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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 ?. Adapted from lectures by: Martin A Montes Rutgers University Institute of Marine and Coastal Sciences. 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? Adapted from lectures by: Martin A Montes Rutgers University Institute of Marine and Coastal Sciences

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

  5. 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 TB 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?

  6. 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.

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

  8. Ocean color sensors: characteristics

  9. Ocean color sensors: characteristics

  10. Ocean color sensors: characteristics Hyperion hyperspectral sensor on EO-1 220 channels

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

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

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

  14. 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. 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

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

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

  17. Phytoplankton Bloom in the Arabian Sea

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