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Retrieval of phytoplankton size classes from light absorption spectra using a
multivariate approach
Emanuele ORGANELLI, Annick BRICAUD, David ANTOINE and Julia UITZ
Laboratoire d’Océanographie de Villefranche, UMR 7093, CNRS and Université Pierre et Marie Curie, Paris 6, 06238 Villefranche sur Mer, FRANCE
THE 45TH INTERNATIONAL LIÈGE COLLOQUIUM17TH MAY 2013
Motivations
To assess Total Primary Production in
the oceans, new approaches (Uitz et
al., 2008, 2010, 2012) concern the
estimation of PHYTOPLANKTON
CLASS-SPECIFIC contributions.
Uitz et al. (2012), Glob. Biogeochem. Cycles, GB2024
Combination of ocean color-based PP
models with algorithms for retrieving
Phytoplankton Size Classes (PSC) from
optical properties (IOPs and AOPs).
Classification of current approaches by Brewin et al. (2011)
Uncertainties and sources of errors!Brewin et al. (2011). Remote Sens. Environ., 115, 325-339
1. Spectral Response-based approaches(based on differences in optical signatures of phytoplankton groups)
2. Abundance-based approaches(rely with the trophic status of the environment and the type of
phytoplankton)
3. Ecological-based approaches(based on the knowledge of physical and biological regime to identify
different types of phytoplankton)
Objective
To develop and test a new model for the retrieval of PSC using the multivariate Partial Least Squares regression (PLS) technique.
Scarcely applied in oceanography but with satisfactory results (Moberg et al., 2002; Stæhr and Cullen, 2003; Seppäla and Olli, 2008; Martinez-Guijarro et al., 2009).
PLS is a spectral response approach which uses light absorption properties.
0
0.02
0.04
0.06
0.08
0.1
0.12
400 450 500 550 600 650 700
a* p (m
2m
g T
Chl a
-1)
wavelength (nm)
Diatoms
Prymnesiophytes
Prasinophytes
Cyanobacteria
Prochlorococcus sp.
Bricaud et al. (2004), J. Geophys. Res., 109, C11010
PLS: INPUT and OUTPUT
INPUT VARIABLES
Fourth-derivative of
PARTICLE (ap(λ)) or
PHYTOPLANKTON (aphy(λ))
light absorption spectra
(400-700 nm, by 1 nm)
OUTPUT VARIABLES (in mg m-3)
[Tchl a]
[DP] ([Micro]+[Nano]+[Pico])
[Micro] (1.41*[Fuco]+1.41*[Perid])a
[Nano] (1.27*[19’-HF]+0.35*[19’-BF]
+0.60*[Allo])a
[Pico] (1.01*[TChl b]+0.86*[Zea])aa Coefficients by Uitz et al. (2006). J. Geophys. Res., 111, C08005
Multivariate technique that relates, by regression, a data matrix of
PREDICTOR variables to a data matrix of RESPONSE variables.
REGIONAL data set for PLS training
Data: HPLC pigment and light absorption (ap(λ) and aphy(λ))
measurements from the first optical depth.
MedCAL data set (n=239): data from the Mediterranean Sea only
MedCAL-trained models
1 model each output
variable
Models were trained
including leave-one-
out (LOO) cross-
validation technique
[Tchl a] measured(a)
0.0 1.0 2.0 3.0 4.0 5.0 6.0
[Tch
l a]
pred
icte
d
0.0
1.0
2.0
3.0
4.0
5.0
6.01:1
[Tchl a] measured0.0 1.0 2.0 3.0 4.0 5.0 6.0
[Tch
l a]
pred
icte
d
0.0
1.0
2.0
3.0
4.0
5.0
6.0
[Micro] measured0.0 0.5 1.0 1.5 2.0 2.5 3.0
[Mic
ro]
pred
icte
d
0.0
0.5
1.0
1.5
2.0
2.5
3.01:1
[Nano] measured0.0 0.5 1.0 1.5 2.0
[Nan
o] p
redi
cted
0.0
0.5
1.0
1.5
2.0
[Pico] measured0.0 0.1 0.2 0.3 0.4 0.5 0.6
[Pic
o] p
redi
cted
0.0
0.1
0.2
0.3
0.4
0.5
0.6
MedCAL aphy(λ)-models
[Micro] measured(e)
0.0 0.5 1.0 1.5 2.0 2.5 3.0
[Mic
ro]
pred
icte
d
0.0
0.5
1.0
1.5
2.0
2.5
3.01:1
[Nano] measured(g)
0.0 0.5 1.0 1.5 2.0
[Nan
o] p
redi
cted
0.0
0.5
1.0
1.5
2.0
[Pico] measured(i)
0.0 0.1 0.2 0.3 0.4 0.5 0.6
[Pic
o] p
redi
cted
0.0
0.1
0.2
0.3
0.4
0.5
0.6
MedCAL ap(λ)-models
R2=0.97RMSE=0.10
R2=0.90RMSE=0.10
R2=0.87RMSE=0.08
R2=0.88RMSE=0.02
R2=0.96RMSE=0.11
R2=0.91RMSE=0.11
R2=0.86RMSE=0.08
R2=0.88RMSE=0.02
MedCAL-trained models: TESTING
BOUSSOLE time-series (NW Mediterranean
Sea): monthly HPLC pigment and light
absorption measurements at the first optical
depth in the period January 2003-May 2011
(n=484).
