1. Assessing Biodiversity of Phytoplankton Communities from Optical Remote Sensing Rick A. Reynolds, Dariusz Stramski, and Julia Uitz Scripps Institution of Oceanography University of California San Diego rreynolds@ucsd.edu NASA Biodiversity and Ecological Forecasting Team Meeting - October 2011

2. Project Objectives and Strategy Phytoplankton diversity in the world’s open oceans from optical remote sensing 1. Exploit current Chla- based approach 2. Explore the potential of hyperspectral approach 2

3. 3 Chla-based approaches  Describe general trends across various trophic regimes  But do not necessarily account for specific local conditions New complementary approaches need to be developed Explore the potential of hyperspectral optical measurement for discriminating different phytoplankton assemblages  Hyperspectral optical measurements have matured into powerful technologies in the field of remote sensing  Yet they remain largely unexplored for open ocean applications Motivation for Hyperspectral Approach

4. Data and Methods 4 Pilot study Small set of stations from Eastern Atlantic open ocean waters HPLC pigments Optical data  Measured hyperspectral IOPs  Measured multispectral R rs (λ)  Modeled hyperspectral R rs (λ) (Torrecilla et al., 2011, RSE) Polarstern ANT-23 cruise track

5. Input dataset Pigment composition Cluster analysis Reference classification Input dataset Optical measurements (e.g., Rrs (λ), aph(λ)…) Derivative analysis Cluster analysis 5 Similarity analysis (statistical indices) Evaluation of performance Similarity analysis (statistical indices) Evaluation of performance Data and Methods

6. 6 Dominant marker pigmentsStation Fuco ≈ MV-ChlbA DV-Chla > ZeaB DV-Chla ≈ ZeaC1, C2, C3, C4 19’-Hexfuco > ZeaD 19’-Hexfuco > FucoE Zea ≈ 19’-HexfucoF Pigment-derived classification provides 5 clusters Consistent with preliminary classification of stations based on 2 dominant marker pigments For example cluster analysis discriminates  Station E dominated by Fuco (diatoms) and Hex (prymnesiophytes)  Stations C1-C4 dominated by DV-Chla (prochlorophytes) and Zea (cyanobacteria and prochlorophytes) (Torrecilla et al., 2011, RSE) A Cluster tree based on pigments Classification Based on Pigment Composition

7. 7 (Torrecilla et al., 2011, RSE) Cluster analysis of phytoplankton absorption spectra provides similar classification as pigments Best results obtained when using 2 nd derivative of phytoplankton absorption spectra Next step is to determine how this result translates to R rs (λ) Dendrogram based on 2 nd derivative of a ph (λ) Classification Based on Phytoplankton Absorption

8. 8 Classification Based on Ocean Reflectance (Torrecilla et al., 2011, RSE) Classification derived from 3 band ratios of R rs traditionally used in ocean color does not provide good discrimination of stations Classification derived from 2 nd derivative of hyperspectral R rs provides highest similarity with pigment analysis A B Dendrogram based on 2 nd derivative of R rs (λ) Dendrogram based on 3 band ratios of R rs (λ)

9. 9 Conclusion Derivative analysis of hyperspectral phytoplankton absorption and ocean reflectance provides similar classification as pigments Initial results indicate significant potential of hyperspectral optical approach for  Discriminating different marine phytoplankton assemblages  Monitoring phytoplankton diversity in the ocean, especially under non-bloom conditions which are the most challenging

10. Estimation of total and class-specific primary production in the Mediterranean Sea (Uitz et al. in rev.) Demonstration of hyperspectral optical approach (Torrecilla et al. 2011, RSE) Completion of cruise covering a long south-to-north transect in the Atlantic  Collected a unique set of pigments and in situ hyperspectral optical data in a broad variety of oceanic regimes  Data being used to continue our investigations of hyperspectral optical approach 10 Work Completed for this Year Polarstern ANT-26 cruise track