2004-10-03 Co-retrieval of Aerosol and Surface Reflectance: Analysis of Daily US SeaWiFS Data for 2000-2002
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2004-10-03 Co-retrieval of Aerosol and Surface Reflectance: Analysis of Daily US SeaWiFS Data for 2000-2002

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  • 1. Co-retrieval of Aerosol and Surface Reflectance: Analysis of Daily US SeaWiFS Data for 2000-2002 Sean Raffuse, Erin Robinson and Rudolf B. Husar CAPITA, Washington University Presented at A&WMA’s 97 th Annual Conference and Exhibition June 22-27, Indianapolis, IN
  • 2. Radiation detected by satellites
    • Air scattering depends on geometry and can be calculated (Rayleigh scattering)
    • Clouds completely obscure the surface and have to be masked out
    • Aerosols redirect incoming radiation by scattering and also absorb a fraction
    • Surface reflectance is a property of the surface
  • 3. SeaWiFS Satellite Platform and Sensors
    • Satellite maps the world daily in 24 polar swaths
    • The 8 sensors are in the transmission windows in the visible & near IR
    • Designed for ocean color but also suitable for land color detection, particularly of vegetation
    Chlorophyll Absorption Designed for Vegetation Detection Swath 2300 KM 24/day Polar Orbit: ~ 1000 km, 100 min. Equator Crossing: Local Noon
  • 4. Aerosol effects on surface color and Surface effects on aerosol color The image was synthesized from the blue (0.412 μm), green (0.555 μm), and red (0.67 μm) channels of the 8 channel SeaWiFS sensor. Air scattering has been removed to highlight the haze and surface reflectance.
  • 5. Preprocessing
    • Transform raw SeaWiFS data
    • Georeferencing – warping data to geographic lat/lon coordinates with a pixel resolution of ~ 1.6 km
    • Splicing – mosaic data from adjacent swaths to cover entire domain
    • Rayleigh correction – remove scattering by atmospheric gases and convert to reflectance units
    • Scattering angle correction – normalize all pixels to remove reflectance dependence on sun-target-sensor angles
    • Result is daily apparent reflectance, R for all 8 channels
  • 6. General Approach: Co-Retrieval of Surface and Aerosol Reflectance
    • Surface Reflectance Retrieval by Time Series Analysis
      • (Sean Raffuse, MS Thesis 2003)
    • Aerosol Retrieval over Land
      • Radiative transfer model + Surface data
    • Refined Surface Reflectance
      • Iteration back to 1., 2. …
  • 7. Scattering angle correction 2
    • Pixels are normalized to a scattering angle of 150 °
  • 8. Approach – Time Series Analysis
    • For any location (pixel), the sensor detects a “clean” day periodically
      • Aerosol scattering (haze) is near zero
      • Pixel must also be free of other interferences
        • Clouds
        • Cloud shadows
  • 9. Methodology – Cloud shadows
    • Clouds are easily detected by their high reflectance values
    • Cloud shadows are found in the vicinity of clouds
    • We enlarge the cloud mask by a three-pixel ‘halo’ to remove cloud shadows
    • Cloud shadows reduce the apparent surface reflectance considerably in all channels
  • 10. Methodology – Preliminary anchor days
    • Surface reflectance is retrieved for individual pixels from time series data (e.g. year)
    • The procedure first identifies a set of ‘preliminary clear anchor’ days in a 17-day moving window
      • The main interferences (clouds and haze) tend to increase the apparent surface reflectance, especially in the low wavelength channels
      • The anchor day is chosen as the day with the minimum sum of the lowest four channels
  • 11. Surface Reflectance in Blue & Red, Illinois
    • Haze perturbation of the surface reflectance is most pronounced at 0.41  m, ‘blue’
    • In some cases, haze is evident in blue, but not in red (0.67  m).
    • Hence, the blue channel is used to identify the anchor days.
    • For the selected days, the pixel’s reflectance is retained for each of the 8 channels (need better explanation)
    Clouds Haze
  • 12. Methodology – Residual haze reduction
    • In some regions, aerosol haze is persistent throughout over long periods e.g. Southeast in the summer
    • Spectral analysis is used to reduce the residual haze over these surfaces
  • 13. Results – Seasonal surface reflectance, Eastern US April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
  • 14. Results – Seasonal surface reflectance, Western US April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
  • 15. Results – Eight month animation
  • 16. Apparent Surface Reflectance, R
    • The surface reflectance R 0 is obscured by aerosol scattering and absorption before it reaches the sensor
    • Aerosol acts as a filter of surface reflectance and as a reflector solar radiation
    Aerosol as Reflector: R a = ( e -  – 1 ) P R = ( R 0 + ( e -  – 1 ) P ) e -  Aerosol as Filter: T a = e -  Surface reflectance R 0
    • The apparent reflectance , R , detected by the sensor is: R = ( R 0 + R a ) T a
    • Under cloud-free conditions, the sensor receives the reflected radiation from surface and aerosols
    • Both surface and aerosol signal varies independently in time and space
    • Challenge: Separate the total received radiation into surface and aerosol components
  • 17. Aerosol Retrieval: Idaho Smoke Event, Aug 2000
    • Generate daily total reflectance image with air reflectance removed, R
    • Generate surface reflectance image, R 0
    • Subtract daily total reflectance image from surface reflectance image to get aerosol optical thickness, 
    • Filter  , removing clouds and other interferences
  • 18. July 10, 2002 July 9, 2002 July 8, 2002 July 7, 2002 July 6, 22002 July 5, 2002
  • 19. Discussion - Advantages
    • Resolution independent – adaptable to other datasets that operate at different resolutions that provide appropriate spectral coverage (available bands near 0.4, 0.6, and 0.85  m)
    • Fully automated, requiring no user input once initiated
    • Spatial, spectral, and temporal resolution of the sensor data are maintained
    • Minimal need for a priori aerosol knowledge
    • Detects surface color change on the order of days/weeks when cloud free data exist
  • 20. Satellite Applications for US AQ Management: Pros and Cons
    • Geographic Coverage
      • Pros:
        • High spatial resolution of (~ 1 km)
        • Global domain (covers intercontinental transport)
      •   Cons:
        • Low fraction (~50%), spatially spotty and unpredictable (wherever clouds are)
    • Vertical Coverage
      • Pros:
        • Vertical integral: Provides climate-relevant integral parameter, e.g. Aerosol Optical Thickness (AOT)
      • Cons:
        • Vertical integral: Not easily related to surface aerosol concentration
    • Temporal Coverage
      • Pros:
        • Time resolution (30 min for GOES, 24 hrs for Wide-POS, 7 days for Narrow-POS)
        • Time domain (1980s for AVHRR, 1990s for TOMS, 2000s for Terra/Aqua suite)
      •   Cons:
        • Time resolution (30 min for GOES, 24 hrs for Wide-POS, 7 days for Narrow-POS)
        • Time domain (1980s for AVHRR, 1990s for TOMS, 2000s for Terra/Aqua suite)
    • Parameter Coverage
        • Dependence on Humidity: Sulfates, nitrates and other hygroscopic aerosols produce humidity-dependent satellite signal (haloes).