BIOCAREX Meeting Villefranche sur mer 24 January 2014 Retrieval of phytoplankton size classes from hyperspectral light absorption measurements WP7 Emanuele Organelli organelli@obs-vlfr.fr

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), 2257-2273. Partial Least Squares regression (PLS)

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 (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

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

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

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

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

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

Work to be done (1) Step 1: Inversion of in situ HYPER-spectral reflectances. A two-year time-series (2012-2013) 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 (399-600 nm with 3 nm increments) must be performed by comparison with in situ absorption data (CDOM + particles).

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 400- 700 nm but with 3 nm increments. To develop PLS models using in situ data with 1 nm increments but within the 400-600 nm range. To develop PLS models using in situ data with 3 nm increments within the 400-600 nm range (combination of 1 and 2). Comparison with PLS models (400-700 nm with 1 nm increments) already published (Organelli et al., 2013).

Work to be done (3) Step 3: Training PLS models basing on TOTAL light absorption measured in situ (399-600 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!!!!

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

...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

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.