1. BIOCAREX Meeting
Villefranche sur mer
24 January 2014
Retrieval of phytoplankton size classes from hyperspectral light absorption measurements
WP7
Emanuele Organelli

2. Objective and First Output
Exploiting hyper-spectral measurements of optical properties to identify changes in the phytoplankton community structure at the BOUSSOLE site.
Published paper:
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),
Partial Least Squares regression (PLS)

3. OUTPUT VARIABLES (in mg m-3)
PLS: INPUT and OUTPUT
Multivariate technique that relates, by regression, a data matrix of PREDICTOR variables to a data matrix of RESPONSE variables.
INPUT VARIABLES
Fourth-derivative of
PARTICLE (ap(λ)) or PHYTOPLANKTON (aphy(λ))
light absorption spectra
( 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])a
a Coefficients by Uitz et al. (2006). J. Geophys. Res., 111, C08005

4. Plan of the work 1. INPUT and OUTPUT 2. TRAINING 3. TEST
Organelli et al. (2013)
1. INPUT and
OUTPUT
2. TRAINING
3. TEST

5. 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

6. MedCAL-trained models
MedCAL aphy(λ)-models
MedCAL ap(λ)-models
R2=0.97
RMSE=0.10
R2=0.90
R2=0.87
RMSE=0.08
R2=0.88
RMSE=0.02
R2=0.96
RMSE=0.11
R2=0.91
R2=0.86
1 model each output variable
Models were trained including leave-one-out (LOO) cross-validation technique

7. MedCAL-trained models: TESTING
MedCAL aphy(λ)-models
MedCAL ap(λ)-models
R2=0.91
RMSE=0.17
R2=0.75
RMSE=0.14
R2=0.66
RMSE=0.12
R2=0.54
RMSE=0.046
RMSE=0.13
R2=0.65
R2=0.52
RMSE=0.047
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).
Good retrievals of Tchl a, DP (not showed), Micro, Nano and Pico
Similar performances of ap(λ) and aphy(λ) trained models

8. Boussole time-series from MedCAL-trained models
Tchl a
Micro
Nano
Pico

9. Seasonal dynamics of algal size structure at BOUSSOLE
Tchl a
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

10. Summary
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.
The PLS approach gives access to the analysis of SEASONAL DYNAMICS of algal community size structure using optical measurements (absorption).
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

11. Work to be done (1)
Step 1:
Inversion of in situ HYPER-spectral reflectances. A two-year time-series ( ) of radiometric measurements collected at high-frequency (every 15 min) by the buoy at BOUSSOLE is available for inversion. Validation of retrieved TOTAL light absorption spectra ( nm with 3 nm increments) must be performed by comparison with in situ absorption data (CDOM + particles).

12. Work to be done (2)
Step 2:
Test performances of PLS models when spectral resolution is reduced. It can be performed with particulate absorption spectra.
To develop PLS models using in situ data within the range nm but with 3 nm increments.
To develop PLS models using in situ data with 1 nm increments but within the nm range.
To develop PLS models using in situ data with 3 nm increments within the nm range (combination of 1 and 2).
Comparison with PLS models ( nm with 1 nm increments) already published (Organelli et al., 2013).

13. Work to be done (3)
Step 3:
Training PLS models basing on TOTAL light absorption measured in situ ( nm with 3 nm increments).
Step 4:
Application of the NEW PLS models on the total light absorption spectra retrieved from inversion of hyper-spectral reflectances (Step 1).
MERCI!!!!

14. 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).
GLOCAL aphy(λ) Trained -models
R2=0.94
RMSE=0.11
R2=0.93
RMSE=0.08
R2=0.89 RMSE=0.06
R2=0.76
RMSE=0.03
RMSE=0.10

15. ...but when we test the models...
GLOCAL aphy(λ)-models
R2=0.91
RMSE=0.17
R2=0.93
RMSE=0.14
R2=0.70
RMSE=0.23
R2=0.48
RMSE=0.13
Good retrievals of Tchl a and DP
Overestimation of Micro
Underestimation of Nano and Pico
R2=0.42
RMSE=0.044

16. 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.