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Atmospheric Correction for Ocean Color Remote Sensing

Atmospheric Correction for Ocean Color Remote Sensing. Geo 6011 Eric Kouba Oct 29, 2012. Ocean Color Overview. Measured data Top of Atmosphere Radiance Need to do Atmospheric Correction Desired signal Water-Leaving Radiance or Remote Sensing Reflectance

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Atmospheric Correction for Ocean Color Remote Sensing

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  1. Atmospheric Correction forOcean Color Remote Sensing Geo 6011 Eric Kouba Oct 29, 2012

  2. Ocean Color Overview • Measured data Top of Atmosphere Radiance Need to do Atmospheric Correction Desired signal Water-Leaving Radiance or Remote Sensing Reflectance **************************************************************************** Use other equations and methods... Proxy parameter e.g. Chlorophyll-A concentration Biological parameter Phytoplankton primary productivity Desired goal Information about health of the ocean

  3. Sunlight to Surface to Sensor 1. Solar spectrum at top of atmosphere 2. Atmospheric absorption, scattering, etc 3. Clouds Thin clouds allow some visibility 4. Reflection from top layer of ocean Case 1 clear waters (tens of meters) Case 2 turbid waters (less penetration) 5. Atmospheric absorption, scattering, etc 6. Radiance measured by satellite sensor Only 10% to 20% of signal comes from ocean waters

  4. Ocean Color Sensors and Satellites • CZCS sensor on Nimbus-7 satellite • SeaWiFS sensor on OrbView-2 satellite • MODIS sensor on Terra satellite • MERIS sensor on ENVISAT satellite • MODIS sensor on Aqua satellite • ETM+ sensor on LANDSAT satellite

  5. Sensors and Satellites - Pastwww.ioccg.org

  6. Sensors and Satellites - Currentwww.ioccg.org

  7. Sensors and Satellites - Plannedwww.ioccg.org

  8. Major types of correction methodsKnow which one your software uses • Dark object subtraction • Invariant object subtraction • Histogram matching • Cosine estimation of atmospheric transmittance • Contrast reduction • Path extraction • Spectral shape matching method -> CAAS • Others

  9. SeaWiFSseadas.gsfc.nasa.gov

  10. SeaDAS Atmospheric CorrectionWavelength dependent equation • ρT(λi) = ρR + ρA + ρC + ρSG + ρWC + transmittancesurf-sensor * ρW • ρT Top of atmosphere reflectance Sensor • ρRA Rayleigh scattering Not difficult • ρAS Aerosol scattering Difficult • ρCPL Coupled Rayleigh-Aerosol effects Assume zero • ρSG Sun glint Assume zero • ρWC Whitecaps (wind on sea surface) Less than 7 m/s • ρW Water-leaving reflectance Desired • Basically a dark object subtraction, with additions • Assumes NIR (765 and 865 nm) should be zero • 12 aerosol models -> 25000 simulation runs -> Lookup tables • Works well for deep, clear, and low growth water • Fails in shallow, turbid, and high growth water

  11. Options when standard correction fails • Flag and ignore regions that are difficult to process • If available, use SWIR instead of NIR for dark subtraction • but MODIS has SWIR signal to noise problem • Simultaneous spectroradiometer measurements in field of view • but Not practical for daily operations • Take spectroradiometer measurements nearby • but Atmospheric parameters vary in time and space • Use other algorithm with standard atmospheres • but Atmospheric parameters vary in time and space • Use algorithm to get aerosol correction from within image data • e.g. Shanmugam (2012)

  12. aeronet.gsfc.nasa.gov

  13. CAAS - Basic Equation • LT(λi) = LR + LA + LC + transsun-surf * LSG + LWC + transsurf-sensor * LW • LT Top of atmosphere reflectance Sensor • LR Rayleigh scattering Not difficult • LA Aerosol scattering Estimated • LC Coupled Rayleigh-Aerosol effects Estimated • LSG Sun glint Estimated • LWC Whitecaps (wind on sea surface) Ignored for now • LW Water-leaving reflectance Desired • Use Rayleigh-corrected Radiance to derive Aerosol correction • Spectral shape matching method • See Shanmugam (2012) pg 205-207 for math discussion

  14. Conclusions • NIR dark subtraction fails in shallow, turbid, and high growth water • Like counting worldwide trees, but failing in the jungle • Aerosol modeling remains difficult • Atmospheric correction • is important... • Choose wisely

  15. Questions?

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