Determination of phytoplankton groups from ocean color spectral measurements in the Senegalo-Mauritanian upwelling

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Determination of phytoplankton groups from ocean

color spectral measurements in the

Senegalo-Mauritanian upwelling

Ymane Taoufiq, Ousmane Farikou, Séverine Alvain, Julien Brajard, Michel

Crépon, Malick Ngom, Sylvie Thiria

To cite this version:

Ymane Taoufiq, Ousmane Farikou, Séverine Alvain, Julien Brajard, Michel Crépon, et al.. Determi-

nation of phytoplankton groups from ocean color spectral measurements in the Senegalo-Mauritanian

upwelling. Ocean Optics, The Oceanography Society (TOS), Oct 2014, Portland, Maine, United

States. �hal-01191676�

o ean olor spe tral measurements in the

Senegalo-Mauritanian upwelling

Ymane Taouq 1

, Ousmane Farikou 2

, Séverine Alvain 3

, Julien

Brajard 1,4

, Mi hel Crépon 1

, Mali k Ngom 1

, and Sylvie Thiria 1

1

Sorbonne Universités (UPMC, Univ Paris 06), CNRS, IRD, MNHN,

LOCEAN Laboratory, IPSL, 4 pla e Jussieu, F-75005, Paris, Fran e

2

Université Cheikh Anta Diop, Dakar, Sénégal

3

Laboratoire d'O eanologie et de Geos ien es, CNRS-ULCO-USTL,

Wimereux, Fran e

4

INRIA, Ro quen ourt, Fran e

20 o tobre 2014

1 Introdu tion

O ean olormeasurementshavebeenintensivelyusedtoestimate hlorophyll-a

on entration (Chl-a in abreviation) in the surfa e waters of the o ean, marginal

seas and lakes.

Phytoplankton is the rst element in the o ean food webs and onsequently

drivestheo eanprodu tivity.Italsoplaysafundamentalrolein limateregulation

by trappingatmospheri CO2through gas ex hanges atthe sea surfa e. Withthe

growing interest in limate hange, one may ask how the dierent phytoplankton

populationswillrespondto hangesino ean hara teristi s(temperature,salinity)

and nutrient supply.

Pigment analysis by High Performan e Liquid Chormatography (HPLC) has

been widelyused to ategorizedbroadPhytoplan ton size lasses (PSC)orphyto-

plan ton fun tional types (PFT) [Hirata et al., 2011℄. Ea h phytoplan ton group

(PSC/PFT) is asso iated with diagnosti pigmentsand a onvertion formula an

ments.

Thesein-situmeasurementswereusedtobuildrelationshipsbetweenPSC/PFT

and o ean properties that an be derived from satellite o ean olor sensors (e.g.

Chl-a on entration or water leaving-radian e), whi h is of fundamental interest

tounderstand the phytoplanktonbehavior andto modelitsevolution [Uitzetal.,

2006,Ciotti and Bri aud, 2006,Hirata et al., 2008,Sathyendranath et al., 2014,

Alvain etal.,2005,Alvainet al.,2012℄.

In the present work, we propose a regional algorithm based on PHYSAT [Al-

vain et al., 2012℄, that estimates diagnosti pigments asso iated with PFT/PSC

fromsatelliteo ean olormeasurements.Theregionofappli ationisthesenegalo-

mauritean upwelling and the results fo used on the relative on entration of Fu-

oxanthin (Fu o) whi h is the main diagnosti pigment for Mi roplankton (

>

20µm

^{)and Diatoms.}

2 Data

2.1 Satellite dataset

For this study, a satellite image ar hive of the senegalo-mauritean upwelling

(

8 ^o N

^-

24 ^o N

^,

14 ^o W − 20 ^o W

^{) obtained fromthe radiometerSeaWiFS wasused and}

the year 2003 was hosen to be atest ase of the algorithm.

