definitions and calculation methods for the statistics. sequencing band ratios of OC3M are hereafter referred to as a
Finally,MODISALevel3compositesforthedurationeachICESCAPE maximum band ratio (MBR; O'Reilly et al., 1998). Note that the
campaignat2kmspatialresolutionweregeneratedfromtheLevel current OBPG version of OC3M includes the modifications pre-
2 data records using OBPG binning software (l2bin and l3gen). sentedinhttp://oceancolor.gsfc.nasa.gov/ANALYSIS/ocv6/.
StandardLevel3maskswereused,asdescribedinFranzetal.(2005). In contrast to the latter algorithms, Generalized Inherent
Optical Property model (GIOP) is a spectral matching inversion
model (i.e., SAA) that uses nonlinear least squares methods to
2.3. Bio-opticalalgorithms
solve for three eigenvalues (a (443), a (443), and the spectral
ph dg
backscattering coefficient for particles (b (λ); m(cid:1)1) at 443nm)
ModeledestimatesofC ,POC,andspectralIOPsweregenerated bp
a λ using predefined eigenvectors (spectral shapes for the three
usingboththeinsituandMODISAR ( ).Thefollowingalgorithms
rs
components).GIOPproducesestimatesofC throughtherelation-
anddataproductswereused: a
ship a (443)¼0.055 C , where 0.055 represents the C -specific
ph a a
absorptioncoefficient(m2mg(cid:1)1)at443nm.Weusedthedefault
Table1
TypeIIregressionstatisticsformodeledgeophysicalparameterscalculatedusing configuration of GIOP; Werdell et al. (2013a) modified following
insituRrsvs.insitumeasuredparameters.Nissamplesize;r2isthecoefficientof Werdell et al. (2013b); Werdell et al. (2013a) provide additional
determination;slope(7SE)istheregressionslopeplus/minusitsstandarderror. detailsontheoperationandlimitationsofGIOP.GIOPwasapplied
RMSEistheresidualmeansquareerroroftheregression.Ratioisthemedianratio twice to in situ R (λ) – first using only six MODISAwavelengths
calculatedasmedian(model/insitu).Ther2,Slope,andRMSEwerecalculatedusing rs
(412, 443, 488, 531, 547, and 667nm) to mimic the satellite
log-transformeddataforCa,aph,andadg.CaisfromOC3M.POCisfromStramski
(2008). The absorption coefficients are from the GIOP algorithm. Six MODISA application of this algorithm, and then using 11 wavelengths
wavelengthswereusedasinputtoGIOP(412,443,488,531,547,and667nm). (adding 395, 465, 510, 560, and 625nm tothe standard MODISA
suite).
N r2 Slope(7SE) RMSE Ratio
WeacknowledgethatalternativeSAAsexist,includingregion-
Ca(OC3M) 33 0.62 0.72(0.08) 0.295 3.05 ally tuned versions [e.g., the Garver–Siegel–Maritorena (GSM)
Ca(GIOP) 32 0.72 0.73(0.07) 0.245 1.56 approach of Maritorena et al. (2002), the Quasi-Analytical Algo-
POC 41 0.63 1.04(0.11) 147.97 1.08 rithm(QAA)ofLeeetal.(2002),andtheirmodifiedversionsinBen
aph(443) 33 0.85 1.18(0.08) 0.202 1.64 Mustaphaetal.(2012)andBélangeretal.(2007),respectively].An
adg(443) 30 0.91 1.05(0.11) 0.082 0.77 algorithminter-comparisonexercise,however,exceededthescope
Fig.2. OC3MestimatedCa(A),StramskiPOC(B),GIOPaph(443)(C),andGIOPadg(443)(D)usinginsituRrs(λ),versusinsitumeasuredvalues.SupportingTypeIIregression
statisticsareprovidedinTable1.
Please citethis articleas: Chaves,J.E.,etal., Assessmentofocean colordata recordsfromMODIS-Aquainthewestern Arctic Ocean.
