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Remote Assessment of Phytoplankton Functional Types from Ocean Color Data

Explore the use of particle size distribution retrieval to assess phytoplankton functions and types globally, focusing on the algorithms, uncertainty analysis, validation, and global climatology.

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Remote Assessment of Phytoplankton Functional Types from Ocean Color Data

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  1. Remote Assessment of Phytoplankton Functional Types Using Retrievalsof the Particle Size Distribution from Ocean Color Data Tihomir Kostadinov, David Siegel, Stéphane Maritorena ICESS, University of California Santa Barbara NASA Ocean Color Research Team Meeting, New Orleans, LA, May 12, 2010

  2. Outline • Introduction & Motivation • Kostadinov et al. (2009) PSD Algorithm • Algorithm theoretical basis & operation • Uncertainty analysis • Validation • Phytoplankton Functional Types Retrieval (Biogeosci. Disc., submitted) • Definition of PFT’s • Validation • Global climatology, seasonal succession

  3. Why PFT’s are Important • PFT’s are groups of phytoplankton with simil2耀 biology & biogeochemical roles, e.g.: • physiology • sinking • CO2 sequestration • DMS production • silicate drawdown • Cell SIZE • is a characteristic feature of PFT’s • determines structure and function of pelagic ecosystems • Global RS retrieval of the PFT’s is needed Chisholm, 2000

  4. PSD’s PFT Link • PSD’s  # & V in any size class • Case I assumption – particle load dominated by Chl and covariates • Size-defined PFT in terms of % volume = f(PSD parameters): • 3 classes – pico, nano, micro • definition does not explicitly take into account taxonomy/biology • Existing methods for PFT retrieval are based on HPLC pigments & Chl (e.g. Uitz, Alvain); phytoplankton absorption (e.g. Mouw, Devred)

  5. Describing the PSD Power-law Junge-type Size Distribution x = PSD slope Do = 2 mm No = N(Do), [m-4] Example PSD measured by LISST-100X 34o12.26’N 119o55.69’ W July 21, 2008 Santa Barbara Channel California x: 3.91 No: 16.7 m-4 log10 of

  6. Link to Optics - Mie Scattering Theory Single particle optical properties depend on: Complex index of refraction mr(l) = nr – i*nr’(l) Size relative to the incident wavelength Shape & internal composition Mie modeling solves the Maxwell equations for the IOP’s of homogeneous spherical particles Retrievable spectrally Goal of retrieval bbp(l) efficiency solved by Mie theory

  7. PSD Algorithm Scheme Theoretical LUT Development Operational Satellite Processing Retrieve spectral bbp(l) and its slope h from Rrs(l) via Loisel et al. (2006) Input Mie model parameters: x = 2.5 to 6 m(l) = n – m’(l)i Dmin; Dmax Use the LUT’s and bbp(440) & h maps to calculate algorithm base products: PSD slope = x N(2 mm) = No • Run Monte Carlo simulation of Mie model with various input combinations & • create two mean LUT’s: • x = f-1(h) • log10(bbp(440)/No) = g-1(h) Calculate derived products: Particle # & V in different size classes PFT’s

  8. Global bbp(440) and h Climatology

  9. Algorithm LUT’s

  10. Global x & No Climatology Mission mean of x (Sept. 1997 – Dec. 2007) Mission mean of No(Sept. 1997 – Dec. 2007) log10(particles*m-4)

  11. Endogenous Uncertainties • Due to Dmax and m • s(x) is small compared to its variability • s(log10(No)) higher, due to n

  12. PSD Validation w/ Coulter Counter N =22 Slope = 1.34 R2 = 0.24 N =22 Slope = 2.05 R2 = 0.26 SeaWiFS No SeaWiFS x In-situ No In-situ x • Regional validation uses GAC monthly data instead (N = 363): • OK for x, great for No!

  13. Partitioning Number Concentration Picoplankton, # m-3 (0.5 mm to 2 mm) Nanoplankton, # m-3 (2 mm to 20 mm) Microplankton, # m-3 (20 mm to 50 mm) log10(particles/m3) Pico’s vary ~100 times Nano’s vary ~ 10,000 times Micro’s vary ~ 106 times

  14. PFT’s Definition by % Volume • Partitioning by volume makes more sense • related to biomass, POC, living C • Three PFT’s quantitatively defined as % volume concentration contribution = f(x): • Picoplankton (0.5 – 2 mm equiv. sphere cell diameter) • Nanoplankton (2 – 20 mm) • Microplankton (20 – 50 mm)

  15. PFT’s = f(PSD slope)

  16. Partitioning Biovolume – the PFT’s Picoplankton % (0.5 mm to 2 mm) Nanoplankton % (2 mm to 20 mm) Microplankton % (20 mm to 50 mm) Pico’s dominate oligotrophic ocean (>90%) Nano’s in transition regions (~50%) Micro’s only found in upwelling zones & high latitudes (<60%)

  17. PFT Validation w/ HPLC Data N =48 Slope = 1.58 R2 = 0.34 • Uses in-situ HPLC diagnostic pigments (Vidussi et al., 2001) • Matched with daily SeaWiFS 9 km data. • Satisfactory for pico & micro, poor for nano. N =48 Slope = 1.01 R2 = 0.41 SeaWiFS % pico SeaWiFS % micro In-situ % pico In-situ % micro

  18. BATS Seasonal Succession

  19. BATS Seasonal Succession

  20. Conclusions First global assessment of PFT’s via the PSD from space Spatial patterns are consistent with current understanding Oligotrophic oceans have high PSD slopes, low abundances & are dominated by pico’s Bloom regions have lower PSD slope & are dominated by nano’s & micro’s Pico’s vary over few orders of magnitude, micro’s – over many. Seasonal succession and relationships to Chl-a are consistent with expectations

  21. Acknowledgements David Siegel, Stéphane Maritorena Funding from the NASA Ocean Biology & Biogeochemistry Program Mike Behrenfeld, Hubert Loisel, Emmanuel Boss, Curtis Mobley, Mary Jane Perry, Collin Roesler, Wayne Slade, Giorgio Dall’Olmo, Toby Westberry

  22. The End

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