Co-retrieval of Aerosol and Surface Reflectance
SeaWiFS Satellite
Sean Raffuse, Erin Robinson
Chlorophyll Absorption
Designed for Vegetation Detection
Co-Retrieval of Surface and Aerosol PropertiesApparent Surface Reflectance, R
Aer. Transmittance
Both R0 and Ra are attenuated by aerosol extinction Ta
which act as a filter
Aerosol Reflectance
Aerosol scattering acts as
reflectance, Ra adding ‘airlight’ to the surface reflectance
Surface Reflectance
The surface reflectance R0 is an inherent characteristic
of the surface
R = (R0 + (e-– 1) P) e-
• The surface reflectance R0 objects viewed from space is modified by aerosol scattering and absorption.
• The apparent reflectance, R, is: R = (R0 + Ra) Ta
Aerosol as Reflector:
Ra = (e-– 1) P
Aerosol as Filter: Ta = e-
Apparent Reflectance
R may be smaller or larger then R0, depending on aerosol
reflectance and filtering.
Co-Retrieval: Seasonal Surface Reflectance, Eastern US
April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290
Kansas Agricultural Smoke, April 12, 2003
Fire Pixels PM25 Mass, FRM65 ug/m3 max
Organics35 ug/m3 max
Ag Fires
SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue
Kansas Smoke Emission Estimation
April 11: 87 T/day
April 10: 1240 T/d
Assuming Mass Extinction Efficiency:
5 m2/g
April 11, 2003
Emission Estimate
Fire PixelsSurface
Observations
Monte CarloDiagnostic Local Model
Satellite-Surface-Model Data IntegrationSmoke Emission Estimation
Emission Model
Land Vegetation
Fire Model
e..g. MM5 winds, plume model
Local Smoke Simulation Model
AOT Aer. Retrieval
Satellite Smoke
Visibility, AIRNOW
Surface Smoke
Assimilated Smoke Pattern
Continuous Smoke Emissions
Assimilated Smoke Emission for Available Data
Fire Pixel, Field Obs
Fire Location
Assimilated Fire Location
Satellite Aerosol Optical Thickness ClimatologySeaWiFS Satellite, Summer 2000 - 2004
20 Percentile
98 Percentile90 Percentile
60 Percentile
Smoke Sources 98 Percentile
Summer AOT 60 Percentile2000-2004 SeaWiFS AOT, 1 km Resolution
Mountain – Low AOT
Valley – High AOT
AtlantaBirmingham
Cloud Contamination?
Temporal Signal Decomposition and
Event Detection
1. Daily median & average over a region
2. Temporal smoothed by a 30 day
EUS Daily Average 50%-ile, 30 day 50%-ile smoothing
Deviation from %-ile
Event : Deviation > x*percentile
Median Seasonal Conc.
Mean Seasonal Conc.
Average
Median
3. Event is the deviation of daily value from the smooth median (event – red; noise blue)
Causes of Temporal Variation by Region
The temporal signal variation is decomposable into seasonal, meteorological noise and events. Statistically:
V2Total = V2
Season + V2MetNoise + V2
Event
Northeast exhibits the largest coeff. variation (56%); seasonal, noise and events each at 30%Southeast is the least variable region (35%), with virtually no contribution from eventsSouthwest, Northwest, S. Cal. and Great Lakes/Plains show 40-50% coeff. variation mostly, due to seasonal and meteorological noise.Interestingly, the noise is about 30% in all regions, while the events vary much more, 5-30%