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Remote sensing applications in Oceanography: How much we can see using ocean color?

Remote sensing applications in Oceanography: How much we can see using ocean color?. Martin A Montes Ph.D Rutgers University Institute of Marine and Coastal Sciences. Spring 2008. Main topics. Introduction: definitions, sensor characteristics Model development:

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Remote sensing applications in Oceanography: How much we can see using ocean color?

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  1. Remote sensing applications in Oceanography: How much we can see using ocean color? Martin A Montes Ph.D Rutgers University Institute of Marine and Coastal Sciences Spring 2008

  2. Main topics Introduction: definitions, sensor characteristics Model development: IOP’s, AOP’s, Forward and Inversion approach Applications: chl, phytoplankton size structure

  3. Ocean color sensors Definition: Types: Passive vs Active Sensor characteristics: swath, footprint, revisiting time, spectral resolution

  4. ‘Atmospheric windows’

  5. First sensors: B& W Ocean color sensors: characteristics • Spectral resolution: • number of channels?, bandwidth? • Temporal resolution: • revisiting time?

  6. Ocean color sensors: characteristics http://www.ioccg.org/reports/

  7. Ocean color sensors: characteristics http://www.ioccg.org/reports/

  8. Ocean color sensors: characteristics

  9. Ideally we need to match channels and optical signatures Ocean color sensors: characteristics SIO PIER

  10. Ocean color sensors: characteristics

  11. Ocean color sensors: Other criteria to keep in mind

  12. Ocean color sensors: S/N of detectors

  13. Ocean color sensors: types

  14. Lidar and detection of plankton and fish layers Spatial Variability in Spatial Variability in Biological Standing Stocks and SST across the GOA Basin and Shelves 2003. Evelyn Brown, Martin Montes, James Churnside. AFSC Symposium

  15. Model development Inherent and apparent Optical properties IOP’S and biogeochemical parameters Forward vs Inversion models

  16. IOP’s:not influenced by the light field (e.g., a, b, c coefficients) Inherent and Apparent Optical properties IOP’s: influenced by the light field (e.g., Rrs, Kd)

  17. IOP’S & biogeochemical parameters VSF?? Absorption Backscattering Phytoplankton CDOM POC SPM

  18. Forward vs Inversion models Inversion: Rrs Forward: IOP’s Rrs IOP’s (Empirical, analytical, statistical) (Hydrolight or non-commercial code)

  19. Forward: Monte Carlo simulations Forward vs Inversion models Montes-Hugo et al. 2006, SPIE

  20. Inversion models

  21. Applications Chlorophyll a concentration in case II waters of Alaska Phytoplankton size structure in Antarctic waters

  22. Rrs: Seawifs, MODIS, Microsas, • hand-held spectrometer • bb = HydroScat • Empirical: band ratio vs • spectral curvature Chlorophyll a concentration in case II waters of Alaska Montes-Hugo et al. 2005. RSE

  23. Remote sensing reflectance TOA 200 m height Spectral curvature Validation RMSlog10 = 0.41 RMSlog10 = 0.33 No regression

  24. STAY AWAY FROM CDOM USING LONGER WAVELENGTHS!!

  25. Spectral Backscattering approach • bb from HS-6 • Rrs from PRR, SeaWiFS • Phytoplankton size: chl fractions , HPLC Phytoplankton size structure in Antarctic waters bbx () = M (o/) bbx Montes-Hugo et al. 2007. IJRS

  26. PRR Field data Phytoplankton size structure in Antarctic waters

  27. Phytoplankton size structure in Antarctic waters

  28. HydroScat-6

  29. SeaWiFS

  30. Model validation based on HPLC signatures

  31. Thank you!!

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