Ea h data is a daily image of the water-leaving ree tan es (

ρ w

^{) at four wa-}

velengths (443nm, 490nm, 510nm and 555nm) and of the on entration of Chl-a

duringtheyear 2003.Theradian e at412nmwasnot retainedbe auseof thehigh

sensitivity of

ρ w

^{to olored dissolved organi matter (CDOM) at this} wavelength.

Due tothe presen e ofsaharandusts inthis region,very fewestimations of

ρ w

andChl-awere availableanditmaylead tostrongover-estimationsof hlorophyll-

a [Gregg and Casey, 2004℄. For that reason, an atmopsheri orre tion algorithm

dedi ated tosaharan dust [Dioufet al.,2013℄ wasused to obtaina urate

ρ w

^and

hl-adata.

As in [Ben Mustapha et al., 2014℄, the ree tan e ratio for ea h pixel was

omputed as follow:

Ra(λ) = ρ w (λ)/ρ wref (λ, chl − a)

⁽¹⁾

The on entration

ρ wref

^{depends on the Chl-a} on entration only.

ρ wref

^was

al ulated for Chl-a values observed by SeaWiFS in the studied region using a

multilayer per eptron whi h is a lass of arti ial neural network able to model

any non linear fun tion. This is a dieren e ompared to [Ben Mustapha et al.,

2014℄ who used tabulated values. This permits to have a smoother

ρ wref

^{fun tion}

even if the dependen y between

ρ wref

^{and Chl-a is not linear.}

The satellite dataset made of the

Ra(λ)

^{during the year 2003 is thereafter}

denoted DSAT.

2.2 The pigment dataset

Phytoplanktonpigmentsare ommonlyusedtodis riminatePSCandPFT[Hi-

rata etal., 2011℄.The strong hypothesis madein this work isthat the orrelation

between the satellite measurement (

ρ w

^{, Chl-a) and pigment} on entrations is not dependantonthelo ationorthedateofthemeasurement.Itmeansthatifasatel-

litemeasurement anbeasso iatedwithapigment on entrationinoneparti ular

pla e, the asso iationmust stay relevantanywhere and atanytimein the o ean.

Forthatreason,itwasde ided touse alargeinsitu dataset ompiledatglobal

s ale during the whole SeaWiFS period. This dataset was ollo ated with the

ρ w

and Chl-a measured by SeaWiFSdata [BenMustaphaetal., 2014℄.Some missing

data was ompleted using aself-organizing map te hni s [Junninen etal., 2004℄.

The pigment dataset, denoted DPIG, is omposed of 1068 variables. Ea h va-

riable isa 10-dimensionalve tor dened as :

 Component 1: hlorophyll-a on entration

 Component 2: divinyl hlorophyll-a on entration ratio

 Component 3: peridin on entration ratio

 Component 4: fu oxanthin on entrationratio

 Component 5: 19'hexanoyloxyfuxanthine on entration ratio

 Component 6: zeaxanthin on entrationratio

 Component7to10: SeaWiFSRaat4wavelengths : 443nm,490nm,510nm

and 555nm.

Thepigmentratioaredenedasin[Alvainetal.,2005℄bynormalizingthepigment

on entration by the Chl-a and divinyl hlorophyll-a on entration.

3 Method

The algorithmwasdivided intwophases.Therst phase onsistsin lustering

the DPIG dataset to retrieve the link between the ree tan e ration

Ra

^{and the}

pigment on entrationratio.The se ondphase onsistsinlabelingthe ree tan es

in DSAT interm of asso iated pigments.

The lusteringofDPIGwasdoneusingSelf-OrganizingMaps(herafterdenoted

SOM). The SOM [Kohonen, 1994℄ algorithmis a powerful non-linear lassier. It

aims at lusteringsamples ofamultidimensionaldataset(inour ase, DPIG) into

lasses represented by adedi ated network (the so- alledSOM map).

SOM is a neural lassier where ea h neuron of the map is asso iated with a

parti ularreferentve tor

V k

^{.ThedierentneuronsogtheSOMmapare onne ted}

together and determine a topologi al (neighbourhood) relationship between the

dierentneurons (subset ofsimilar data).

