The document summarizes a course on detecting aerosols using satellites. The course covers: 1) Introduction to satellite aerosol detection, different satellite types and their usage. 2) How satellites detect aerosol events like fires, dust storms and haze. 3) Using satellite data to study aerosols for projects like analyzing their fast response to changing environmental conditions. 4) Applications of satellite data in air quality management and issues around its use.

1. Air and Waste Management Association Professional Development Course AIR-257: Satellite Detection of Aerosols Concepts and Theory Instructor: Rudolf Husar, Ph.D. Professor of Mechanical Engineering Washington University, St. Louis, MO October 25, 2004, 9:00 a.m. - 12:00 p.m. Asheville, NC

4. Just like the human eye, satellite sensors detect the total amount of solar radiation that is reflected from the earth’s surface ( R o ) and backscattered by the atmosphere from aerosol, pure air, and clouds. A simplified expression for the relative radiatioin detected by a satellite sensor (I/I o ) is: I / I o = R o e -  + (1- e -  ) P Satellite Detection of Aerosols Today, geo-synchronous and polar orbiting satellites can detect different aspects of aerosols over the globe daily. where  is the aerosol optical thickness and P the angular light scattering probability.

6. Key aerosol microphysical parameters Particle size and size distribution Aerosol particles > 1  m in size are produced by windblown dust and sea salt from sea spray and bursting bubbles. Aerosols smaller than 1  m are mostly formed by condensation processes such as conversion of sulfur dioxide (SO 2 ) gas to sulfate particles and by formation of soot and smoke during burning processes Effective radius Moment of size distribution weighted by particle area and number density distribution Complex refractive index The real part mainly affects scattering and the imaginary part mainly affects absorption Particle shape Aerosol particles can be liquid or solid, and therefore spherical or nonspherical. The most common nonspherical particles are dust and cirrus

7. Key aerosol optical parameters Optical depth  negative logarithm of the direct-beam transmittance  column integrated measure of the amount of extinction (absorption + scattering) Single-scattering albedo  0  given an interaction between a photon and a particle, the probability that the photon is scattered in some direction, rather than absorbed Scattering phase function  probability per unit solid angle that a photon is scattered into a particular direction relative to the direction of the incident beam Angstrom exponent   exponent of power law representation of extinction vs. wavelength

11. Atmospheric Absorption in the Wavelength Range from 0-15 µm Michael D. King, EOS Senior Project Scientist

12. Generalized Spectral Reflectance Envelopes for Deciduous and Coniferous Trees Michael D. King, EOS Senior Project Scientist

13. Typical Spectral Reflectance Curves for Vegetation, Soil, and Water Michael D. King, EOS Senior Project Scientist

14. Different Types of Reflectors Specular reflector (mirror) diffuse reflector (lambertian) nearly diffuse reflector Nearly Specular reflector (water) E. Vermote, 2002 Hot spot reflection

15. Sun glint as seen by MODIS Hot hot-spot over dense vegetation E. Vermote, 2002

16. Scattering of Sunlight by the Earth-Atmosphere-Surface System A = radiation transmitted through the atmosphere and reflected by the surface B = radiation scattered by the atmosphere and reflected by the surface C = radiation scattered by the atmosphere and into the ‘radiometer’ G = radiation transmitted through the atmosphere, reflected by background objects, and subsequently reflected by the surface towards the ‘radiometer’ I = ‘adjacency effect’ of reflectance from a surface outside the field of view of the sensor into its field of view Michael D. King, EOS Senior Project Scientist

17. Solar Energy Paths

20. Apparent Surface Reflectance, R Aerosols will increase the apparent surface reflectance, R, if P/R 0 < 1. For this reason, the reflectance of ocean and dark vegetation increases with τ. When P/R 0 > 1, aerosols will decrease the surface reflectance. Accordingly, the brightness of clouds is reduced by overlying aerosols. At P~ R 0 the reflectance is unchanged by haze aerosols (e.g. soil and vegetation at 0.8 um). . At large τ (radiation equilibrium), both dark and bright surfaces asymptotically approach the ‘aerosol reflectance’, P The critical parameter whether aerosols will increase or decrease the apparent reflectance, R, is the ratio of aerosol angular reflectance, P, to bi-directional surface reflectance, R 0 , P/ R 0

21. Reduced Reflectance Reflectance Reflectance Increased Reflectance

22. Loss of Contrast The aerosol τ can also be estimated from the loss of surface contrast. Whether contrast decays fast or slow with increasing τ depends on the ratio of aerosol to surface reflectance, P/ R 0 Note: For horizontal vision against the horizon sky, P/R 0 = 1, contrast decays exponentially with τ, C/C 0 =e -τ .

23. Obtaining Aerosol Optical Thickness from Excess Reflectance The perturbed surface reflectance, R, can be used to derive the the aerosol optical thickness, τ , provided that the true surface reflectance R 0 and the aerosol reflectance function, P are known. The excess reflectance due to aerosol is : R- R 0 = (P- R 0 )(1-e - τ ) and the optical depth is: For a black surface, R 0 =0 and optically thin aerosol, τ < 0.1, τ is proportional to excess radiance, τ =R/P. For τ > 0.1, the full logarithmic expression is needed. As R 0 increases, the same excess reflectance corresponds to increasing values of τ. When R 0 ~P the aerosol τ can not be retrieved since the excess reflectance is zero. For R 0 > P, the surface reflectance actually decreases with τ, so τ could be retrieved from the loss of reflectance, e.g. over bright clouds. The value of P is derived from fitting the observed and retrieved surface reflectance spectra. For summer light haze at 0.412 μm, P=0.38. Accurate and automatic retrieval of the relevant aerosol P is the most difficult part of the co-retrieval process. Iteratively calculating P from the estimated τ( λ) is one possibility.

26. SeaWiFS Images and Spectra at Four Wavelengths (Click on the Images to View) At blue (0.412) wavelength, the haze reflectance dominates over land surface reflectance. The surface features are obscured by haze. Air scattering (not included) would add further reflectance in the blue. The blue wavelength is well suited for aerosol detection over land but surface detection is difficult. At green (0.555) over land, the haze is reduced and the vegetation reflectance is increased . The surface features are obscured by haze but discernable. Due to the low reflectance of the sea, haze reflectance dominates. The green not well suited for haze detection over land but appropriate for haze detection over the ocean and for the detection of surface features. At red (0.67) wavelength over land, dark vegetation is distinctly different from brighter yellow-gray soil. The surface features, particularly water (R 0 <0.01), vegetation (R 0 <0.04), and soil (R 0 <0.30) are are easily distinguishable. Haze reflectance dominates over the ocean. Hence, the red is suitable for haze detection over dark vegetation and the ocean as well as for surface detection over land. In the near IR (0.865) over land, the surface reflectance is uniformly high (R 0 >0.30) over both vegetation and soil and haze is not discernable . Water is completely dark (R 0 <0.01) making land and water clearly distinguishable. The excess haze reflectance over land is barely perceptible but measurable over water. Hence, the near IR is suitable for haze detection over water and land-water differentiation.

27. Aerosol effects on surface color and Surface effects on aerosol color The image was synthesized from the blue (0.412 μm), green (0.555 μm), and red (0.67 μm) channels of the 8 channel SeaWiFS sensor. Air scattering has been removed to highlight the haze and surface reflectance.

33. Results – Seasonal surface reflectance, Eastern US April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290

34. Results – Eight month animation