1. October 25, 2004, 9:00 a.m. - 12:00 p.m. Asheville, NC
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

2. Syllabus
9:00-9:30 Introduction to satellite aerosol detection and monitoring
9:30-10:00 Satellite Types and their Usage
10:00-10:30 Satellite detection of aerosol events: fires, dust storms, haze
10:30-10:45 Break
10:45-11:00 Satellite data and tools for the RPO FASTNET project
11:15-11:30 Satellite Data Use in AQ Management: Issues and Opportunities
11:30-12:00 Class-defined problems, feedback, discussion, exam(?)

3. Radiation detected by satellites
Air scattering depends on geometry and can be calculated (Rayleigh scattering)
Clouds completely obscure the surface and have to be masked out
Aerosols redirect incoming radiation by scattering and also absorb a fraction
Surface reflectance is a property of the surface

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

5. SeaWiFS Satellite Platform and Sensors
Swath
2300 KM
24/day
Polar Orbit: ~ 1000 km, 100 min.
Equator Crossing: Local Noon
Designed for Vegetation Detection
Chlorophyll Absorption
Satellite maps the world daily in 24 polar swaths
The 8 sensors are in the transmission windows in the visible & near IR
Designed for ocean color but also suitable for land color detection, particularly of vegetation

6. Key aerosol microphysical parameters
Particle size and size distribution
Aerosol particles > 1 mm in size are produced by windblown dust and sea salt from
sea spray and bursting bubbles. Aerosols smaller than 1 mm are mostly formed by
condensation processes such as conversion of sulfur dioxide (SO2) 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 v0
 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 a
 exponent of power law representation of extinction vs. wavelength

8. Remote Sensing Overview
What is “remote sensing”?
Using artificial devices, rather than our eyes, to observe or measure things from a distance without disturbing the intervening medium
It enables us to observe & measure things on spatial, spectral, & temporal scales that otherwise would not be possible
It allows us to observe our environment using a consistent set of measurements throughout the globe, without prejudice associated with national boundaries and accuracy of datasets or timeliness of reporting
How is remote sensing done?
Electromagnetic spectrum
Passive sensors from the ultraviolet to the microwave
Active sensors such as radars and lidars
Satellite, airborne, and surface sensors
Training and validation sites

9. Remote Sensing Applications to be Covered in this Course
History of remote sensing & global change
Remote sensing of land surface properties
Spectral and angular reflectance, land cover & land cover change
Fire monitoring and burn scars
Leaf area index & flux of photosynthetically active radiation
Temperature & emissivity separation of terrestrial surfaces
Remote sensing of atmospheric properties
Cloud cover, cloud optical properties, and cloud top properties
Aerosol properties
Water vapor
Atmospheric chemistry (carbon monoxide and methane)
Earth radiation budget and cloud radiative forcing
Remote sensing of the oceans from space
Chlorophyll concentration and biological productivity of the oceans
Sea surface temperature using thermal methods
Angular directional models of the Earth-atmosphere-ocean system

10. The Electromagnetic Spectrum
Remote sensing uses the radiant energy that is reflected and emitted from Earth at various “wavelengths” of the electromagnetic spectrum
Our eyes are only sensitive to the “visible light” portion of the EM spectrum
Why do we use nonvisible wavelengths?
Michael D. King, EOS Senior Project Scientist

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
diffuse reflector (lambertian)
Specular reflector (mirror)
Nearly Specular reflector (water)
nearly diffuse reflector
Hot spot reflection
E. Vermote, 2002

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

18. Aerosol and Surface Radiative Transfer
Major Assumptions
Gaseous scattering and absorption is subtractable from the sensed radiation – multiple scattering is negligible.
All the remaining solar radiation reaches the surface directly or diffusely –small backscattering fraction.
Backscattering to space is due to incoming solar radiation – low surface reflectance.
I0 – Intensity of the incoming radiation.
R0- surface reflectance. Depends on surface type as well as the incoming and outgoing angles
R- surface reflectance sensed at the top of the atmosphere as perturbed by the atmosphere
P - aerosol angular reflectance function; includes absorption, P = ω p

