1 / 62

MODIS Land Validation Workshop Jan. 22-23, 2001

MODIS Land Validation Workshop Jan. 22-23, 2001. Validating MODIS land surface reflectance and albedo products Shunlin Liang Department of Geography University of Maryland at College Park Charlie Walthall and Craig Daughtry Hydrology and Remote Sensing Laboratory

catherinemurphy
Download Presentation

MODIS Land Validation Workshop Jan. 22-23, 2001

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MODIS Land Validation WorkshopJan. 22-23, 2001 Validating MODIS land surface reflectance and albedo products Shunlin Liang Department of Geography University of Maryland at College Park Charlie Walthall and Craig Daughtry Hydrology and Remote Sensing Laboratory USDA Beltsville Agricultural Research Center

  2. Products and Validation Methods • MODIS products to validate: • MOD09: surface reflectance from atmospheric correction • MOD43: BRDF & albedo • Validation Methods: • field campaigns at USDA/BARC • scaling up from high-resolution imagery • comparisons with independent algorithms

  3. USDA BARC EOS Land Core Validation Site

  4. Remotely sensed data over BARC • Satellite data • ETM+ (July 28,1999, May 11, 2000, Oct.4, 2000, Nov.3, 2000, Dec. 5, 2000) • IKONOS (June 3, 2000; Sept. 29, 2000) • ASTER (May 11, 2000) • TM (10+) • MODIS, MISR, GOES, AVHRR, SeaWiFS • Airborne data • AVIRIS (May 11, 2000, May 24, 2000) • MAS (May 24, 2000) • ASIA (many), POSITIVE (one)

  5. Field Campaigns • Field campaigns in 1998, 1999 • Field campaigns in 2000 • May 11 • August 4 • October 3 • Data collected • Reflectance spectra - ASD • Broadband albedos (albedometer) • LAI & leaf optics

  6. Up-scaling Point Measurements MODIS Products High-resolution images (ETM+, IKONOS, etc) Calibration Aggregation

  7. Upscaling Algorithm Development • Atmospheric correction • Liang, S., H. Fang, M. Chen, Atmospheric correction of Landsat ETM+ imagery, I. Method, IEEE Trans. Geos. Remote Sens., submitted. • Liang, S., H. Fang, M. Chen, J. Moriseette, C. Walthall, and C. Daughtry, Atmospheric correction of Landsat ETM+ imagery, II, validation and applications, IEEE Trans. Geos. Remote Sens., submitted. • Narrowband to broadband albedo conversion • Liang, S., Narrowband to Broadband Conversions of Land Surface Albedo. I.Algorithms, Remote Sensing of Environment, in press. • Aggregation laws • Liang, S., Numerical Experiments on Spatial Scaling of Land Surface Albedo and Leaf Area Index, Remote Sensing Reviews, in press.

  8. Limitations of the current 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

  9. Limitations of the current 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

  10. Limitations of the current methods • Dark-object algorithms(e.g., Kaufman and Remer, 1994, Liang, et al., 1997): • identify dense canopy pixels using band 7 • rely on empirical surface reflectance relations between band 7 and bands 1 and 3 • uniform dense vegetation distribution • stable empirical relations

  11. New Algorithm (Liang, et al., 2000a,b) Aerosol estimation: • clustering analysis using bands 4,5 and 7 • multiple choices for determining high aerosol loading regions, including the use of high-end radiance, TC transformation and manual drawing • mean reflectance matching and spatial smoothing to determine the aerosol distribution • minimum band reflectance for an absolute correction • Adjacency effects • empirical formula of "effective" reflectance from extensive 3D atmospheric radiative transfer simulations

  12. Atmospheric correction examples (Liang, et al., 1997)

  13. ETM+ atmospheric correction

  14. ETM+ atmospheric correction

  15. ETM+ atmospheric correction

  16. ETM+ atmospheric correction

  17. ETM+ atmospheric correction

  18. ETM+ atmospheric correction

  19. ETM+ imagery acquired on July 28,1999

  20. July-28-99 Band 1 ETM+ imagery

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

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

  23. IKONOS imagery of USDA/BARC, June 3, 2000

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

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

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

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

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

  29. Narrowband to broadband albedo conversion Liang, S., (2000), Narrowband to broadband conversions of land surface albedo. I. Algorithms, Remote Sensing of Environment, in press. (ASTER, AVHRR, ETM+/TM,GOES, MISR, MODIS, POLDER, VEGETATION)

  30. Narrowband to broadband albedo conversions • Most formulaeare based on either field measurements or radiative transfer simulations • Limited number of measured data or reflectance spectra in simulations • An innovative method to incorporate hundreds of spectra and atmospheric conditions (Liang et al., 1999; Liang, 2000) • Same database for multiple sensors (ASTER, AVHRR, TM/ETM+, MODIS, MISR, POLDER) and multiple broadband albedos: total shortwave, total visible and near-IR, direct and diffuse visible and near-IR. • Landsat TM shortwave albedo conversion formulae have good agreements with those developed by Duguay and LeDrew (1992) and Knap, et al. (1999).

  31. BRDF/albedo scaling • Liang, S., Numerical Experiments on Spatial Scaling of Land Surface Albedo and Leaf Area Index, Remote Sensing Reviews, in press. • BRDF/albedo scaling from 30m to 1km is linear; • LAI scaling from 30m to 1km is nonlinear (i.e., the simple average will not provide the right answer).

  32. Preliminary validation results • Validating ETM+ surface reflectance • Validating ETM+/MODIS narrowband to broadband albedo conversion • Comparisons of MODIS three black-sky albedo (MOD43B3) with aggregated ETM+ albedo products • Comparisons of MODIS equivalent nadir-view reflectance (MOD43B4) with aggregated ETM+ surface reflectance • Comparisons of MODIS surface reflectance (MOD09) with predicted from MOD43B1

  33. May 11, 2000

  34. Oct.2, 2000

  35. Nov.3, 2000

  36. Dec. 5, 2000

  37. Validating retrieved ETM+ surface reflectance using simultaneous ASD measurements (May 11, 2000)

  38. Inter-comparison of “invariant” object reflectance retrieved from two ETM+ imagery

  39. ETM+/IKONOS/MODIS conversion formulae validation • Two field campaigns at USDA/BARC • May 11, 2000 • August 4, 2000 • ASD spectral reflectance measurements • Albedometer broadband albedo measurements

  40. Mean reflectance of different cover types (May)

  41. Mean reflectance of different cover types (August)

  42. Three broadband albedos (Oct. 2, 2000)

  43. ETM+/MODIS registration

  44. Validating MODIS “black-sky” broadband albedos • Two ETM+ (Oct.2, Nov.3) • Atmospheric correction • Narrowband to broadband conversion • Spatial aggregation • MOD43B3: 16-day composite

  45. Mean diff:0.0083 Diff std:0.0301 Mean diff: -0.016 Diff std: 0.0266

More Related