1. Satellite Remote Sensing of Aerosols
Pawan K Bhartia
Laboratory for Atmospheres
NASA Goddard Space Flight Center
Maryland, USA

2. Linkages A Priori information Aethalometer Nephalometer
Ground-based remote sensing
Satellite
remote sensing
Laboratory Measurements
In situ field Measurements
Aethalometer
Nephalometer
Particle Counters •
Direct-sun
Sky-radiance
Hem. Irradiance
Lidar
Solar occultation
Solar backscattered
Lidar
Chemical prop
Optical prop
Particle shape
ISSAOS 2008

3. Outline Basic Concepts Satellites/Instruments Model comparisons
Solar Occultation/ Limb scattering
Multi-spectral backscattered radiance
Multi-angle backscattered radiance
Polarization
Satellites/Instruments
The “A-train”
MODIS, MISR & OMI
Model comparisons
ISSAOS 2008

4. Solar Occultation & Limb Scattering
Limb Scatt: measures Laer,throughout the orbit, but much less accurate than occultation.
Occultation: measures text, sunrise & sunset only, twice per satellite orbit
Both methods limited to the stratosphere because of cloud interference
Ref: www-sage2.larc.nasa.gov/
ISSAOS 2008

5. A typical scene from a nadir-viewing satellite instrument
ISSAOS 2008

6. Backscattered Radiance MethodL
Backscattered radiance (watt/m2/nm/sr): L(q0,q,f0-f)
Top-of-the-atm Reflectance: r(q0,q,f0-f) =pL/I0cosq0
Surface reflectance: rs(q0,q,f0-f) q0 q
can be thought of as the Lambert-eqv reflectivity of the atmosphere. A Lambertian surface of reflectivity r will produce radiance L in the direction (q0,q,f0-f).
ISSAOS 2008

7. Properties of TOA Reflectance (r)
r=rRayl+raer+TRaylTaerrs+ …. higher order terms
inaccessible by satellite
Phase fn is small
For satellites, typically, raer=0.1taer
Therefore, to get ±0.05 precision in estimating taer one needs ±0.005 precision in estimating rs.
ISSAOS 2008

8. Reflectivity of Ocean
rs(q0,q,f0-f)=rFresnel+rwater-leaving+rwhite_caps
Solar glint q0 q
Fresnel Reflection:
q0=q and f0-f=180˚
independent of l
cone angle depends upon wind speed.
diffuse (l-dep) sky radiance is Fresnel reflected at all angles.
Water Leaving Radiance:
strongly l-dep, peaks at ~400 nm. Very small >500 nm.
reduced by chlorophyll and CDOM absorption, enhanced by sediments.
weak angular dependence.
White Caps:
important at high wind speeds only
ISSAOS 2008

9. Remote Sensing of Aerosols over open ocean
AVHRR Channel 1 (0.6 mm)
Ocean reflectivity at l>0.5 mm is very small at directions away from the solar glint direction, which allows accurate estimation of AOT from satellites
Over most of the open ocean, cloud contamination is the main error source.
UV/blue ls are much less suitable over ocean.
ISSAOS 2008

10. Estimation of size distribution from l-dependence of s or t
wt fns 2m l=
0.34
s is sensitive to a limited range of particle volumes
As W moves to right with increase in l, it samples larger particles
issue: W is very sensitive to REAL(m), which varies significantly.
ISSAOS 2008

11. Aerosol Remote Sensing Over Land
Land reflectivity is larger and highly variable, both spectrally and with viewing geometry, which makes it difficult to do aerosol remote sensing over land.
Several clever techniques have been devised to minimize the problem.
ISSAOS 2008

12. Why can’t one see aerosols over bright surfaces?
r=rRayl+raer+TRaylTaerrs+…
Since aerosols reflect light to space, as raer increases Taer decreases. This reduces the effect of aerosols when rs≠0.
At some surface reflectivity (rs), 2nd and 3rd terms can cancel, i.e., aerosols cannot be seen at all.
If aerosols are absorbing, they can decrease r over bright surfaces.
Dust storm over the Red Sea
ISSAOS 2008

13. Land Aerosols Techniques
r=rRayl+raer+TRaylTaerrs+ ….
Operational MODIS technique
In near IR r≈ rs for small particles
At other ls, estimate rs(l)=k(l) rs(IR), where k(l) are pre-tabulated
“Deep Blue” Technique
Takes advantage of the fact that deserts appear dark at blue wavelengths
Multi-angle Technique
ISSAOS 2008

14. Multi-angle Technique
Satellite motion
Because of the cosq term, raer becomes at large large q, hence surface contribution becomes smaller.
P(Q) also changes with Q providing phase fun information to help select the correct aerosol model to do retrieval. q1 q2 Q1 Q2
ISSAOS 2008

15. UV Remote Sensing of Aerosols
Large Rayleigh scattering makes UV unattractive for measuring aerosol scattering. (At 340 nm rRayl can be times larger than raer.)
In UV, aerosol absorption reduces the Rayleigh scattering from below the aerosol layer. This effect can be quite large if the aerosols are elevated.
Chief advantage of UV is that smoke and dust plumes can be detected over both dark and bright surfaces, including clouds, deserts, and snow/ice.
Retrieval algorithms exist to estimate tabs=text(1-w0) over dark surfaces.

