monitoring of phytoplankton functional types in surface waters using ocean color imagery
DESCRIPTION
Monitoring of Phytoplankton Functional Types in surface waters using ocean color imagery C. Moulin 1 , S. Alvain 1,2 , Y. Dandonneau 3 , L. Bopp 1 , H. Loisel 2 LSCE/IPSL, Gif-sur-Yvette, France ELICO, Wimereux, France LOCEAN/IPSL, Paris, France [email protected]. ?. - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
?
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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
? ?? ??
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Natural variability of nLw
Is it related to PFT (at least partly) ?
NOMAD
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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)
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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
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0
0,5
1
1,5
2
2,5
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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
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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
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400 420 440 460 480 500 520 540 560
nLw
*
Wavelength
Most frequent dominant PFT for the month
January 2001
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PHYSAT 1997-2005 (Monthly Climatology)
Haptophytes - Prochlorococcus - SLC - Diatoms - Bloom Cocco. - Phaeocystis
January February
March April
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PHYSAT 1998-2004 (Monthly Climatology)
Haptophytes - Prochlorococcus - SLC - Diatoms - Bloom Cocco. - Phaeocystis
May June
July August
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PHYSAT 1998-2004 (Monthly Climatology)
Haptophytes - Prochlorococcus - SLC - Diatoms - Bloom Cocco. - Phaeocystis
September October
November December
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Three dominant groups in the Global Ocean
HaptophytesProchlorococcusSLCDiatoméesPhaeocystis
Relative fraction of total chl-a for each dominant group
Prochlorococcus
Haptophytes
SLC
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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
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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
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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.
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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
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Perspectives (2): Model validationP
ISC
ES
Annual mean Chl Annual mean frequency of diatom blooms
?
SE
AW
IFS
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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.
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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
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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