Assessing Biodiversity of Phytoplankton Communities from Optical Remote Sensing

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

3. Motivation for Hyperspectral Approach • 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

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

5. Data and Methods Similarity analysis (statistical indices) Evaluation of performance

6. Classification Based on Pigment Composition Cluster tree based on pigments A • 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)

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

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

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. Work Completed for this Year • 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 Polarstern ANT-26 cruise track