Agricultural Smoke Detection with Satellite and Surface Sensors

Erin RobinsonAdvisor, Rudolf Husar

Center for Air Pollution Impact and Trend Analysis

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 wind vectors

Case Study Kansas, April 11, 2003

Case Study Kansas, April 12, 2003

Case Study Kansas, April 13, 2003

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

Agricultural Smoke Detection with Satellite and Surface Sensors

Case Study Kansas, April 10, 2003

Surface reflectance

Yellow dots represent surface PM2.5 measurements taken at 12:00pm, AIRNOW

AOT and wind vectors