1. Remote Sensing of Cloud, Aerosol, and Land Properties from MODIS
Michael D. King,1 Steven Platnick,1,2 D. Allen Chu,1,3 and Eric G. Moody,1,4
1NASA Goddard Space Flight Center
2University of Maryland Baltimore County
3Science Systems and Applications Inc.
4Emergent Information Technologies, Inc.
Outline
MODIS atmosphere products
Granule level (5 min.) product examples
Global examples
Summary

2. Global Level-1B Composite Image
R = 0.65 µm
G = 0.56 µm
B = 0.47 µm
May 28, 2001

3. Global Level-1B Composite Image
R = 0.65 µm
G = 0.56 µm
B = 0.47 µm
May 28, 2001
example data granule coverage (5 min)

4. MODIS Atmosphere Products
Pixel-level (level-2) products
Cloud mask for distinguishing clear sky from clouds 47.4 MB/file)
Cloud radiative and microphysical properties 44.6 MB/file)
Cloud top pressure, temperature, and effective emissivity
Cloud optical thickness, thermodynamic phase, and effective radius
Thin cirrus reflectance in the visible
Aerosol optical properties 10.5 MB/file)
Optical thickness over the land and ocean
Size distribution (parameters) over the ocean
Atmospheric moisture and temperature gradients 32.3 MB/file)
Column water vapor amount 12.7 MB/file)
Gridded time-averaged (level-3) atmosphere product
Daily, 8-day, and monthly products (409.2, 781.9, MB)
1° ´1° equal angle grid
Mean, standard deviation, marginal probability density function, joint probability density functions
modis-atmos.gsfc.nasa.gov

5. Cloud Mask
MODIS cloud mask uses multispectral imagery to indicate whether the scene is clear, cloudy, or affected by shadows
Cloud mask is input to rest of atmosphere, land, and ocean algorithms
Mask is generated at 250 m and 1 km resolutions
Mask uses 17 spectral bands ranging from µm (including new 1.38 µm band)
11 different spectral tests are performed, with different tests being conducted over each of 5 different domains (land, ocean, coast, snow, and desert)
temporal consistency test is run over the ocean and at night over the desert
spatial variability is run over the oceans
Algorithm based on radiance thresholds in the infrared, and reflectance and reflectance ratio thresholds in the visible and near-infrared
Cloud mask consists of 48 bits of information for each pixel, including results of individual tests and the processing path used
bits 1 & 2 give combined results (confident clear, probably clear, probably cloudy, cloudy)

6. Level-1B Image of Namibian Stratus during SAFARI 2000
September 13, 2000
Angola
marine stratocumulus
Etosha Pan
Namibia
Red = 0.65 µm
Green = 0.56 µm
Blue = 0.47 µm

7. Cloud Mask of Namibian Stratus

8. Cloud Properties
Twelve MODIS bands are utilized to derive cloud properties
0.65, 0.86, 1.24, 1.38, 1.64, 2.13, 3.75, 8.55, 11.03, 12.02, 13.34, and µm
Visible and near-infrared bands
daytime retrievals of cloud optical thickness and effective radius
1.6 µm band will be used to derive thermodynamic phase of clouds during the daytime (post-launch)
Thermal infrared bands
determination of cloud top properties, including cloud top altitude, cloud top temperature, and thermodynamic phase

9. Liquid Water Clouds - ocean surface
Retrieval of tc and re
The reflection function of a nonabsorbing band (e.g., 0.86 µm) is primarily a function of optical thickness
The reflection function of a near-infrared absorbing band (e.g., 2.14 µm) is primarily a function of effective radius
clouds with small drops (or ice crystals) reflect more than those with large particles
For optically thick clouds, there is a near orthogonality in the retrieval of tc and re using a visible and near-infrared band
Liquid Water Clouds - ocean surface

10. Ice Clouds - ocean surface
Retrieval of tc and re
The reflection function of a nonabsorbing band (e.g., 0.86 µm) is primarily a function of optical thickness
The reflection function of a near-infrared absorbing band (e.g., 2.14 µm) is primarily a function of effective radius
clouds with small drops (or ice crystals) reflect more than those with large particles
For optically thick clouds, there is a near orthogonality in the retrieval of tc and re using a visible and near-infrared band
Ice Clouds - ocean surface

11. Liquid Water Clouds - ocean surface
Retrieval of tc and re
The reflection function of a nonabsorbing band (e.g., 0.86 µm) is primarily a function of optical thickness
The reflection function of a near-infrared absorbing band (e.g., 2.14 µm) is primarily a function of effective radius
clouds with small drops (or ice crystals) reflect more than those with large particles
For optically thick clouds, there is a near orthogonality in the retrieval of tc and re using a visible and near-infrared band
Liquid Water Clouds - ocean surface

12. Liquid Water Clouds - ice surface
Retrieval of tc and re
The reflection function of a nonabsorbing band (e.g., 0.86 µm) is primarily a function of optical thickness
The reflection function of a near-infrared absorbing band (e.g., 2.14 µm) is primarily a function of effective radius
clouds with small drops (or ice crystals) reflect more than those with large particles
For optically thick clouds, there is a near orthogonality in the retrieval of tc and re using a visible and near-infrared band
Liquid Water Clouds - ice surface

13. MOD12 (IGBP ecosystem classification) + USGS water + tundra
Ecosystem Map
MOD12 (IGBP ecosystem classification) + USGS water + tundra
For the time being we’re using ecosystem as a surrogate for surface albedo.

