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

    • 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
    • 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
    • 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
    • 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.
    • 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
    • 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. …
    • Scattering angle correction 2
      • Pixels are normalized to a scattering angle of 150 °
    • 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
    • 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
    • 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
    • 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
    • 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
    • Results – Seasonal surface reflectance, Eastern US April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
    • Results – Seasonal surface reflectance, Western US April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
    • Results – Eight month animation
    • 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
    • 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
    • July 10, 2002 July 9, 2002 July 8, 2002 July 7, 2002 July 6, 22002 July 5, 2002
    • 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
    • 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).