Transcript ppt
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 **************************************************************************** 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 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 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 Sensors and Satellites - Past www.ioccg.org Sensors and Satellites - Current www.ioccg.org Sensors and Satellites - Planned www.ioccg.org Major types of correction methods Know 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 Others -> CAAS SeaWiFS seadas.gsfc.nasa.gov SeaDAS Atmospheric Correction Wavelength dependent equation ρT(λi) = ρR + ρA + ρC + ρSG + ρWC + transmittancesurf-sensor * ρW ρT ρRA ρAS ρCPL ρSG ρWC ρW Top of atmosphere reflectance Rayleigh scattering Aerosol scattering Coupled Rayleigh-Aerosol effects Sun glint Whitecaps (wind on sea surface) Water-leaving reflectance Sensor Not difficult Difficult Assume zero Assume zero Less than 7 m/s 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 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) aeronet.gsfc.nasa.gov CAAS - Basic Equation LT(λi) = LR + LA + LC + transsun-surf * LSG + LWC + transsurf-sensor * LW LT LR LA LC LSG LWC LW Top of atmosphere reflectance Rayleigh scattering Aerosol scattering Coupled Rayleigh-Aerosol effects Sun glint Whitecaps (wind on sea surface) Water-leaving reflectance Sensor Not difficult Estimated Estimated Estimated Ignored for now Desired Use Rayleigh-corrected Radiance to derive Aerosol correction Spectral shape matching method See Shanmugam (2012) pg 205-207 for math discussion 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 Questions?