1. Retrieval of phytoplankton size classes from light absorption spectra using a multivariate approach Emanuele O RGANELLI, Annick B RICAUD, David A NTOINE and Julia U ITZ 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 45 TH INTERNATIONAL L IÈGE C OLLOQUIUM 17 TH M AY 2013

2. 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).

3. 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)

4. 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. Bricaud et al. (2004), J. Geophys. Res., 109, C11010

5. PLS: INPUT and OUTPUT INPUT VARIABLES Fourth-derivative of PARTICLE (a p (λ)) or PHYTOPLANKTON (a phy (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]) a a 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.

6. Plan of the work 1. INPUT and OUTPUT 2. TRAINING 3. TEST

7. REGIONAL data set for PLS training Data: HPLC pigment and light absorption (a p (λ) and a phy (λ)) measurements from the first optical depth. MedCAL data set (n=239): data from the Mediterranean Sea only

8. MedCAL-trained models  1 model each output variable  Models were trained including leave-one-out (LOO) cross-validation technique MedCAL a phy (λ)-modelsMedCAL a p (λ)-models R 2 =0.97 RMSE=0.10 R 2 =0.90 RMSE=0.10 R 2 =0.87 RMSE=0.08 R 2 =0.88 RMSE=0.02 R 2 =0.96 RMSE=0.11 R 2 =0.91 RMSE=0.11 R 2 =0.86 RMSE=0.08 R 2 =0.88 RMSE=0.02

9. 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). MedCAL a phy (λ)-modelsMedCAL a p (λ)-models R 2 =0.91 RMSE=0.17 R 2 =0.75 RMSE=0.14 R 2 =0.66 RMSE=0.12 R 2 =0.54 RMSE=0.046 R 2 =0.91 RMSE=0.17 R 2 =0.75 RMSE=0.13 R 2 =0.65 RMSE=0.12 R 2 =0.52 RMSE=0.047  Good retrievals of Tchl a, DP (not showed), Micro, Nano and Pico  Similar performances of a p (λ) and a phy (λ) trained models

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

11. 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

12. If PLS models are trained with a global dataset... GLOCAL data set (n=716): HPLC pigment and phytoplankton light absorption measurements (a phy (λ)) from various locations of the world’s oceans (Mediterranean Sea included). GLOCAL a phy (λ) Trained -models R 2 =0.94 RMSE=0.11 R 2 =0.93 RMSE=0.08 R 2 =0.89 RMSE=0.06 R 2 =0.76 RMSE=0.03 R 2 =0.94 RMSE=0.10

13. ...but when we test the models...  Good retrievals of Tchl a and DP  Overestimation of Micro  Underestimation of Nano and Pico GLOCAL a phy (λ)-models R 2 =0.42 RMSE=0.044 R 2 =0.48 RMSE=0.13 R 2 =0.70 RMSE=0.23 R 2 =0.91 RMSE=0.17 R 2 =0.93 RMSE=0.14

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.

15.  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 a p (λ) and a phy (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!)

16. 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!