In the present ase, the SOM map is a two-dimensional (

13 × 12

^{) grid that}

represents the partition of the DPIG dataset. Ea h lass is asso iated with a so-

alled referent ve tor

V k

⁽

k = {1, 2, ...180}

^).

V k

^{are al ulated by a weigted mean}

ofelementsinDPIG. Therefore,

V k

^{has thesame dimensionasea helementofthe}

dataset(inour ase

V k ∈ R ¹⁰

^{)and ontains 5relativepigment} on entrationratio, the remote sensing hlorophyll-a on entration and

Ra

^at4 wavelengths.

At the end of the lustering, ea h element of the dataset DPIG is asso iated

withthe referent

V k

^{(denotedthe BestMat hing Unit)whi histhe losestinterm}

of the eu lidien distan e.

3.2 Labelisation

The labelisation phase onsists in asso iating ea h element of DSAT with a

pigment on entration ratio.

Ea h pixel

P j

^{of DSAT ontains the SeaWiFS}

Ra

^{at the four} wavelengths (443nm, 490nm, 510nm and 555nm). These elements an be dire tly ompared

with the omposants 7 to10 of the

V k

^{ve tor that represents}

Ra

^{value inDPIG.}

The problem is thus to determine the Best Mat hing Unit

V k

^{using only four}

values amongthe10usedintheSOM map.Atrun ateddistan e (TD)that onsi-

dereonlytheexistingvalueswasused.TheBestMat hingUnit

V k

^{wasdetermined}

using the TD.

Then,thepixel

P j

^{isdire tlyasso iatedwiththevaluesofthepigments on en-}

tration ratioof

V k

^.

With this method, ea h pixel

P j

^{is asso iated with 5 pigment} on entration ratios. The underlying assumption is that the link between ree tan es and pig-

ment ratios is the same in DPIG than for DSAT. In the following, we fo us on

the fu oxanthin on entration ratio whi h isa ara teristi ofdiatoms and mi ro-

planktons.

Chloro−a

n 0.0609

0.686

1.31

Rdiva

n 0.0103

0.162

0.313

Rperid

n 0.00915

0.0443

0.0794

Rfuco

n 0.0336

0.141

0.248

R19HF

n 0.0513

0.17

0.288

Rzeax

n 0.00738

0.179

0.351

Ra(443)

n 0.251

0.78

1.31

Ra(490)

n 0.434

0.868

1.3

Ra(510)

n 0.431

0.805

1.18

Ra(555)

n 0.383

0.955

1.53

Fig. 1  Representation of the value of the 7 omposants of

R k

^{on the Self-}

Organizing Map. Ea h image represent a omposant (6 pigments and 4

Ra

^{, ea h}

node ofthe imagerepresent a lass. Here the dimension of the map is

13 × 12

4.1 Labelisation of the ree tan e spe tra

In this se tion, the asso iation between

Ra

^{spe tra and the pigment on en-}

tration ratioare presented.

First,inFig.1,weshowthevaluesofallthe

V k

^{omponentsofthe}

13×12

^SOM

map. Ea h omponent was represented by the olor intensity of the grid point.It

an be noti e that values of the

V k

^{omponents were spatially well stru tured on}

SOM. Anotherimportantremark isthatthe values ofea h omponenthas alarge

range of variation of the same order as the range of variation of DPIG. It means

that the SOM map has aptured most of the variability of the dataset.

As ea h

V k

^{ontains a value for the pigment} on entration ratio, it is possible toestimateseveral typi alindexof PSC orPFT.It isanimportantfeature ofthis

algorithmthat doesnot estimateone parti ularPSC/PFT but asso iatesdire tly

pigment on entration ratiosthat an be used asproxies.

As an illustrationand a validationof the approa h, we an ompute the per-

entage of mi roplankton(Mi ro) following the formula[Hirata etal., 2011℄ :

Micro = 1.41 × (F uco + P erid)

⁽²⁾

where

F uco

^(resp.

