liang apeis capacity building workshop on integrated environmental monitoring of asia-pacific region...

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APEIS Capacity Building Workshop on Integrated Environmental Monitoring of Asia-Pacific Region

20-21 September 2002, Beijing,, China

Atmospheric Correction of Optical Remotely Sensed Imagery

Shunlin Liang

Department of Geography

University of Maryland at College Park, USA

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Outline

IntroductionMODIS atmospheric correction algorithmsOther correction methods and examplesSummary

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Atmospheric effects

Gaseous AbsorptionWater vaporOzone ( ) and others

Particle ScatteringRayleigh (Molecular)Aerosol (large sizes)

2CO3O

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Water vapor absorption

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Water vapor

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Water Vapor

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Ozone

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Ozone

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CO2

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CO2

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Total transmittance(mid-latitude summer, nadir viewing)

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Rayleigh Scattering

* Optical depth decreases quickly as wavelength

* Very stable in both time and space

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Aerosol scattering

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Outline

IntroductionMODIS atmospheric correction algorithmsOther correction methods and examplesSummary

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MODIS atmospheric correction

Water absorption estimation and correction (MOD05) – Dr. Gao Bo-Cai

Aerosol estimation (MOD04) – Dr. Yoram Kaufman

Surface reflectance retrieval (MOD-09) – Dr. Eric Vermote

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Differential absorption Methods for estimating water vapor content

Two-band ratio:

Three-band ratio:

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Differential absorption Methods for estimating water vapor content

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Aerosol Climatology

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Aerosol climatology

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Estimation of aerosol optical depth(dark object approach)

Step 1: low surface reflectance at 2.2 um

Step 2: surface reflectance at red and blue

Step 3: aerosol properties from TOA radiances

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Estimation of aerosol optical depth(dark object approach)

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Estimation of aerosol optical depth(dark object approach)

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Surface reflectance retrieval

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Major limitations (dark-object approaches)

Relies on empirical statistical relations

works only over vegetated surfaces

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Outline

IntroductionMODIS atmospheric correction algorithmsOther correction methods and examplesSummary

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Other atmospheric correction methods

Invariant object regression method for temporal evaluation (Hall, et al.,

1991): find a set of pixels whose reflectance values do not change significantly

under different solar and atmospheric conditions simple and easy implementation relative correction uniform aerosol distribution

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Other atmospheric correction methods

Histogram matching technique (ATCOR2 in ERDAS; Richter, 1996): identify hazy regions using the Tasseled Cap

transformationmatch histograms of both clear and hazy regions.Tasseled Cap transformation does not always work approximate correction, not well for heterogeneous

aerosols uniform landscape

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Other atmospheric correction methods

Dark-object algorithms for TM Imagery

Liang S., H. Fallah-Adl, S. Kalluri, J. JaJa, Y. J. Kaufman, and J. R. G. Townshend, (1997), An Operational Atmospheric Correction Algorithm for Landsat Thematic Mapper Imagery over the Land, J. Geophys. Res. - Atmosphere,102:17173-17186.

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Atmospheric correction examples (Liang, et al., J. Geophys. Res., 1997)

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Cluster matching method

Liang, S., H. Fang, M. Chen, (2001), Atmospheric Correction of Landsat ETM+ Land Surface Imagery: I. Methods, IEEE Transactions on Geosciences and Remote Sensing 39:2490-2498.

Liang, S., H. Fang, J. Morisette, M. Chen, C. Walthall, C. Daughtry, and C. Shuey, (2002), Atmospheric Correction of Landsat ETM+ Land Surface Imagery: II. Validation and Applications, IEEE Transactions on Geosciences and Remote Sensing, in press

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Are bands 4,5 &7 hazy or there shadows?

Histogram matching

Clustering analysis

Determining clear and hazy regions

Determining reflectance of clear regions

Mean reflectance matching of each cluster in both clear & hazy regions

Look-up tables searching for aerosol optical depth

Spatial smoothing of the estimated aerosol optical depth

Reflectance retrieval by considering adjacency effects

YES

NO

Are near-IR bands hazy or there shadows?

Histogram matching

Clustering analysis

Determining clear and hazy regions

Determining reflectance of clear regions

Mean reflectance matching of each cluster in both clear & hazy regions

Look-up tables searching for aerosol optical depth

Spatial smoothing of the estimated aerosol optical depth

Reflectance retrieval by considering adjacency effects

YES

NO

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ETM+ atmospheric correction: Case1

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ETM+ atmospheric correction: case1

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ETM+ atmospheric correction: Case 2

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ETM+ atmospheric correction: case 2

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ETM+ atmospheric correction: case 3

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ETM+ atmospheric correction: case 3

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AVIRIS ( Airborne Visible InfraRed Imaging Spectrometer)

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AVIRIS Imagery of Parana, Brazil acquired on August 23, 1995

Band 18 (549nm) Band 26 (627nm) Band 34 (673nm)

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Atmospheric correction of AVIRIS Imagery

Composite imagery of Parana, Brazil, August 23, 1995 Bands 26 (627nm), 34(673nm) and 46 (788nm)

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Sea-viewing Wide Field-of-view Sensor (SeaWiFS)

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SeaWiFS imagery of Washington DC

area, Nov. 6, 2000

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SeaWiFS imagery of Washington

DC area, Nov. 6, 2000

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MODIS (Moderate Resolution Imaging Spectroradiometer)

MODIS is the key instrument aboard the Terra and Aqua satellites. Terra/Aqua MODIS is viewing the entire Earth's surface every 1 to 2

days, acquiring data in 36 spectral bands.

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MODIS imagery (northeastern coast,

China May 7, 2000)

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MODIS imagery of China northeastern

coast, May 7, 2000

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MODIS imagery of China northeastern

coast, May 7, 2000

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Summary

Atmospheric correction is very critical in monitoring land surfaces, particularly for regions with frequently cloudy and hazy conditions

There exist many different algorithms, but further developments are needed for global applications (inter-comparision, calibration and validation)

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References

Kaufman, Y, 1989. The Atmospheric Effect on Remote Sensing and Its Correction, in Theory and Applications of Optical Remote Sensing, G. Asrar (Ed.) John Wiley & Sons

Liang, S. Quantitative Remote Sensing of Land Surfaces, John Wiley & SonsCh2: atmospheric radiative transfer modelingCh6: atmospheric correction methods

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Thank you !