19. Apparent Surface Reflectance, R
The surface reflectance R0 is obscured by aerosol scattering and absorption before it reaches the sensor
Aerosol acts as a filter of surface reflectance and as a reflector solar radiation
R = (R0 + (e-t – 1) P) e-t
Aerosol as Filter: Ta = e-t
Aerosol as Reflector: Ra = (e-t – 1) P
Surface reflectance R0
The apparent reflectance , R, detected by the sensor is: R = (R0 + Ra) Ta
Under cloud-free conditions, the sensor receives the reflected radiation from surface and aerosols
Both surface and aerosol signal varies independently in time and space
Challenge: Separate the total received radiation into surface and aerosol components

20. Apparent Surface Reflectance, R
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, R0, P/ R0
Aerosols will increase the apparent surface reflectance, R, if P/R0 < 1. For this reason, the reflectance of ocean and dark vegetation increases with τ.
When P/R0 > 1, aerosols will decrease the surface reflectance. Accordingly, the brightness of clouds is reduced by overlying aerosols.
At P~ R0 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

21. Increased Reflectance
Reduced Reflectance
Increased Reflectance
Reflectance
Reflectance

22. The aerosol τ can also be estimated from the loss of surface contrast.
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/ R0
Note: For horizontal vision against the horizon sky, P/R0 = 1, contrast decays exponentially with τ, C/C0=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 R0 and the aerosol reflectance function, P are known. The excess reflectance due to aerosol is : R- R0 = (P- R0)(1-e- τ) and the optical depth is:
For a black surface, R0 =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 R0 increases, the same excess reflectance corresponds to increasing values of τ.
When R0 ~P the aerosol τ can not be retrieved since the excess reflectance is zero. For R0 > 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 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.

24. 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 is removed to highlight the haze and surface reflectance.

25. Aerosol Effect on Surface Color and Surface Effect on Aerosol
Aerosols add to the reflectance and sometimes reduce the reflectance of surface objects
Aerosols always diminish the contrast between dark a bright surface objects
Haze and smoke aerosols change the color of surface objects to bluish while dust adds a yellowish tint. (Click on the Images to View)
Dark surfaces like ocean and dark vegetation makes the aerosol appear bright.
Bright surfaces like sand and clouds makes the aerosol invisible.

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 (R0<0.01), vegetation (R0<0.04), and soil (R0<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 (R0>0.30) over both vegetation and soil and haze is not discernable. Water is completely dark (R0<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.

28. Preprocessing Transform raw SeaWiFS data
Georeferencing – warping data to geographic lat/lon coordinates with a pixel resolution of ~ 1.6 km
Splicing – mosaic data from adjacent swaths to cover entire domain
Rayleigh correction – remove scattering by atmospheric gases and convert to reflectance units
Scattering angle correction – normalize all pixels to remove reflectance dependence on sun-target-sensor angles
Result is daily apparent reflectance, R for all 8 channels

29. General Approach: Co-Retrieval of Surface and Aerosol Reflectance
Surface Reflectance Retrieval by Time Series Analysis
(Sean Raffuse, MS Thesis 2003)
Aerosol Retrieval over Land
Radiative transfer model + Surface data
Refined Surface Reflectance
Iteration back to 1., 2. …

30. Approach – Time Series Analysis
For any location (pixel), the sensor detects a “clean” day periodically
Aerosol scattering (haze) is near zero
Pixel must also be free of other interferences
Clouds
Cloud shadows

31. Methodology – Cloud shadows
Clouds are easily detected by their high reflectance values
Cloud shadows are found in the vicinity of clouds
We enlarge the cloud mask by a three-pixel ‘halo’ to remove cloud shadows
Cloud shadows reduce the apparent surface reflectance considerably in all channels

32. Methodology – Preliminary anchor days
Surface reflectance is retrieved for individual pixels from time series data (e.g. year)
The procedure first identifies a set of ‘preliminary clear anchor’ days in a 17-day moving window
The main interferences (clouds and haze) tend to increase the apparent surface reflectance, especially in the low wavelength channels
The anchor day is chosen as the day with the minimum sum of the lowest four channels

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

35. Retrieval Procedures
Rayleigh air scattering and gaseous absorption is removed first by the E. Vermote algorithm.
Cloudy pixels are masked out since they obscure the surface and aerosol reflectance
The remaining reflectance over land and water consists of the combined effect of aerosol scattering/absorption and surface reflectance.
The goal of the co-retrieval is to separate the reflectance due to aerosol from surface reflectance