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*organelli@obs-vlfr.fr

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)

ObjectiveTo 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 VARIABLESFourth-derivative ofPARTICLE (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.

Plan of the work

1. INPUT and

OUTPUT

2. TRAINING 3. TEST

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] p

redi

cted

0.01.02.03.04.05.06.0

1:1

[Tchl a] measured0.0 1.0 2.0 3.0 4.0 5.0 6.0

[Tch

l a] p

redi

cted

0.01.02.03.04.05.06.0

[Micro] measured0.0 0.5 1.0 1.5 2.0 2.5 3.0

[Mic

ro] p

redi

cted

0.00.51.01.52.02.53.0

1:1

[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

0.00.10.20.30.40.50.6

MedCAL aphy(λ)-models

[Micro] measured(e)

0.0 0.5 1.0 1.5 2.0 2.5 3.0

[Mic

ro] p

redi

cted

0.00.51.01.52.02.53.0

1:1

[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

0.10.20.30.40.50.6

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] p

redi

cted

0.01

0.1

1

1:1

MedCAL aphy(λ)-models

MedCAL ap(λ)-models

[Micro] measured(e)

0.001 0.01 0.1 1

[Mic

ro] p

redi

cted

0.001

0.01

0.1

1

1:1

[Nano] measured(g)

0.001 0.01 0.1 1[N

ano]

pre

dict

ed

0.001

0.01

0.1

1

1:1

[Pico] measured(i)

0.001 0.01 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

Boussole time-series from MedCAL-trained models

Micro

Nano

Pico

Tchl a

Seasonal dynamics of algal size structure at BOUSSOLE

Tchl aMax in Spring bloom (from mid-March to mid-April)Low concentrations from June to OctoberIncrease in Winter

Micro-phytoplanktonMax in Spring bloom (from mid-March to mid-April)Low concentrations during the rest of the year

Nano- and Pico-phytoplanktonRecurrent maximal abundance in late Winter and early SpringIncrease 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

-0.10.00.10.20.30.40.5

1:1

[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] p

redi

cted

0.01.02.03.04.05.06.0

1:1

[Micro] measured0.0 1.0 2.0 3.0 4.0

[Mic

ro] p

redi

cted

0.0

1.0

2.0

3.0

4.01:1

[DP] measured0.0 1.0 2.0 3.0 4.0 5.0

[DP]

pre

dict

ed

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] p

redi

cted

0.01.02.03.04.05.06.0

1:1

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] p

redi

cted

0.001

0.01

0.1

1

1:1

[DP] measured

[DP]

pre

dict

ed

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

] pre

dict

ed

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!