16. How do aerosols absorb in the UV?
tabs=0.05 BC OC
Dust
ISSAOS 2008

17. Effect of aerosol absorption on UV reflectance ratio
UV Aerosol Index (UV-AI) is derived from the left-down shift of this curve due to aerosol absorption
The shift is proportional to tabs, but depends upon the height of the aerosol plume, higher the plume larger the shift.
Solar ZA: 45˚-55˚
Satellite ZA: 0˚-60˚
Azimuth= ~90˚
Curve Shifts due to aerosol absorption
Sky brightness
color Saturation
blue
gray
ISSAOS 2008

18. Smoke Desert Dust TOMS UV Aerosol Index
Smoke from Colorado fires (June 25, 2002)
Transport of Mongolian dust to N. America in April This image was made by compositing several days of TOMS data.
ISSAOS 2008

19. Satellites & Instruments

20. Older Instruments with Long Time Series
AVHRR on NOAA Polar Satellites
TOMS on Nimbus-7
Sea-WIFs
Eqv. AOT
UV-Aerosol Index
Dust plume image
ISSAOS 2008

21. 2008
2008

22. Aerosol Instruments on the A-Train
Aqua
Moderate Resolution Imaging Spectroradiometer (MODIS)
Terra (not part of the A-train)
MODIS
Multi-angle Imaging Spectroradiometer (MISR)
Aura
(UV aerosols) Ozone Monitoring Instrument (OMI)
Parasol
Multi-angle polarization measurement.
CALIPSO
Aerosol Lidar
ISSAOS 2008

23. MODerate-resolution Imaging Spectroradiometer [MODIS]
NASA, Terra & Aqua
launches 1999, 2001
705 km polar orbits, descending (10:30 a.m.) & ascending (1:30 p.m.)
Sensor Characteristics
36 spectral bands ranging from 0.41 to µm
cross-track scan mirror with 2330 km swath width
Spatial resolutions:
250 m (bands 1 - 2)
500 m (bands 3 - 7)
1000 m (bands )
2% reflectance calibration accuracy
onboard solar diffuser & solar diffuser stability monitor
Improved over AVHRR:
• Calibration
• Spatial Resolution
• Spectral Range & # Bands
Source: MODIS Team, NASA/GSFC 23

24. Fine to Coarse Mode Fraction
MODIS Results
AOT
Fine to Coarse Mode Fraction
ISSAOS 2008

25. 2007 minus 8-yr mean

26. While Indonesia’s smoke had a strong peak in 2006, S
While Indonesia’s smoke had a strong peak in 2006, S. America was more normal. This has a lot to do with wet/dry years and the opposite effects of El Niño on the two regions

27. Sudden Decrease In 2006 Koren et al. (2007)
MODIS aerosol products used to
identify interannual patterns.
Slopes of 6 year AOD
trend ( )
Strong
Increase
Of smoke
In 6 years
Sudden
Decrease
In 2006
Difference
Between
2006
And 2005
Decrease due to a combination
of a wetter year and small
rural farmers adhering to fire
control measures
Koren et al. (2007)

28. Multi-angle Imaging SpectroRadiometer
• Nine CCD push-broom cameras
• Nine view angles at Earth surface:
70.5º forward to 70.5º aft
• Four spectral bands at each angle:
446, 558, 672, 866 nm
• Studies Aerosols, Clouds, & Surface
Multi-angle Imaging SpectroRadiometer

29. MISR Monthly Global Aerosol Mid-VIS AOT
July 2005
• Land & Water
• Bright Surfaces
• Globe ~ weekly
• ~ 10:30 AM
[+ particle size, shape,
SSA constraints]
January 2005

30. Sensitivity to aerosols over bright surfaces
Thin haze over land is difficult to detect in the nadir view due to the brightness of the land surface
nadir
70º
Saudi Arabia,
Red Sea,
Eritrea
Over Bright Desert Sites, mid-vis. AOT to ± [Martonchik et al., GRL 2004] 30

31. MISR height analysis of World Trade Center plume
12 September 2001
MISR
70º image
MISR
stereo heights
of plume
patches
From: Stenchikov et al., J. Env. Fl. Mech., 2006 31

32. Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL)
POLDER instrument
6 km x 7 km nadir pixel
9 channels ( nm)
3 polarization channels (443, 670, 865 nm)
Best for detecting fine mode fraction and particle shape.
//smsc.cnes.fr/PARASOL/

