remote sensing applications in oceanography: how much we can see using ocean color ?

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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. Main topics. Introduction: definitions, sensor characteristics Model development: - PowerPoint PPT Presentation

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Page 1: Remote sensing applications in Oceanography: How much we can see using  ocean color ?
Page 2: Remote sensing applications in Oceanography: How much we can see using  ocean color ?

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Remote sensing applications in Oceanography:

How much we can see using ocean color?

Adapted from lectures by: Martin A MontesRutgers University

Institute of Marine and Coastal Sciences

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Main topics

Introduction:definitions, sensor characteristics

Model development: IOP’s, AOP’s, Forward and Inversion approach

Applications: chl, phytoplankton size structure

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Ocean color sensors

Definition:

Types: Passive vs Active

Sensor characteristics: swath, footprint, revisiting time, spectral resolution

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Ocean color sensors: characteristics

•First sensors: B&W

•Temporal resolution:revisiting time?

•Spectral resolution: number of channels?, bandwidth?

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Differences between measuring SST and ocean color:

Infrared radiometers (like AVHRR) measure radiation emitted from the ocean surfaceAssumes ocean is like a black-body emitter with TB related to actual temperatureMeasures skin temperature only

Ocean color sensors do not measure emission – they measure reflectance

How do we know we’re measuring reflectance, not emission?

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How do we know we’re measuring reflectance, not emission?

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.

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Ocean color sensors: characteristics

http://www.ioccg.org/reports_ioccg.html

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Ocean color sensors: characteristics

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Ocean color sensors: characteristics

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Ocean color sensors: characteristics

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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)

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IOP’S & biogeochemical parameters

Absorption Backscattering

Phytoplankton CDOM POC SPM

VSF??

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Forward vs. Inversion models

Forward:

IOP’s Rrs

(Hydrolight or non-commercial code)

Given what we know isin the water, what do weexpect it to look like?

Inversion:

Rrs

(Empirical, analytical, statistical)

Given what we see, what can we tell about what is in the water?

IOP’s

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

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Phytoplankton Bloom in the Arabian Sea