Deep-SeaRes.II(2015),http://dx.doi.org/10.1016/j.dsr2.2015.02.011i
J.E.Chavesetal./Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎ 5
Fig.3. MODISAversusinsituRrsat443(A),488(B)and547(C)nm.Themaximumbandratio(MBR),calculatedasmax(Rrs4434Rrs488)/Rrs547,isshownin(D).TheMBRis
usedasinputintotheOC3Malgorithm.TypeIIregressionstatisticsarepresentedinTable2.
Table2 led to a depressed regression slope of 0.72 (Fig. 2a). When only
TypeIIregressionstatisticsforMODISAproductsvs.insitumeasuredparameters. consideringC r1and0.2mgm(cid:1)3,theratiosroseto3.37and5.25
a
SeeTable1captionforadditionaldetails. andtheslopesfellto0.66and0.64,respectively.GIOPC showed
a
significantlyreducedbiasrelativetothatfromOC3M,astheratio
N r2 Slope(7SE) RMSE Ratio
forallC fellto1.56,butmaintainedasimilarregressionslopeof
a
Rrs(412) 7 0.15 4.29(2.11) 0.0013 1.52 0.73. Stramski POC calculated with in situ Rrs performed statisti-
Rrs(443) 7 0.73 2.06(0.50) 0.0006 1.37 callybetterthantheCaalgorithms.Asdemonstratedbyitsslopeof
Rrs(488) 7 0.98 1.37(0.10) 0.0002 1.06 1.04 and ratio of 1.08, the Stramski POC showed no clear bias
Rrs(531) 7 0.96 0.97(0.08) 0.0002 1.06 relative to in situ measurements over its full dynamic range
Rrs(547) 7 0.92 0.79(0.10) 0.0003 1.12
Rrs(667) 7 0.94 0.87(0.09) 0.00001 1.07 (Fig. 2b). The six-wavelength GIOP run provided reasonable
MBR 7 0.94 0.88(0.10) 0.160 1.03 estimates of a (443) and a (443) with slopes slightly above
ph dg
POC 13 0.93 0.55(0.05) 35.15 0.92 unity, but with positivelyand negatively biased model-to-in situ
Ca(OC3M) 10 0.64 0.37(0.10) 0.177 3.95 ratios of 1.64 and 0.77, respectively (Fig. 2c, d). GIOP showed
Capah((G44IO3P)) 1102 00..6654 00..4924((00..1119)) 00..220650 32..6222 significant improvement over OC3M with regards to estimating
adg(443) 10 0.44 (cid:1)0.74(0.21) 0.119 0.43 thepresenceofphytoplanktonoveritsfulldynamicrange.Ther2,
regressionslope,andRMSEforOC3MC improvedfrom0.62,0.72,
a
and0.295,respectively,to0.85,1.18,and0.202forGIOPa (443).
ph
λ
of this manuscript and did not substantially contribute to our GIOPlessvariablydescribedphytoplanktonsignatureswitha ( )
ph
stated goals of comparing empirical and SAA approaches and thanwithC ,mostlikelyduetotheinabilityofthefixedvalueof
a
exploring expanded spectral resolutions. Furthermore, previous an (443) (¼0.055m2mg(cid:1)1) to accurately represent phytoplank-
ph
studieshaveshownGIOPtoperformequivalentlytoorbetterthan ton physiological conditions at all times. For context, the mean
GSMandQAAonglobalscales(Brewinetal.,2013;Werdelletal., an (443)forourtwofieldcampaigndatasetwas0.054(70.025)
ph
2013a, 2013b). We recommend that future studies continue to m2mg(cid:1)1.