33. Ozone Monitoring Instrument
Joint Dutch-Finish Instrument with
Dutch/Finish/U.S. Science Team
PI: P. Levelt, KNMI
Hyperspectral wide FOV Radiometer nm
13x24 km nadir footprint
Swath width 2600 km
2-dimensional CCD
wavelength
~ 780 pixels
~ 580 pixels
viewing angle
± 57 deg
flight direction
» 7 km/sec
13 km
(~2 sec flight))
2600 km
ISSAOS 2008
12 km/24 km (binned & co-added)

34. Absorbing Aerosols as seen by OMI
Dust
Smoke
Aerosol Transport across the Oceans in terms of the Absorbing Aerosol Index

35. Retrieving Aerosol Absorption in the near-UV
March 9, 2007
By means of an inversion algorithm AOD and SSA are derived

36. Model Comparisons

37. Alaska/Canada smoke transport
North America Boreal fire
In July 2004, large forest fires occurred in the North America boreal region. Smoke aerosols were being transported to large areas in Canada and the U.S., affecting regional air qualities.
Figures show the aerosol distributions of July 2004 over North America as seem by the MODIS and MISR satellite instruments and simulated by the GOCART model. Superimposed in circle are the aerosol optical depth measured by the AERONET sunphotometer network
NASA data used: MODIS, MISR, AERONET for aerosol optical depth, MODIS fire counts for modeling (Petrenko et al., AMS meeting, 2007).

38. MODIS, MISR, GOCART, AERONET: 200407
AERONET data in circles
AERONET data in circles
Feature: North America Boreal fire – captured by MODIS, MISR, GOCART
MODIS: Not available over bright surfaces (e.g., deserts) and cloudy regions (e.g., N. Pacific)
MISR: Not available over cloudy regions (N. Pacific, central America); excessive AOT over Greenland
GOCART: North America boreal fire emission or injection height maybe too low so smoke did not go far enough
AERONET data in circles

39. Aerosols in and : North America and Europe: Decrease from 2000 to East Asia: Increase from 2000 to Indonesia: Intense fire in October 2006
MODIS
GOCART
MODIS
GOCART

40. MODIS (Satellite) GOCART (Model)
The figures below show global aerosol distribution and transport observed by the MODIS instrument on EOS-Terra (left column) and simulated by the global model GOCART (right column) for April 13 (top row) and August 22 (bottom row), Red color indicates fine mode aerosols (e.g., pollution and smoke) and green color coarse mode aerosols (e.g., dust and sea-salt). Brightness of the color is proportional to the aerosol optical depth. On April 13, 2001, there are heavy dust and pollutions transported from Asia to the Pacific and dust transported from Africa to Atlantic; while on August 22 large smoke plumes from South America and Southern Africa are evident. Figure credit: Yoram Kaufman.
MODIS (Satellite)
GOCART (Model)

41. Trans-Pacific Transport of Dust
Simulated by GOCART (model)
Observed by TOMS (satellite)
TOMS AI April 11, 2001
Dust AOT April 11, GOCART
TOMS AI April 14, 2001
TOMS AI April 8, 2001
Dust AOT April 8, GOCART
Dust AOT April 14, GOCART
Trans-Pacific transport of dust in April Dust originating from Asian desert (April 8) is being transported across the Pacific and reaches North America (April 14). Left column: GOCART model simulation; right column: aerosol index from NASA satellite instrument TOMS (Chin et al., JGR 2003).

42. Contribution of Satellites in improving aerosol models
Improving the dust sources by comparing models with TOMS AI (Ginoux et al.).
Mass transport of dust and pollution aerosols using MODIS (Kaufman et. al. 2005)
MISR smoke plume height to improve smoke injection height.
MISR non-spherical particle fraction for evaluating model-derived dust and non-dust aerosols.

43. Further Reading
Nature, Vol 419, 12 Sept 2002
Yoram Kaufman

44. Passive Remote Sensing of Aerosols by Satellites- Future
New instruments will have MODIS-like spatial and spectral coverage with MISR and PARASOL-like multi-angle and polarization capability to determine ref index, size, and shape.
Advanced UV instruments may allow separation of OC and BC aerosols.
High spectral resolution O2-A band measurements may provide aerosol vert profile information with daily global mapping.

45. References

46. Some Satellite-Aerosol Product Web Sites
• MISR Home page; background, image gallery,..
• MISR, CERES, SAGE, MOPITT, TES, data & docs
• MODIS global browse imagery
• MODIS on-line visualization & analysis tools
• MODIS atmosphere products & docs
• MISR+MODIS climate data (surface emphasis)
• MODIS-UMD Fire products & docs
• MODIS-UMD global Fire occurrence mapper
• IDEA merged MODIS-EPA Air Quality
• UMBC Air Quality events
• TOMS/OMI aerosol & O3, data & docs
• NOAA AVHRR aerosols
• SeaWiFS data & docs
• AERONET AOT & properties, data & docs 46

47. Levy et al., 2nd generation MODIS Land algorithm, JGR, vol 112, (doi: /2006JD & /2006JD007811), 2007. 47