P erid

^{) denotes the} on entration ratio of Fu oxanthin (resp.

Peridin).

In Fig. 2, we represent the per entage of mi roplankton ( al ulated using 2

with respe t with the hlorophyll-a on entration obtained for the referent ve -

tors

V k

^{. The} relationshipbetween the Chl-a on entration and the Mi roplan ton per entage is onsistent with the relationship found in [Hirata etal.,2011℄ :

Micro = [a ⁰ + exp(a ¹ log ¹⁰ (Chl − a) + a ² )] ^{− 1}

⁽³⁾

where

a ⁰ = 0.9117

^,

a ¹ = −2.7330

^and

a ² = 0.4003

^.

We annoti ethat,in omparisonwiththisglobalrelationship,theregionalre-

lationshipfound usingSOM overestimatesthemi roplanktonper entageforsmall

value of Chl-a (

< 0.2mg/m3

^{). It would need further analyses to nd if it is an}

artifa t of the algorithmor aregional spe i ity.

This is a rst validation of the orrelations found in the SOM map, and it

demonstrates the potentiallity of the approa h. The asso iation between remote

sensingree tan e spe tra andpigment on entrationsisane ient way toiden-

tify phytoplankton groups.

4.2 Labelisation of images

Usingthe trun ateddistan e (TD) des ribed inthe previous se tion,itis pos-

sible to asso iate a pixelof animage to areferent

V k

^{and thus to allthe pigment}

on entration ratios. In this work, we present results of this asso iation with the

Fu oxanthin. AllpixelsfromDSATwere asso iatedwiththe Fu oxanthin on en-

tration ratio. In g 3, the mean ratio for ea h sequen e of 3 months is presented

(January toMar h JFM, April toJune AMJ, July to Septembre JAS and nally

O tober toDe ember OND).

We an observethe seasonalvariability of the asso iated Fu oxanthin on en-

tration ratio, with a maximum on entration and a southernmost extent at the

beginning of the year. Knowing that high Fu oxanthin on entration ratio are

hara teristi of the presen e of diatoms, this seasonal variability is onsistent

with previous studies of the senegalo-mauritanean upwelling variability [Farikou

10 ⁻² 10 ⁻¹ 10 ⁰ 10 ¹

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Mikroplankton proportion

Chl−a concentration (mg/m3)

Fig. 2  Per entage of Mi roplankton following Eq. 2 with respe t with the

hlorophyll-a on entration. Ea h ir le represents a referent ve tor of the SOM

map and the solid bla k-line represents the relationship in Eq.3

et al., 2013,Lathuilière et al., 2008℄, and the observations done during the EU-

MELI ruises [Claustre and Marty, 1995℄ and the Atlanti Meridional Transe

(AMT) [Aiken etal.,2009℄.

5 Con lusion

A regional lassi ation te hnique derived from [Ben Mustapha et al., 2014℄

that asso iates ree tan e spe tra with pigment on entration ratio was develop-

ped andtestedduringthe year2003inthe senegalo-mauritanianandtheFu oxan-

thin on entration ratio. It was shown that results was oherent with relations

found in literature. It also allows to retrieve the seasonal variability of the as-

so iated Fu oxanthin on entration ratio. It is thus possible to have signi ant

several period : January to Mar h JFM, April to June AMJ, July to Septembre

JAS and nallyO tober toDe ember OND

quantitativeindi es ( on entration ratio) ofphytoplankton groups.

This approa h gives a lot of opportunity to study variability of pytoplankton

groups (PSC or PFT) in the region of the senegal-mauritanean upwelling. This

study ouldbe ontinued forother lassi ationgroupsandvalidatedusingin-situ

data in this region.

A knowledgement

Theauthorsa knowledgeNASA/GSFC/DAACforprovidinga esstodailyL2

SeaWiFSprodu ts.Thisworkwasfundedbythefren hspa eagen yCNES/Tos a

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