http://capitawiki.wustl.edu/index.php/20041118_Multi-Sensory_Detection_of_Agricultural_Smoke

1. Multi-Sensory Detection of Agricultural Smoke Erin Robinson Advisor, Rudolf Husar

2. 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)

3. 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

4. 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

5. Case Study Kansas, April 11, 2003 Case Study Kansas, April 12, 2003

6. Case Study Kansas, April 13, 2003