14. Surface Albedo
Surface albedo = ecosystem + MOD43 (Strahler, Schaaf et al.) aggregation
For the time being we’re using ecosystem as a surrogate for surface albedo.

15. Example spectral surface albedos (ecosystem + MOD43 aggregation)
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00

16. Weighting Functions for CO2 Slicing
CO2 slicing method
ratio of cloud forcing at two near-by wavelengths
assumes the emissivity at each wavelength is same, and cancels out in ratio of two bands
The more absorbing the band, the more sensitive it is to high clouds
technique the most accurate for high and middle clouds
MODIS is the first sensor to have CO2 slicing bands at high spatial resolution (5 km)
technique has been applied to HIRS data for ~20 years

17. Cloud Top Pressure Level-3 Daily May 28, 2001 pc (hPa) 1000 850 700
550
400
250
100

18. Cloud Top Temperature Level-3 Daily May 28, 2001 c (K) 300 280 260
240
220
200

19. Cloud Optical Thickness
Level-3 Daily
May 28, 2001 tc 20 16 12 8 4

20. Cloud Effective Particle Radius
Level-3 Daily
May 28, 2001
re(µm) 50 40 30 20 10

21. Atmospheric Water Vapor
April 12, 2000
R: 0.65, G: 0.86, B: 0.46
Precipitable Water Vapor (cm)

22. Precipitable Water over Land and Sunglint
Level-3 Daily
May 28, 2001
q (cm)
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0

23. Aerosol Properties
Seven MODIS bands are utilized to derive aerosol properties
0.47, 0.55, 0.65, 0.86, 1.24, 1.64, and 2.13 µm
Ocean
reflectance contrast between cloud-free atmosphere and ocean reflectance (dark)
aerosol optical thickness ( µm)
size distribution characteristics (fraction of aerosol optical thickness in the fine particle mode; effective radius)
Land
dense dark vegetation and semi-arid regions determined where aerosol is most transparent (2.13 µm)
contrast between Earth-atmosphere reflectance and that for dense dark vegetation surface (0.47 and 0.66 µm)
enhanced reflectance and reduced contrast over bright surfaces (post-launch)
aerosol optical thickness (0.47 and 0.66 µm)

24. Gobi Dust Outbreak over China & Korea
March 20, 2001
ta (0.55 µm)
2.0
1.5
1.0
0.5
0.0

25. Frequency of Aerosol Retrievals20 40 60 80
100
Fraction of Aerosol Retrievals for 150 days

26. Aerosol Optical Thickness & Effective Radius
ta (0.55 µm) re
0.5
2.4
0.4
1.8
0.3
1.2
0.2
0.6
0.1
0.0
0.0

27. Continental to Regional Scale Pollution in Northern Italy
September 25, 2000
600 km
400 km
2030 km
2.0
a(0.55 µm)
1.0
Ispra
Venice
2330 km
0.0

28. How Do Terra Aerosol Properties Compare to the Daily Cycle?

29. MODIS Surface Reflectance Product for Asia

30. MODIS Atmosphere Web Site
modis-atmos.gsfc.nasa.gov
Images
Validation
News
Staff
Staffing plan
References
Presentations
Publications
ATBDs & Plans
Tools
Granule locator tools
Spatial and dataset subsetting
Visualization and analysis
Data products
Format and content
Sample images
Known problems
Modification history
Acquiring data

31. Ordering MODIS Data Level-2 Products Release (beta or provisional)
Data processing facility
Data dates
Processing dates
Software version number

32. Ordering MODIS Data Level-3 Products Release (beta or provisional)
Data processing facility
Data dates
Processing dates
Software version number

33. Near-Term Plans for MODIS Data Processing
Updated algorithms submitted in May and integrated into production
Consistent year processing (November 2000-November 2001)
Will test both forward production and parallel reprocessing to fill in a continuous year of production
Should be completed near the time of the Aqua launch
Reprocessing of validation data
ARM SGP mission (March 2000)
SAFARI 2000 (August-September 2000)
Products released thus far
35 products have been released to the public
All atmosphere, land, and ocean products have been released (provisional version)
Users are encouraged to order products from the DAACs

34. Summary
MODIS provides an unprecedented opportunity for atmospheric studies
36 spectral channels, high spatial resolution
Comprehensive set of atmospheric algorithms
Archive of pixel level retrievals and global statistics
Validation activities are high priority (ground-based, in situ, aircraft, and satellite intercomparisons)