1. An Introduction to Using Spectral Information in Aerosol Remote Sensing Richard Kleidman SSAI/NASA Goddard Lorraine Remer UMBC / JCET Robert C. Levy NASA GSFC Revised November 2014

2. An Introduction to Using Spectral Information in Aerosol Remote Sensing Richard Kleidman SSAI/NASA Goddard Lorraine Remer UMBC / JCET Robert C. Levy NASA GSFC This presentation is intended to introduce some basic concepts on how spectral information is used in remote sensing (of aerosols) and how this information is used to construct remote sensing products.

3. We begin by looking at some general problems presented by looking down through a dirty atmosphere.

4. In an ideal situation with no atmosphere all of the incoming radiation would reach the surface. A portion of the photons would be absorbed at the surface. The remaining photons reflect back up into space. Diagrams from E. Vermote et. al, 6S manual Measured radiance directly depends on surface properties No Atmosphere

5. What does the satellite see? What information do the photons contain? 1) Backscattered photons which never reach the surface. Signal or Noise? … and for whom ? Diagram from E. Vermote et. al, 6S manual With Atmosphere

6. 2) Scattered photons which illuminate the ground. Signal or Noise? 3) Photons reflected by the surface and then scattered by the atmosphere. Diffuse solar radiation.

7. Multiple scattering events. This is usually ignored after one or two interactions. Diagram from E. Vermote et. al, 6S manual

8. From E. Vermote et. al, 6S manual The real atmosphere complicates the signal. Only a fraction of the photons reach the sensor so that the target seems less reflecting. Real AtmospherePhotons lost due to 1)Atmospheric absorption 2)Scattering

9. Geometric issues of the illumination and the measurement Very important for surface and atmospheric signal Solar zenith angle Sensor zenith angle Solar view angle Sensor view angle

10. From Spectra to Product Some steps along the way from satellite observations to useful geophysical content. Aviris Spectra MODIS Band 4 MODIS Cloud Fraction MODIS Aerosol Optical Depth

11. One of the advantages of MODIS is its broad spectral range. The wider the spectral range the more information content we have when we observe the Earth - Atmosphere system. Let’s begin simply by examining some sample spectra of different surfaces. Aspen Leaves - very uniform Aspen Green Leaf Aspen Yellow Leaf The signal reaching any space borne sensor is a complex mixture of surface and atmospheric components.

12. From Spectra to Product A catalog of spectral responses of known surfaces can help us establish the identity of an unknown or mixed scene we are observing much like a fingerprint can help us establish the identity of the person who made the print. Product – A set of values that is used to describe, in a consistent way, physical properties of an observed geophysical phenomena. Products can consist of: Images Qualitative evaluations – Cloudy or clear Quantitative measurements – Amount of aerosol, percent cloud There are many complications but let’s examine a little bit about how we get from raw spectra to useful product. ?

13. Gaseous Absorption Atmospheric gases - CO 2, O 2, and H 2 O absorb the solar radiation at specific locations in the EM spectra causing the gaps we see at the left. In most cases the absorption bands limit our ability to obtain useful information There are some cases where we can exploit the absorption bands to obtain additional information.

14. Slant-Path Absorption of the Atmosphere & Location of Primary Atmospheric Windows Wavelength (µm) 0.500.5 5 0.600.650.750.800.85 0.01 0.00 0.02 0.03 0.04 0.05 Absorption 0.70 O 2 B-Band O 2 A-Band Courtesy of Michael King

15. Spectral optical properties of aerosol Dust Smoke from Y. Kaufman Both dust and smoke interact with the shorter wavelengths reflecting light back to the sensor. The larger dust particles interact with the longer infrared wavelengths but not the smaller smoke particles which remain invisible. This distinction is made possible by the wide spectral range of the MODIS sensor.

16. Spectral optical properties of aerosol Dust / Sea Salt Smoke / Pollution wavelength in µm Here you can see the spectral response of the large and small particles. Note that the large particles produce a high response across the spectral range. The small particles produce weaker responses as the wavelength of the light increases

17. Physical Properties Angstrom Exponent - α The Angstrom exponent is often used as a qualitative indicator of mean aerosol particle size in the atmospheric column. Values greater than 2 – small particles Values less than 1 – large particles For measurements of optical thickness  1 and  2 taken at two different wavelengths 1 and 2 α = -  1  2 ln 1 2 ln

18. Physical Properties The angstrom exponent really represents the (negative of the) slope of the spectral response. wavelength in µm The response of large particles is characterized by little to no slope. The response of small particles is characterized by moderate to large slopes.

19. 20 km 12 km R = 0.66 µm G = 0.55 µm B = 0.47 µm R = 1.6 µm G = 1.2 µm B = 2.1 µm A g (2.1 µm) < 0.10 0.10 < A g (2.1 µm) < 0.15  0 = 36° Biomass burning Cuiabá, Brazil (August 25, 1995) Extracting Information by Specific Band Usage AVIRIS

20. Example MODIS Data Granule Canadian Fires, MODIS Terra, 7 July 2002 true colorSWIR composite sea ice smoke ice cloud

21. Using multiple bands to separate cloud, land and ocean surfaces. This is an Aqua MODIS image of a storm system off the coast of South America We are going to use a combination of three different bands to quantitatively draw a distinction between clouds, land and ocean.

22. We begin by making an ocean vs land mask MODIS band 2 0.86 um Sensitive to vegetation Clouds are bright Water is dark MODIS band 1 0.66 um Land is dark Clouds are bright Water is dark Band 2 / Band 1 Accentuates the differences between land and ocean Is not sensitive to clouds -clouds are spectrally bright through all of the reflectance bands. When we divide band 2 by band 1 almost all values for cloud become very close to 1.0

23. Separating Cloud from Non-Cloud MODIS Band 31- 11um This is a thermal bands so it is sensitive to temperature. Clouds are cold relative to land and ocean surfaces. Typically higher response values map to higher intensity display values. In this case we use an inverted display scale so that the lower response values of the cold clouds will appear bright in the image.

24. Putting it all together This is a scatter plot of band 31 (temperature information) on the x-axis Vs Band 2 / Band 1 (our land/sea mask on the y-axis We have managed to separate the two features into somewhat distinct groups. Band 31(11μm) – brightness temperature Band 2 (.86 μm ) / Band 1 (0.66 μm ) – Land / Sea mask

25. Putting it all together Using the hydra toolhydra tool we can select some of the distinctive clusters of points in the scatter plot and color their corresponding locations in the image. The image at bottom left now shows our crude mask of Land, Ocean and Cloud.

26. Observe the following true color images. See how many surface issues, satellite issues or combinations of the two you can find from the following three images. 09 April 200412 June 200419 December 2004

27. What are some complications as we add more aerosol?