λ
explore regional parameterizations (Bélanger et al., 2007, Ben MODISA-versus-insituLevel2R ( )match-upsshowedbetter
rs
Mustapha et al., 2012) and ensemble approaches for applying agreement in green wavelengths than in blue wavelengths, with
SAAs[suchastheopticalwatertypemethodofMooreetal.(2009) MODISAvaluesexceedingin situvaluesat wavelengthsless than
or the iterative method of Brando et al. (2012)] with particular 488nm (Fig. 3a–c, Table 2). This overestimation was spectrally
emphasesonexploitingincreasedspectralresolution. dependent with regression slopes and satellite-to-in situ ratios
thatsequentiallyfellfrom4.29and1.52,respectively,to0.97and
1.06 from R (412) to R (531). Nevertheless, the MBR agreement
rs rs
3. Results was very good, with a satellite-to-in situ ratio of 1.03 (Fig. 3d).
λ
WhilethebrighteningofMODISAR ( )hadspectraldependence,
rs
The direct comparison of modeled and ground-truth (in this theMBRpredominantlyusedR (488)andR (547)(alsotheinput
rs rs
case, in situ) values provided a straightforward mechanism for to the Stramski POC algorithm), which had comparable satellite-
assessing the accuracy of the MODISA bio-optical algorithms. to-insituratiosof1.06and1.12,respectively.Weexpectthatthis
λ
OC3MC derivedusinginsituR ( )waspositivelybiasedrelative Rayleigh-like brightening of blue wavelengths resulted from
a rs
to in situ C , with a model-to-in situ ratio of 3.05 (Table 1). This adjacency effects imposed, for example, by the nearby presence
a
wasparticularlytrueforvaluesbelow1mgm(cid:1)3,whichultimately oficeedges(Bélangeretal.,2007;Perovich,1996; Warren,1982;
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6 J.E.Chavesetal./Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎
Warren et al., 2006) in combination with imperfect atmospheric andneartherecedingiceedgeatthenorthendofeachscene,as
correction due to the elevated solar and sensor geometries that opposedtothoseneartheAlaskancoastandKotzubueSoundon
occurathighlatitudes.Furthermore,elevatedturbidity(e.g.,when theLevel3imagery(Figs.5and6).Aroundtheformer,particularly
C exceeds1or2mgm-3)triggersabio-opticalmodelwithinthe in 2010, GIOP reported low a (443) and prominent features of
a dg
atmosphericcorrectionprocesstoaccountfornon-negligiblenear- high C captured by both OC3M and GIOP. On the other hand,
a
λ
infrared R ( ) (Bailey et al., 2010). The applicability of this bio- along the AK coast, particularly in Kotzbue Sound, GIOP a (443)
rs dg
opticalmodeltoArcticwatersrequiresfurtherevaluation. andOC3MC werebothhigh,whereasthemagnitudeofGIOPC
a a
Thepatternsofmodeled-versus-measuredC ,POC,andIOPsfor was much reduced. These examples imply that either elevated
a
λ λ
MODISAR ( )weresimilartothosemodeledusinginsituR ( ), a (443)ora (443)triggeredOC3MtoreportelevatedC ,which
rs rs dg ph a
whichwasnotunexpectedforOC3MandStramskiPOCalgorithms is consistent with previous findings that OC3M and OC4 mistake
λ
giventhesimilaritiesinthesatelliteandinsituR ( )at488and elevated CDOM as elevated C (Ben Mustapha et al., 2012;
rs a
547nm(Table2).OC3MC fromMODISAversusHPLCC yielded Matsuoka et al., 2007). Maps of unbiased percent difference
a a
r2,regressionslope,andratiosof0.64,0.37,and3.95,respectively, (UPD; see Fig. 7 for its calculation) between OC3M and GIOP C
a
λ
comparedto0.62,0.72,and3.05forOC3MC frominsituR ( ). showed that the regions of least difference between those esti-
a rs
Bothinsitu-andMODISA-derivedC showedclearelevatedbiases mates,duringbothcampaigns,werelocatedtowardsthereceding
a
atvaluesbelow1mgm(cid:1)3(Fig.4a).WhenonlyconsideringC r1 iceedge,awayfromthecoast,andintheBeringStraitwherethose
a
and 0.2mgm(cid:1)3, the ratios rose to 4.02 and 4.57, respectively. highC featuresoccurred.Thesetwoexampleregions(i.e.,Bering
a
Qualitatively,theMODISA-derivedStramskiPOCachievedasgood St., ice edge vs. AK coast, Kotzbue sound) corresponded to the
agreement with in situ measurements as did the estimatesobta- minimal and maximal UPDs in the Level 3 composites. MODISA
λ
inedwithinsituR ( )(Fig.4b),howeveritsslopeandratiofellto data along the USCGC Healy cruise tracks during the ICESCAPE
rs
0.55 and 0.92. Despite the spectral brightening of blue MODIS campaigns further underscore the overestimation of C byOC3M
a
λ
R ( ), a (443)and a (443)fromGIOPalso attainedcomparable intheregionsofhighestCDOM(Fig.8).Thereismarkedcoherence
rs ph dg
metricstoestimatesobtainedusingGIOPwithinsituradiometry betweentheUPDbetweenOC3MandGIOPC anda (443)along
a dg
at MODISA wavelengths (Fig. 4c, d). Both in situ- and MODISA- the cruise tracks for both campaigns. The UPD between those
derived IOPs showed similar biases in comparison with field estimates trended towards null as a (443) decreased below
dg
measurements – specifically, ratios of 1.64 and 2.22 for a (443), values of 0.02m(cid:1)1, and reached values above 100% as a (443)
ph dg
and0.77and0.43fora (443).Notethatinallcases,thenumber 0.10m(cid:1)1.
