Agricultural Smoke Detection with Satellite and Surface Sensors

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Agricultural Smoke Detection with Satellite and
Surface Sensors

• Erin Robinson
• Advisor, Rudolf Husar
• Center for Air Pollution Impact and Trend Analysis

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Algorithm for obtaining Aerosol Optical Thickness
(AOT)
Surface reflectance as obtained from SeaWiFS
satellite (http//daac.gsfc.nasa.gov/data/dataset/
SEAWIFS/01_Data_Products/02_LAC/01_L1A_HRPT/index.
html) Rayleigh Corrected
Minimum surface reflection which contains no
aerosol a true surface reflection
AOT obtained through an algorithm which takes the
difference between the original surface
reflectance and the minimum surface reflection
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Case Study Kansas Agricultural Smoke
Rayleigh corrected SeaWiFS with fire pixels and
wind vectors
AOT with fire pixels and wind vectors
Zoomed in portion of Kansas, red dots represent
fire pixels and yellow arrows represent the wind
vectors. Heaviest smoke is seen in the AOT image
in blue
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Case Study Kansas, April 10, 2003
Yellow dots represent surface PM2.5 measurements
taken at 1200pm, AIRNOW
Surface reflectance
AOT and wind vectors
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Case Study Kansas, April 11, 2003
Case Study Kansas, April 12, 2003
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Case Study Kansas, April 13, 2003
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Using IDL

1. Compile PM2.5 Data into a text file
2. Read the text file into the program
3. Using the appropriate AOT file find the pixel
   that correlates to the appropriate
   latitude/longitude of the station
4. Write out the same file with AOT values included

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Agricultural Smoke Detection with Satellite and
Surface Sensors