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