dg
ofinsitusamples(Table1)exceededthosefromMODISA(Table2)
byapproximatelythree-foldduetothesmallnumberofsatellite-
insitumatchupsattainedduringbothcampaigns. 4. Discussion
MODISA Level 3 composites for both ICESCAPE campaigns
reiteratedthatCafromanempiricalapproach(OC3M)consistently Our observations of positively biased OC3M Ca relative to
exceeds that from a semi-analytical approach (GIOP) in the coincident in situ measurements are consistent with previous
western Arctic (Figs. 5a, b and 6a, b). For both 2010 and 2011, studiesthathaveevaluatedtheperformanceofempirical,globally
regions where such differences were most qualitatively obvious tuned ocean coloralgorithmsin the Arctic Ocean (Ben Mustapha
corresponded to regions with the highest CDOM (as inferred via etal.,2012;Cotaetal.,2004;Stramskaetal.,2003).Thesebiases
adg(443)).Forexample,considertheareasaroundtheBeringStrait were realized for both the MODISA and in situ Rrs(λ), which
Fig.4. MODISACa(A)andPOC(B);GIOPaph(443)(C)andadg(443)(D)usingMODISARrs(λ)versusinsitumeasuredvalues.SupportingTypeIIregressionstatisticsare
providedinTable2.
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J.E.Chavesetal./Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎ 7
Fig.5. Level3MODISAcompositesfortheregionofstudyduringICESCAPE2010:(A)OC3MCa;(B)GIOPCa;(C)GIOPadg(443);and(D)bbp(443).
Fig.6. Level3MODISAcompositesfortheregionofstudyduringICESCAPE2011:(A)OC3MCa;(B)GIOPCa;(C)GIOPadg(443);and(D)bbp(443).
implies the source of the bias was the bio-optical algorithm and in situ C from ICESCAPE fell well below the global relationship
a
notbytheobservedradiometry(Tables1and2,Figs.2and4).In employed by OC3M, which was derived using the NASA bio-
general,therelationshipbetweenMODISAandinsituMBRversus Optical Marine Algorithm data set (NOMAD; Werdell and Bailey,
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8 J.E.Chavesetal./Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎
Fig.7. Level3MODISAcompositesfortheunbiasedpercentdifference(UPD)betweenOC3MandGIOPCa,fortheICESCAPEcampaignsin(A)2010,and(B)2011.UPDwas
calculatedas200%*(OC3M-GIOP)/(OC3MþGIOP).Blackdenotespixelsflaggedforice,clouds,orsediments.
Fig.8. Unbiasedpercentdifference(UPD)betweenOC3MandGIOPCa,andadg(443),forMODISApixelscollectedalongthetrackoftheUSCGCHealyduringtheICESCAPE
2010(A),and2011(B)campaigns.Therighty-axisshowadg(443),whichisshowninredinbothpanels.Tickmarksaboveeachplotindicatethelocationsalongtrackwhere
insituASBobservationswereconducted.