[Tchl a] measured
0.01
0.1
1
[Tchl a] measured(a)
0.01 0.1 1
[Tch
l a]
pred
icte
d
0.01
0.1
1
1:1
MedCAL aphy(λ)-models
MedCAL ap(λ)-models
[Micro] measured(e)
0.0010.01 0.1 1
[Mic
ro]
pred
icte
d
0.001
0.01
0.1
1
1:1
[Nano] measured(g)
0.0010.01 0.1 1
[Nan
o] p
redi
cted
0.001
0.01
0.1
1
1:1
[Pico] measured(i)
0.0010.01 0.1 1
[Pic
o] p
redi
cted
0.001
0.01
0.1
1
1:1
[Micro] measured0.001
0.01 0.1 1
0.001
0.01
0.1
1
1:1
[Nano] measured0.001
0.01 0.1 1
0.001
0.01
0.1
1
1:1
[Pico] measured0.001
0.01 0.1 1
0.001
0.01
0.1
1
1:1
R2=0.91RMSE=0.17
R2=0.75RMSE=0.14
R2=0.66RMSE=0.12
R2=0.54RMSE=0.046
R2=0.91RMSE=0.17
R2=0.75RMSE=0.13
R2=0.65RMSE=0.12
R2=0.52RMSE=0.047 Good retrievals of Tchl a, DP (not
showed), Micro, Nano and Pico
Similar performances of ap(λ) and
aphy(λ) trained models
Seasonal dynamics of algal size structure at BOUSSOLE
Tchl a
Max in Spring bloom (from mid-March to mid-
April)
Low concentrations from June to October
Increase in Winter
Micro-phytoplankton
Max in Spring bloom (from mid-March to mid-
April)
Low concentrations during the rest of the year
Nano- and Pico-phytoplankton
Recurrent maximal abundance in late Winter
and early Spring
Increase in Summer and from October to
December
If PLS models are trained with a global dataset...
GLOCAL data set (n=716): HPLC pigment and phytoplankton light absorption measurements (aphy(λ)) from various locations of the
world’s oceans (Mediterranean Sea included).
[Pico] measured(e)
-0.1 0.0 0.1 0.2 0.3 0.4 0.5
[Pic
o] p
redi
cted
-0.1
0.0
0.1
0.2
0.3
0.4
0.51:1
[Nano] measured(d)
0.0 0.5 1.0 1.5 2.0
[Nan
o] p
redi
cted
0.0
0.5
1.0
1.5
2.01:1
[Tchl a] measured(a)
0.0 1.0 2.0 3.0 4.0 5.0 6.0
[Tch
l a]
pred
icte
d
0.0
1.0
2.0
3.0
4.0
5.0
6.01:1
[Micro] measured
0.0 1.0 2.0 3.0 4.0
[Mic
ro]
pred
icte
d
0.0
1.0
2.0
3.0
4.01:1
[DP] measured
0.0 1.0 2.0 3.0 4.0 5.0
[DP
] p
redi
cted
0.0
1.0
2.0
3.0
4.0
5.01:1
[Tchl a] measured(a)
0.0 1.0 2.0 3.0 4.0 5.0 6.0
[Tch
l a]
pred
icte
d
0.0
1.0
2.0
3.0
4.0
5.0
6.01:1
GLOCAL aphy(λ) Trained -models
R2=0.94RMSE=0.11
R2=0.93RMSE=0.08
R2=0.89 RMSE=0.06
R2=0.76RMSE=0.03
R2=0.94RMSE=0.10
...but when we test the models...
Good retrievals of Tchl
a and DP
Overestimation of
Micro
Underestimation of
Nano and Pico
GLOCAL aphy(λ)-models
[Tchl a] measured0.001
0.01 0.1 1
[Tch
l a]
pred
icte
d
0.001
0.01
0.1
1
1:1
[DP] measured
[DP
] pr
edic
ted
0.01
0.1
1
[Pico] measured0.001
0.01 0.1 1
[Pic
o] p
redi
cted
0.001
0.01
0.1
1
1:1
[Micro] measured[M
icro
] pr
edic
ted
0.001
0.01
0.1
1
[Nano] measured0.001
0.01 0.1 1
[Nan
o] p
redi
cted
0.001
0.01
0.1
1
1:1
R2=0.42RMSE=0.044
R2=0.48RMSE=0.13
R2=0.70RMSE=0.23
R2=0.91RMSE=0.17
R2=0.93RMSE=0.14
How to explain differences?
Amplitude and center
wavelength of absorption
bands in the fourth–
derivative spectra at the
BOUSSOLE site are:
Similar to those of the
other Mediterranean
areas.
Different to those of the
Atlantic and Pacific
Oceans.
The PLS approach gives access to the analysis of SEASONAL DYNAMICS of
algal community size structure using optical measurements (absorption).
Retrieval of algal biomass and size structure from in vivo hyper-spectral
absorption measurements can be achieved by PLS:
High prediction accuracy when PLS models are trained and tested with a
REGIONAL dataset (MedCAL and BOUSSOLE);
The dataset assembled from various locations in the World’s oceans
(GLOCAL) gives satisfactory predictions of Tchl a and DP only.
Summary and Conclusions
Main advantage of PLS approach is the INSENSITIVITY of the fourth-
derivative to NAP and CDOM (new analyses reveal it!) absorption
properties that means:
Prediction ability is very similar for ap(λ) and aphy(λ) PLS trained models
This opens the way to a PLS application to total absorption spectra
derived from inversion of field or satellite hyperspectral radiance
measurements (this is currently being tested over the BOUSSOLE time
series!)
Citation: Organelli E., Bricaud A., Antoine D., Uitz J. (2013). Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site). Applied Optics, 52(11), 2257-2273.
Acknowledgements: This study is a contribution to the BIOCAREX (funded by ANR) and BOUSSOLE (funded by ESA, NASA, CNES, CNRS, INSU, UPMC, OOV) projects.
Many thanks to the
BOUSSOLE team!
Thank you for the
attention!