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2 Remote sensing applications in Oceanography: How much we can see using ocean color? Adapted from lectures by: Martin A Montes Rutgers University Institute.

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Presentation on theme: "2 Remote sensing applications in Oceanography: How much we can see using ocean color? Adapted from lectures by: Martin A Montes Rutgers University Institute."— Presentation transcript:

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2 2 Remote sensing applications in Oceanography: How much we can see using ocean color? Adapted from lectures by: Martin A Montes Rutgers University Institute of Marine and Coastal Sciences

3 3 Main topics  Introduction: definitions, sensor characteristics  Model development: IOP’s, AOP’s, Forward and Inversion approach  Applications: chl, phytoplankton size structure

4 4 Ocean color sensors  Definition:  Types: Passive vs Active  Sensor characteristics: swath, footprint, revisiting time, spectral resolution

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

6 Differences between measuring SST and ocean color: Infrared radiometers (like AVHRR) measure radiation emitted from the ocean surface Assumes ocean is like a black-body emitter with T B related to actual temperature Measures skin temperature only Ocean color sensors do not measure emission – they measure reflectance How do we know we’re measuring reflectance, not emission?

7 Emission by the Earth in the visible is zero. Reflectance of the ocean in the thermal infrared is almost zero Reflectance of the ocean is not only a “skin” phenomenon. Its signal is more complex because the optical depth is much greater and depends on wavelength.

8 Ocean color sensors: characteristics http://www.ioccg.org/reports_ioccg.html

9 Ocean color sensors: characteristics

10 10 Ocean color sensors: characteristics

11 Hyperion hyperspectral sensor on EO-1 220 channels

12 12 Inherent and Apparent Optical properties IOP’s: not influenced by the light field (e.g., absorption coefficients) AOP’s: influenced by the light field (e.g., reflectance, backscattering)

13 13 IOP’S & biogeochemical parameters AbsorptionBackscattering PhytoplanktonCDOMPOCSPM VSF??

14 14 Forward vs. Inversion models Forward: IOP’s R rs (Hydrolight or non-commercial code) Given what we know is in the water, what do we expect it to look like? Inversion: R rs (Empirical, analytical, statistical) Given what we see, what can we tell about what is in the water? IOP’s

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16 Rrs(412)/Rrs(555) band ratio yields a reasonably consistent relationship with in situ observations of CDOM absorption across several regions in the Mid-Atlantic continental shelf Can also derive empirical relationship between backscatter and particulate matter in the water. This allows estimation, by satelite, or Particulate Organic Carbon (POC) in the ocean.

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21 CZCS image of the Gulf Stream obtained on April 1, 1982, showing a prominent warm-core ring. http://disc.sci.gsfc.nasa.gov/oceancolor/additional/science-focus/classic_scenes

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24 24 MODIS Sea Surface Temperature, 2000 December 6, 17:05 and MODIS Surface Chlorophyll Concentration

25 25 Phytoplankton Bloom in the Arabian Sea


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