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J.E.Chavesetal./Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎ 9
In the western Arctic Ocean, the OC3M algorithm appears to
λ
attribute CDOM absorption, a ( ), to phytoplankton absorption,
g
λ λ
a ( )(Matsuokaetal.,2007).Conceptually,excessa ( )(relative
ph g
to what would be derivative of only phytoplankton) produces
lower MBRs that result in overestimates of C (Fig. 9). In the
a
λ
westernArctic,wedonotexpecta ( )tocovarywithC asitdoes
dg a
in lower latitude and oligotrophic oceans (Siegel et al., 2002,
2005). High levels of CDOM in Arctic Ocean waters have been
widely documented (e.g., Brunelle et al., 2012; Granskog et al.,
2012; Guéguen et al., 2005; Matsuoka et al., 2013a, 2011, 2007;
Pegau,2002;Stedmonetal.,2011).Thebasinasawholereceives
the largest dissolved organic carbon input (and hence CDOM
input)fromrivers,relativetoitssize,thananyotheroceanbasin
λ
(Racholdetal.,2004).InthewesternArcticOcean,a ( )hasbeen
g
Fig.9. InsituHPLCCaversusMaximumBandRatio(MBR)frominsituradiometry reportedasmorethan50%ofthetotalnon-waterabsorptionatall
(blackcircles),andfromMODISA(crosses),forobservationscollectedduringthe
visible wavelengths, and up to 75% at 443nm (Brunelle et al.,
ICESCAPEscampaigns.ThegraypointsaretheobservationsintheNASABio-Optical
MarineAlgorithmDataSet(NOMAD;WerdellandBailey,2005)usedtoderivethe 2012;Matsuokaetal.,2007).
MODISAOC3Malgorithm(solidline). GIOP effectively estimated both phytoplankton and CDOM
absorption(Figs.2and4).LikemostSAAs,GIOPdoesnotassume
λ
Table3 covariance between adg( ) and Ca. The quality of the absorption
TypeIIregressionstatisticsforIOPsfromGIOPcalculatedusinginsituRrs(λ)at estimates and the demonstrateddeficiencies in OC3M Ca suggest
elevenwavelengths(adding395,465,510,560,and625nmtothesuiteofMODISA to a first order that SAAs provide more reliable data records for
λ)vs.insitumeasuredparameters.SeeTable1captionforadditionaldetails.
western Arctic studies than do existing empirical methods. GIOP
N r2 Slope(7SE) RMSE Ratio provided less variable estimates of aph(443) (r2¼0.85; Table 1)
than of C (r2¼0.72; Table 1). In principle, this suggests that the
a
Ca(GIOP) 32 0.69 0.65(0.07) 0.232 1.59 spectral shape of the Ca-specific absorption coefficient employed
aph(443) 33 0.87 1.16(0.08) 0.184 1.38 by GIOP (from Bricaud et al., 1998) is adequate for the western
adg(443) 30 0.92 1.03(0.05) 0.077 1.33 Arctic,butthatitsmagnitude(an (443)¼0.055m2mg(cid:1)1)isinsuf-
ph
ficient to unequivocally relate absorption to C at all times. The
a
2005) (Fig. 9). These depressed blue-to-green ratios of R (λ) mean observed an (443) for our two field campaigns mat-
rs ph
(relative to what has been observed on average globally) resulted ched the GIOP default (0.05470.025m2mg(cid:1)1), but ranged from
inpositivelybiasedC inthewesternArctic.Thisgeographicregion 0.009 to 0.102m2mg(cid:1)1. Studies that have measured an (λ) in
a ph
appears to be bio-optically unique, as the bulk of the ICESCAPE Arcticwatershavefoundstrongseasonalitycausedbychangesin
observations occupy a region of the C -MBR space that maintains “pigmentpackaging”associatedwiththeextremeseasonalvaria-
a
little or no overlap with the extent of the globally-distributed tion in light availability (Brunelle et al., 2012; Matsuoka et al.,
NOMADobservations,particularlyatC o0.5mgm(cid:1)3.Furthermore, 2011). Well-established interdependencies between an (λ) and
a ph
at the very low end of the dynamic range of OC3M (i.e., phytoplanktoncommunityindices(e.g.,C ,dominantcellsize)at
a
0.01oC o0.1mgm(cid:1)3), the measurements from ICESCAPE share lower latitudes (Ciotti et al., 2002), might be upended in Arctic
a
no commonality with any other major ocean basin (see Fig.1 in waters by the strong seasonality and its effect on phytoplankton
Szetoetal.,2011).If thebio-opticalsignaturesweobservedinthe physiology(Brunelleetal.,2012).
