Atmospheric Correction of Optical Remotely Sensed Imagery Shunlin Liang Department of Geography

1. APEIS Capacity Building Workshop on Integrated Environmental Monitoring of Asia-Pacific Region20-21 September 2002, Beijing,, China Atmospheric Correction of Optical Remotely Sensed Imagery Shunlin Liang Department of Geography University of Maryland at College Park, USA

2. Outline • Introduction • MODIS atmospheric correction algorithms • Other correction methods and examples • Summary

4. Atmospheric effects • Gaseous Absorption • Water vapor • Ozone ( ) • and others • Particle Scattering • Rayleigh (Molecular) • Aerosol (large sizes)

5. Water vapor absorption

6. Water vapor

7. Water Vapor

8. Ozone

9. Ozone

10. CO2

11. CO2

12. Total transmittance(mid-latitude summer, nadir viewing)

13. Rayleigh Scattering * Optical depth decreases quickly as wavelength * Very stable in both time and space

14. Aerosol scattering

16. Outline • Introduction • MODIS atmospheric correction algorithms • Other correction methods and examples • Summary

17. 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

18. Differential absorption Methodsfor estimating water vapor content Two-band ratio: Three-band ratio:

19. Differential absorption Methodsfor estimating water vapor content

20. Aerosol Climatology

21. Aerosol climatology

23. 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

24. Estimation of aerosol optical depth(dark object approach)

25. Estimation of aerosol optical depth(dark object approach)

26. Surface reflectance retrieval

27. Major limitations (dark-object approaches) • Relies on empirical statistical relations • works only over vegetated surfaces

28. Outline • Introduction • MODIS atmospheric correction algorithms • Other correction methods and examples • Summary

29. 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

30. Other atmospheric correction methods • Histogram matching technique (ATCOR2 in ERDAS; Richter, 1996): • identify hazy regions using the Tasseled Cap transformation • match histograms of both clear and hazy regions. • Tasseled Cap transformation does not always work • approximate correction, not well for heterogeneous aerosols • uniform landscape

31. 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.

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

33. 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

34. Are bands 4,5 &7 hazy or there shadows? YES Histogram matching Determining clear and hazy regions NO Clustering analysis 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 Are near-IR bands hazy or there shadows? YES Histogram matching Determining clear and hazy regions NO Clustering analysis 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

36. ETM+ atmospheric correction: Case1

37. ETM+ atmospheric correction: case1

38. ETM+ atmospheric correction: Case 2

39. ETM+ atmospheric correction: case 2

40. ETM+ atmospheric correction: case 3

41. ETM+ atmospheric correction: case 3

42. AVIRIS( Airborne Visible InfraRed Imaging Spectrometer)

43. AVIRIS Imagery of Parana, Brazil acquired on August 23, 1995 Band 34 (673nm) Band 18 (549nm) Band 26 (627nm)

44. Atmospheric correction of AVIRIS Imagery Composite imagery of Parana, Brazil, August 23, 1995 Bands 26 (627nm), 34(673nm) and 46 (788nm)

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

46. SeaWiFS imagery of Washington DC area, Nov. 6, 2000

47. SeaWiFS imagery of Washington DC area, Nov. 6, 2000

48. 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.

49. MODIS imagery (northeastern coast, China May 7, 2000)

50. MODIS imagery of China northeastern coast, May 7, 2000