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Multi-Sensory Detection of Agricultural Smoke
Erin RobinsonAdvisor, Rudolf Husar
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
Algorithm for obtaining Aerosol Optical Thickness (AOT)
Case Study: Kansas Agricultural Smoke
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
Rayleigh corrected SeaWiFS with fire pixels and wind vectors
AOT with fire pixels and wind vectors
Case Study Kansas, April 10, 2003
Surface reflectance
Yellow dots represent surface PM2.5 measurements taken at 12:00pm, AIRNOW
AOT and surface PM2.5 measurements superimposed
AOT and wind vectors
Case Study Kansas, April 11, 2003
Case Study Kansas, April 12, 2003
Case Study Kansas, April 13, 2003
Using IDL1. Compile PM2.5 Data into a
text file2. Read the text file into the
program3. 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
