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.

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Presentation transcript:

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

4

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

6

7 ‘Atmospheric windows’

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

9 Ocean color sensors: characteristics

10 Ocean color sensors: characteristics

11 Ocean color sensors: characteristics

12 Ideally we need to match channels and optical signatures 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 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 Inherent and Apparent Optical properties IOP’s: not influenced by the light field (e.g., a, b, c coefficients) IOP’s: influenced by the light field (e.g., R rs, K d )

20 IOP’S & biogeochemical parameters AbsorptionBackscattering PhytoplanktonCDOMPOCSPM VSF??

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

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

23 Inversion models

24 Applications 1. Chlorophyll a concentration in case II waters of Alaska 2. Phytoplankton size structure in Antarctic waters

25 Chlorophyll a concentration in case II waters of Alaska Montes-Hugo et al RSE R rs :R rs : Seawifs, MODIS, Microsas, hand-held spectrometer b b = HydroScat Empirical:Empirical: band ratio vs spectral curvature

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

27 STAY AWAY FROM CDOM USING LONGER WAVELENGTHS!!

28 Phytoplankton size structure in Antarctic waters Montes-Hugo et al IJRS Spectral Backscattering approach b b from HS-6 R rs from PRR, SeaWiFS Phytoplankton size: chl fractions, HPLC b bx ( ) = M ( o / )  bbx

29 Phytoplankton size structure in Antarctic waters Field data PRR

30 Phytoplankton size structure in Antarctic waters

31 HydroScat-6

32 SeaWiFS

33 Model validation based on HPLC signatures

34 Thank you!!