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)

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.

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]

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

Boussole time-series from MedCAL-trained models

Micro

Nano

Pico

Tchl a

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!