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April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
Results – Seasonal surface reflectance, Eastern US
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
Swath
2300 KM
24/day
Polar Orbit: ~ 1000 km, 100 min.
Equator Crossing: Local NoonChlorophyll Absorption
Designed for Vegetation Detection
Satellite Aerosol Optical Thickness ClimatologySeaWiFS Satellite, Summer 2000 - 2003
20 Percentile
99 Percentile90 Percentile
60 Percentile
Satellite AOT – Time Fraction (0-100%)SeaWiFS Satellite, Summer 2000 - 2003
Dec, Jan Feb
Sep, Oct, NovJun, Jul, Aug
Mar, Apr, May
SeaWiFS AOT – Summer 60 Percentile1 km Resolution
Technical Challenge: Characterization
• PM characterization requires many different instruments and analysis tools.
• Each sensor/network covers only a limited fraction of the 8-D PM data space.
• Most of the 8D PM pattern is extrapolated from sparse measured data.
• Some devices (e.g. single particle electron microscopy) measure only a small subset of the PM; the challenge is extrapolation to larger space-time domains.
• Others, like satellites, integrate over height, size, composition, shape, and mixture dimensions; these data need de-convolution of the integral measures.
Summary
• Satellite data have aided the science of Particulate Matter since the 1970s
• Satellite data have supported PM air quality management since the 1990s.
• Past satellite data helped the qualitative description of PM spatial pattern
• Quantitative satellite data use and fusion with surface data is still in infancy
• Satellite data applications will require collaboration across disciplines
April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
Results – Seasonal surface reflectance, Western US
Results – Eight month animation
Apparent Surface Reflectance, R• The surface reflectance R0 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: Ra = (e-– 1) P
R = (R0 + (e-– 1) P) e-
Aerosol as Filter: Ta = e-
Surface reflectance R0
• The apparent reflectance , R, detected by the sensor is: R = (R0 + Ra) Ta
• 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
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