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E manuele Organelli, Annick Bricaud, David Antoine and Julia Uitz

t he 45 th international Li è ge Colloquium 17 th May 2013. Retrieval of phytoplankton size classes from light absorption spectra using a multivariate approach. E manuele Organelli, Annick Bricaud, David Antoine and Julia Uitz

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E manuele Organelli, Annick Bricaud, David Antoine and Julia Uitz

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  1. the 45th international Liège Colloquium 17th May 2013 Retrieval of phytoplankton size classes from light absorption spectra using a multivariate approach Emanuele Organelli, Annick Bricaud, David Antoine andJulia 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

  2. Motivations • Toassess Total Primary Production in the oceans, newapproaches(Uitzet al., 2008, 2010, 2012) concern the estimation of PHYTOPLANKTON CLASS-SPECIFIC contributions. • Combination of ocean color-based PP models with algorithms for retrieving Phytoplankton Size Classes (PSC) from optical properties (IOPs and AOPs). Uitz et al. (2012), Glob. Biogeochem. Cycles, GB2024

  3. ClassificationofcurrentapproachesbyBrewinet al. (2011) 1. SpectralResponse-basedapproaches (based on differences in opticalsignaturesofphytoplanktongroups) 2. Abundance-basedapproaches (relywith the trophicstatus of the environment and the typeofphytoplankton) 3. Ecological-basedapproaches (based on the knowledgeofphysical and biological regime toidentifydifferenttypesofphytoplankton) Uncertainties and sources of errors! Brewin et al. (2011). Remote Sens. Environ., 115, 325-339

  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 satisfactoryresults(Moberget al., 2002; Stæhr and Cullen, 2003; Seppäla and Olli, 2008; Martinez-Guijarroet al., 2009). • PLS is a spectral response approach which uses light absorption properties. Bricaudet al. (2004), J. Geophys. Res., 109, C11010

  5. PLS: INPUT and OUTPUT Multivariate technique that relates, by regression, a data matrix of PREDICTORvariables to a data matrix of RESPONSE variables. 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) [Tchla] [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*[TChlb]+0.86*[Zea])a a Coefficients by Uitz et al. (2006). J. Geophys. Res., 111, C08005

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

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

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

  9. MedCAL-trainedmodels: TESTING MedCALap(λ)-models MedCALaphy(λ)-models R2=0.91 RMSE=0.17 R2=0.91 RMSE=0.17 R2=0.75 RMSE=0.14 R2=0.75 RMSE=0.13 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). R2=0.66 RMSE=0.12 R2=0.65 RMSE=0.12 • Good retrievals of Tchla, DP (not showed), Micro, Nano and Pico • Similar performances ofap(λ)and aphy(λ)trained models R2=0.54 RMSE=0.046 R2=0.52 RMSE=0.047

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

  11. Seasonal dynamics of algal sizestructure at BOUSSOLE Tchla 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 PLSmodels are trained with a global dataset... GLOCALdata 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=0.76 RMSE=0.03 R2=0.94 RMSE=0.11 R2=0.94 RMSE=0.10 R2=0.93 RMSE=0.08 R2=0.89 RMSE=0.06

  13. ...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 Tchla and DP • Overestimation of Micro • Underestimation of Nano and Pico R2=0.42 RMSE=0.044

  14. How to explaindifferences? • 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. Summary and Conclusions • 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 REGIONALdataset (MedCALand BOUSSOLE); • The dataset assembled from various locations in the World’s oceans (GLOCAL) gives satisfactory predictions of Tchla 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 (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!

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