western Arctic Ocean waters are typical at basin-wide scales, Inpractice,futurestudieswithinterestinArcticphytoplankton
attempts to derive pan-Arctic satellite ocean color-based carbon dynamicsandproductionmightbenefitfromevaluatingabsorption
stockassessmentscouldrenderpotentiallylargeoverestimations. spectra in lieu of C abundances. Alternatively, when stocks and
a
λ
In contrast, the satisfactory performance of the Stramski POC abundancesarerequired,regionalrelationshipsbetweena ( )and
ph
λ
algorithm(Tables1and2,Figs.2and4)suggeststhatthedatasets C could be developed for application to the GIOP-derived a ( )
a ph
usedtocrafttheempiricalrelationshipsforthatalgorithmbetter following, for example, the approach presented in Werdell et al.
represent high latitude oceans. The fundamental relationships (2013c).AsufficientinsitudatasetexistsfromICESCAPEsuchthat
behind the bio-optical linkage between suspended particulate regional statistical relationships between a (443) and C can be
ph a
stocks (which include chlorophyll-bearing, live phytoplankton, developed (e.g., Bricaud et al., 1998; Werdell et al., 2013c). Our
heterotrophic organisms, as well as detrital particulates) are less limited in situ data set yielded the following best fit relationship
variabledue toadetritalportion thatismorehomogenousinits usingTypeIIlinearleastsquaresregression:
(cid:1) (cid:3)
bwieol-lowptiitchalthsiegnbautlukresc.aPtatertriicnlgecporonpceenrttireastioofnsiesaewxapteecrt(eed.gt.o,bcov(λa)r)y, log10ðCaÞ¼1:564nlog10 aphð443Þ þ2:283 ð6Þ
bp
especially in the region of the spectrum where particles absorb (r2¼0.92;RMSE¼0.171).ApplicationofEq.(6)totheGIOP-derived
weakly (Stramska and Stramski, 2005, and references therein), a (443) from MODISA yielded C match-ups with improved r2
ph a
whichincidentallyaretheregionsofthespectrum(i.e.,4500nm) (¼0.71 versus 0.65 in Table 2) and regression slopes (0.89 versus
where a is less important. The authors of the Stramski POC 0.44). However, the positive bias in MODISA-derived C remained
g a
algorithmnotedthat“[their]proposedbandratioalgorithmshave (ratioof3.77comparedto3.62)becauseofthepositivebiasrealized
similar regression coefficients to those previously determined intheMODISA-deriveda (443)(Fig.4).Weexpectthatadditional
ph
from selected historical data sets that included 205 data pairs regional tuning (e.g., Ben Mustapha et al., 2012) in subsequent
of POC and reflectance measured in several oceanic regions” studieswouldfacilitatefurtherreductionofthesebiases.
(Stramski et al., 2008). Moreover, the work that established the Two additional aspects of GIOP (representing all SAAs) merit
underlying bio-optical relationships between POC stocks and discussioninthecontextofusingitsMODISAdatarecordstostudy
water-leavingradianceswasderivedfromalargefractionoffield the western Arctic. First, as previously noted, MODISA R (λ)
rs
observationsacquiredintheSouthernOceanandthenorthpolar showed a Rayleigh-like spectral brightening in blue wavelengths
Atlantic(StramskaandStramski,2005;Stramskietal.,1999). as compared to in situ measurements, which we originally
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10 J.E.Chavesetal./Deep-SeaResearchII∎(∎∎∎∎)∎∎∎–∎∎∎
Fig.10. GIOP-derivedaph(443)(A)andadg(443)(B)using11wavelengths,ratherthanjustMODISAwavelengths,versusinsitumeasuredvalues.TypeIIregressionstatistics
presentedinTable3.
