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

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

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

7. ‘Atmospheric windows’

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

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

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

11. Ocean color sensors: characteristics

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

13. Ocean color sensors: characteristics

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

15. Ocean color sensors: S/N of detectors

16. Ocean color sensors: types

17. 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

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

19. 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)

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

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

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

23. Inversion models

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

25. 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

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

27. STAY AWAY FROM CDOM USING LONGER WAVELENGTHS!!

28. 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

29. PRR Field data Phytoplankton size structure in Antarctic waters

30. Phytoplankton size structure in Antarctic waters

31. HydroScat-6

32. SeaWiFS

33. Model validation based on HPLC signatures

34. Thank you!!