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Remote Assessment of Remote Assessment of Phytoplankton Functional TypesPhytoplankton Functional Types
Using RetrievalsUsing Retrievalsof the of the Particle Size DistributionParticle Size Distribution
from Ocean Color Datafrom Ocean Color Data
Tihomir Kostadinov, David Siegel, Stéphane MaritorenaTihomir Kostadinov, David Siegel, Stéphane Maritorena
ICESS, University of California Santa BarbaraICESS, University of California Santa Barbara
NASA Ocean Color Research Team Meeting,NASA Ocean Color Research Team Meeting,
New Orleans, LA, May 12, 2010New Orleans, LA, May 12, 2010
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OutlineOutline
• Introduction & Motivation• Kostadinov et al. (2009) PSD Algorithm
– Algorithm theoretical basis & operation– Uncertainty analysis– Validation
• Phytoplankton Functional Types Retrieval (Biogeosci. Disc., submitted)
– Definition of PFT’s– Validation– Global climatology, seasonal succession
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Why PFT’s are ImportantWhy PFT’s are Important• PFT’s are groups of
phytoplankton with simil2耀 biology & biogeochemical roles, e.g.:– physiology– sinking – CO2 sequestration– DMS production– silicate drawdown
• Cell SIZE– is a characteristic feature of
PFT’s– determines structure and
function of pelagic ecosystems
• Global RS retrieval of the PFT’s is needed
Chisholm, 2000
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PSD’s PSD’s PFT LinkPFT Link• PSD’s # & V in any size class• Case I assumption – particle load dominated by
Chl and covariates• Size-defined PFT in terms of % volume = f(PSD
parameters):– 3 classes – pico, nano, micro– definition does not explicitly take into account
taxonomy/biology
• Existing methods for PFT retrieval are based on HPLC pigments & Chl (e.g. Uitz, Alvain); phytoplankton absorption (e.g. Mouw, Devred)
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Describing the PSDDescribing the PSDPower-law Junge-type
Size Distribution
= PSD slope
Do = 2 m
No = N(Do), [m-4]
oo D
DNDN )(
Example PSD measured by LISST-100X
July 21, 2008Santa Barbara Channel
California
: 3.91No: 16.7 m-4
log1
0 o
f
34o12.26’N 119o55.69’ W
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Link to Optics - Mie Scattering TheoryLink to Optics - Mie Scattering Theory
• Single particle optical properties depend on:– Complex index of refraction mr() = nr – i*nr’()
– Size relative to the incident wavelength– Shape & internal composition
• Mie modeling solves the Maxwell equations for the IOP’s of homogeneous spherical particles
dDD
DNmDQDb
oobb
D
D
bp
),,(
4)(
max
min
2
Retrievable spectrallyGoal of retrievalbbp() efficiency solved by Mie theory
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PSD Algorithm SchemePSD Algorithm Scheme
Use the LUT’s and bbp(440) & maps to calculate algorithm base products:
PSD slope = N(2 m) = No
Calculate derived products: Particle # & V in different size classes
PFT’s
Input Mie model parameters: = 2.5 to 6
m() = n – m’()i Dmin; Dmax
Run Monte Carlo simulation of Mie model with various input
combinations & create two mean LUT’s:
= f-1() log10(bbp(440)/No) = g-1()
Operational Satellite Processing
Retrieve spectral bbp() and its slope from Rrs() via Loisel et al. (2006)
Theoretical LUT Development
oobpbp bb )()(
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Global bGlobal bbpbp(440) and (440) and ClimatologyClimatology
oobpbp bb )()(
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Algorithm LUT’sAlgorithm LUT’s
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log10(particles*m-4)
Mission mean of (Sept. 1997 – Dec. 2007)
Mission mean of (Sept. 1997 – Dec. 2007)
Global Global & & NNoo Climatology Climatology
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Endogenous UncertaintiesEndogenous Uncertainties
•Due to Dmax and m
• () is small compared to its variability
• (log10(No)) higher, due to n
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PSD Validation w/ Coulter CounterPSD Validation w/ Coulter Counter
Regional validation uses GAC monthly data instead (N = 363):• OK for , great for No!
In-situ
Sea
WiF
S
In-situ S
eaW
iFS
N =22Slope = 1.34 R2 = 0.24
N =22Slope = 2.05 R2 = 0.26
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Partitioning Number ConcentrationPartitioning Number Concentration
Picoplankton, # m-3 (0.5 m to 2 m)
Microplankton, # m-3 (20 m to 50 m)
Nanoplankton, # m-3 (2 m to 20 m)
Pico’s vary ~100 times
Nano’s vary ~ 10,000 times
Micro’s vary ~ 106 times
log10(particles/m3)
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PFT’s Definition by % VolumePFT’s Definition by % Volume
• Partitioning by volume makes more sense– related to biomass, POC, living C
• Three PFT’s quantitatively defined as %
volume concentration contribution = f():– Picoplankton (0.5 – 2 m equiv. sphere cell
diameter)– Nanoplankton (2 – 20 m)– Microplankton (20 – 50 m)
max
min
3
6
D
D oo dDD
DNDV
(_ 4min
4max
4min
4max f
DD
DDPFTPBv PFTPFT
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PFT’s = f(PSD slope)PFT’s = f(PSD slope)
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Partitioning Biovolume – the PFT’sPartitioning Biovolume – the PFT’sPicoplankton % (0.5 m to 2 m)
Microplankton % (20 m to 50 m)
Nanoplankton % (2 m to 20 m)
Pico’s dominate oligotrophic ocean (>90%)
Nano’s in transition regions (~50%)
Micro’s only found in upwelling zones & high latitudes (<60%)
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PFT Validation w/ HPLC DataPFT Validation w/ HPLC Data
• Uses in-situ HPLC diagnostic pigments (Vidussi et al., 2001)• Matched with daily SeaWiFS 9 km data.• Satisfactory for pico & micro, poor for nano.
N =48Slope = 1.58 R2 = 0.34
N =48Slope = 1.01 R2 = 0.41
Sea
WiF
S %
pic
o
Sea
WiF
S %
mic
ro
In-situ % pico In-situ % micro
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BATS Seasonal SuccessionBATS Seasonal Succession
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BATS Seasonal SuccessionBATS Seasonal Succession
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ConclusionsConclusions• First global assessment of PFT’s via the
PSD from space• Spatial patterns are consistent with current
understanding– Oligotrophic oceans have high PSD slopes,
low abundances & are dominated by pico’s– Bloom regions have lower PSD slope & are
dominated by nano’s & micro’s– Pico’s vary over few orders of magnitude,
micro’s – over many.
• Seasonal succession and relationships to Chl-a are consistent with expectations
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AcknowledgementsAcknowledgements
• David Siegel, Stéphane Maritorena
• Funding from the NASA Ocean Biology & Biogeochemistry Program
• Mike Behrenfeld, Hubert Loisel, Emmanuel Boss, Curtis Mobley, Mary Jane Perry, Collin Roesler, Wayne Slade, Giorgio Dall’Olmo, Toby Westberry
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The EndThe End