anticipatedwouldinfluencetheGIOPretrievals.Note,theelevated reflectances.Theresultingbio-opticalsignatureforthisregionhas
λ
R ( )didnotundulyaffecttheMBR(andthusOC3MandStramski not been well characterized in the datasets that support ocean
rs
POC) because the bands for its calculation (i.e., R (488) and coloralgorithmdevelopmentforcurrentoceancolormissions.The
rs
R (547))weretheleastaffectedbytheoverestimation(Table2). improved performance of the semi-analytical GIOP approach
rs
λ
Inpractice,thebrightenedR ( )didnotalterourGIOPresults.The in separating the in-water light absorbing components suggests
rs
spectral dependence of the brightening mirrored the spectral thatapathforwardforimprovedoceancolorapplications,which
absorption of CDOM (sequentially increasing with decreasing satisfies climate research needs, should rely more heavily on a
λ
wavelength). Given that R ( ) and absorption are inversely pro- mechanistic understanding of the phenomena that affect light
rs
λ
portional(MorelandPrieur,1977),erroneouslyelevatedblueR ( ) propagation in theArcticOcean.Forexample, futurestudies that
rs
conceptually produces an underestimation of CDOM in an SAA furthercharacterizeandusephytoplanktonabsorptioninsteadof
(i.e.,lessabsorbedbluelightsuggestslowCDOMabsorption).The justchlorophyll-aabundancewillhelpimproveIOP-to-C transfer
a
model-to-in situ ratios of 1.64 for a (443) and 0.77 for a (443) functions in semi-analytical algorithms. Likewise, better charac-
ph dg
support this theory. With this in mind, our demonstration that terization of Rayleigh-like spectral brightening of satellite data
OC3MoverestimatedC inregionsofhighCDOMremainsrelevant identified here suggests adjacency effects that merit further
a
and,ifanything,understated.Naturally,theinfluenceoficeedges review. Furthermore, the improved performance from app-
(Bélanger et al., 2007) – and how adjacency effects propagate lying additional spectral bands to semi-analytical algorithms
throughaRayleighatmosphereathighlatitudes–meritsfurther demonstrates the advantage of improved spectral resolution for
λ
investigation. Any brightening of satellite R ( ) bands relative to futureoceancolorsensorsthatwillbeappliedtoArcticandglobal
rs
in situ measurements is uncommon – the most frequently seen oceanresearch.
biasestendtowardsunder-estimation(BaileyandWerdell,2006).
That said, our interest was in evaluating derived biogeochemical
data records. Further analysis of MODISA atmospheric correction Acknowledgements
inthewesternArcticexceedsthescopeofthispaper.
Second,onemightexpecttheperformanceofGIOPtoimprove The NASAOcean Biologyand Biogeochemistry (OBB) program
withtheadditionofmoreR (λ)intheultraviolet-to-visiblespectral providedsupportfortheICESCAPEproject.Additionalsupportwas
rs
range. Future ocean color sensors, such as the NASA Pre-Aerosols, provided through an award to NASA GSFC for the Field Program
Clouds, and ocean Ecosystems (PACE) instrument, are expected to Support Group. We thank the officers and crew of the USCGC
collectR (λ)withadditionalspectralresolution(NationalResearch Healy for their logistical and technical support that made this
rs
Council,2011;PACEMissionScienceDefinitionTeam,2012).Using workpossible.Wethanktwoanonymousreviewersfortheirmany
fiveadditionalspectralbands(395,465,510,560,and625nm)from helpfulsuggestions.
the in situ radiometry – in addition to the MODISA bands at 412,
443,488,531,547,and667nm–toestimateGIOPaph(λ)andadg(λ) References
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Deep-SeaRes.II(2015),http://dx.doi.org/10.1016/j.dsr2.2015.02.011i