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Evaluating two approaches to the Bio-Optical Model for Coastal Waters

Evaluating two approaches to the Bio-Optical Model for Coastal Waters. Mimi Szeto Research and Discover Fellow Candidate for UNH MS, Oceanography Advisor: Dr. Janet W. Campbell. Background: Primary Productivity. Phytoplankton. Primary Productivity. Photosynthesis.

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Evaluating two approaches to the Bio-Optical Model for Coastal Waters

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  1. Evaluating two approaches to the Bio-Optical Model for Coastal Waters Mimi Szeto Research and Discover Fellow Candidate for UNH MS, Oceanography Advisor: Dr. Janet W. Campbell

  2. Background: Primary Productivity Phytoplankton Primary Productivity Photosynthesis National Oceanography Centre, Southhampton

  3. Marine Ecosystems Fisheries Oak Ridge National Library Background: The Big Picture The Global Carbon Cycle NOAA MIT

  4. Ocean Color Algorithms The OC4.v4 and OC3M Algorithms at work The SeaWiFS Sensor NASA SeaWiFSProject NASA Bio-Optical Relationship Light <=> Phytoplankton (absorption and scattering) NASA

  5. Ocean Color Algorithms The OC4.v4 and OC3M Algorithms at work The SeaWiFS Sensor NASA SeaWiFSProject NASA Bio-Optical Relationship Light <=> Phytoplankton Inherent Optical Properties (IOP) Apparent Optical Properties (AOP) (absorption and scattering) NASA

  6. AOP Rrs IOP absorption and scattering IN-WATER CONSTITUENTS chlorophyll Bio-Optical Models Semi-Analytical Algorithms Empirical Algorithms

  7. AOP IOP chl Objective: Comparing two approaches to the Bio-Optical Model • Underdog Model: Sydor, 2007 • Popular Model: Gordon, 1988 KEY: IOPs: a = absorption bb= backscattering b=scattering AOP: Rrs= Remote Sensing Reflectance

  8. AOP IOP chl Method • Three different in situ data sets for comparing and sensitivity analysis: • COOA- Gulf of Maine • CalCOFI – Monterey Bay, California • BBOPs – Bermuda • Simulating Datasets: • HYDROLIGHT • in situIOPs  modeled rrs • Closure

  9. What I have so far! • Compare plots of in situ data • Rrs vs bb /(a+bb) and Rrs vs b/a • Run COOA IOPs in Hydrolight • Current Problem: too many parameters • WINDSPEED

  10. Showdown: Gordon vs. Sydor Wavelengths 412 443 490 510 555 670

  11. Recap. and Overall Purpose • AOP – IOP : Foundation of semi-analytical algorithms • Main goal: • Reduce uncertainty incurred in ocean color algorithms for coastal regions. • Improve estimate of PRIMARY PRODUCTIVITY AOP IOP chl

  12. Acknowledgements • Janet Campbell, Ph. D. • Tim Moore, Ph. D. • Research and Discover Fellowship • Jeremy Werdell, GSFC • Joe Salisbury, Ph. D. • Ru Morrison, Ph. D. • Doug Vandemark, Ph. D. • Amanda Plagge, Deb Goodwin, and Shivanesh Rao • COOA and NASA Ocean Color Processing Group

  13. Problem with Wind Speed • Example of one station at different depths.

  14. Objective: Comparing two approaches to the Bio-Optical Model • Component: Sydor, 2007 • Multiple scattering Underdog Equation AOP: Remote sensing Reflectance IOPs: a = absorption bb= backscattering

  15. AOP IOP chl IOPSIn-Water Constituents Remote Sensing Equation ABSORPTION water plankton CDOM and non-algal particles BACKSCATTERING water particles

  16. AOP IOP chl IOPSIn-Water Constituents a or bb = [magnitude] x (spectral shape) WATER CDOM AND NAP PLANKTON 2.5 1.0 0.008 2.0 0.8 0.006 S a*(l) 0.6 1.5 Absorption 0.004 0.4 1.0 0.002 0.2 0.5 0.0 0.000 0.0 600 400 500 600 700 400 500 700 400 500 600 700 Wavelengths Wavelengths Wavelengths

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