Monitoring of Phytoplankton Functional Types in surface waters using ocean color imagery

Monitoring of Phytoplankton Functional Typesin surface waters using ocean color imagery

C. Moulin1, S. Alvain1,2, Y. Dandonneau3, L. Bopp1, H. Loisel2

• LSCE/IPSL, Gif-sur-Yvette, France• ELICO, Wimereux, France • LOCEAN/IPSL, Paris, France

[email protected]

PFT and the Ocean Carbon CycleP

ISC

ES

Annual mean Chl Annual mean frequency of diatom blooms

Recent global biogeochemical models account for more than one PFT to quantify the marine « biological pump » of CO2

Validation ?

SE

AW

IFS

?

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

2

2,2

2,4

2,6

2,8

3

400 420 440 460 480 500 520 540 560

0.04

0.07

0.1

0.2

0.3

4.0

Wavelenghts(nm)Nor

mal

ized

wat

er-le

avin

g ra

dian

ce

Chl a (mg.m-3)

nLwref(,Chl a)

Chl a, the main ocean color product

0.040.070.10.2

0.3

4.0

Our goal is to identify the Phytoplankton Functional Type (PFT)associated with Chl a

? ?? ??

Natural variability of nLw

Is it related to PFT (at least partly) ?

NOMAD

SEAWIFS nLw spectra and PFT

We looked for a correlation between anomalies of the SEAWIFS nLw spectrum and the dominant phytoplankton group.

Two steps:

1.Develop a normalization technique to remove the 1st order Chl a effect on the nLw spectrum and to evidence a 2nd order spectral variability.

2.Compare nLw* spectra with coincident in situ pigment inventories from the GeP&CO dataset (Dandonneau et al., 2004) to find relationships between nLw* and phytoplankton groups.

PHYSAT (Alvain et al., DSRI, 2005)

nLw*() = nLw()/nLwref(, Chl a)

The specific normalized water-leaving radiance, nLw*

nLw*()=

nLw()/nLwref(,Chla)

(nm) 0

0,20,40,60,81

1,21,41,61,82

2,22,42,62,83

400 420 440 460 480 500 520 540 560

0.04

0.07

0.1

0.2

0.3

4.0

nLwref(, Chl a)

555 nm

510 nm

490 nm

443 nm

412 nm

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

400 420 440 460 480 500 520 540 560

nLw

*

Wavelength

Coccoliths

TrichodesmiumDiatomsPhaeocystisCyanobacteriaProchlorococcusHaptophytes

- The GeP&CO dataset has allowed us to « identify » four groups (Diatoms, Prochlorococcus, Cyanobacteria and Haptophytes).

- Three additional groups (Phaeocystis, Coccoliths and Trichodesmium) have still to be validated.

Relationships between nLw* and PFT

The PHYSAT method

nLwobs and Chl-a SeaWiFS

nLw* = nLwobs / nLwref(Chl-a)

Identification of thedominant PFT for the pixel

Haptophytes-Prochlorococcus-Synechococcus-Diatoms

Daily Level-3 GAC data

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

400 420 440 460 480 500 520 540 560

nLw

*

Wavelength

Most frequent dominant PFT for the month

January 2001

PHYSAT 1997-2005 (Monthly Climatology)

Haptophytes - Prochlorococcus - SLC - Diatoms - Bloom Cocco. - Phaeocystis

January February

March April



May June

July August



September October

November December

Three dominant groups in the Global Ocean

HaptophytesProchlorococcusSLCDiatoméesPhaeocystis

Relative fraction of total chl-a for each dominant group

Prochlorococcus

Haptophytes

SLC

0.04

0.12

0.08

0.16

Diatoms (Relative fraction of total chl-a -

Global)

0.01

0.02

0.03

0.04

0.05

Phaeocystis (Relative fraction of total chl-a -Global)

Interannual variability of « blooming » PFTs

North Atlantic and Pacific blooms

June 2001

Austral Ocean bloom

Jan. 2001

The 1998 bloom of diatoms in the Equatorial Pacific

Equatorial Pacific Area

Effect of La Nina ?

July 1998

July 1999

HaptophytesProchlorococcusSLCDiatoméesPhaeocystis

Relative fraction of total chl-a for each dominant group

1.0

0.5

0.0

Conclusions

Major Phytoplankton Functional Types are associated with specific spectral signatures that can be detected from space.

PHYSAT results are globally OK, but further validation is needed (phaeocystis, coccolithophorids, trichodesmiums,…).

PHYSAT allows to monitor the seasonal and inter-annual variability of the distributions of major PFTs.

Diatoms and Phaeocystis are the major blooming PFTs in the Austral Ocean.

Define a bio-optical Algorithm for

Haptophytes and Diatoms only.

In situ Seabam nLwobs and Chl-a

PHYSAT

data labelized as Haptophytes, SLC,

Prochloroc. and Diatoms

Perspective (1): Improved bio-optical models

Alvain et al., DSRI, 2006

Perspectives (2): Model validationP

ISC

ES

Annual mean Chl Annual mean frequency of diatom blooms

?

SE

AW

IFS

Perspectives (3): Intercomparison of PFT’s algorithms

PHYSAT is not the only existing method to identify PFTs from space.

(but it is the only one that both relies on the analysis of the nLw spectrum and allows a global processing)

A recent IOCCG working group is dedicated to the comparison of existing PFT’s algorithms.

THE GEP&CO DATASET

20 pigments were measured daily (5 observations per day) during 12 GeP&CO cruises from France to New Caledonia between November 1999 and July 2002.

- Nov. 1999- Feb. 2000- May 2000- Aug. 2000- Oct. 2000- Feb. 2001

- Apr. 2001- Jul. 2001- Oct. 2001- Jan. 2002- Apr. 2002- Jul. 2002

http://www.lodyc.jussieu.fr/gepco/

Gep&CoShipping track

Phaeocystis and Diatoms in the Austral Ocean

Climatology of the mixed-layerDepth for January (Boyer Montégut et al., 2004).

PHYSAT January 2001

(diatoms, phaeocystis-like)

10 100 1000 m

Documents

Monitoring of Phytoplankton Functional Types in surface waters using ocean color imagery