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Uncertainties of Global Moderate Resolution Leaf Area Index (LAI) Products Derived from Satellite Data

This study explores the uncertainties of global moderate resolution Leaf Area Index (LAI) products derived from satellite data and evaluates their ability to meet observational requirements. The validation results show that the current LAI products are still unable to meet the accuracy requirement proposed by the Global Climate Observation System (GCOS).

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Uncertainties of Global Moderate Resolution Leaf Area Index (LAI) Products Derived from Satellite Data

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  1. Uncertainties of global moderate resolution Leaf Area Index (LAI) products derived from satellite data Hongliang Fanga, Shanshan Weia,b, Shunlin Liangc aLREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. bDepartment of Geography, School of Urban and Environmental Sciences, Northeast Normal University, Changchun, Jilin Province, 130024, China. cDepartment of Geography, University of Maryland, College Park, Maryland, 20742, USA. IGARSS’01, Vancouver, Canada, Jul 24-27, 2011

  2. Outline • Introduction • Validation method • Results • Conclusions

  3. Background • Leaf Area Index (LAI): the one-sided green leaf area per unit of ground area in broadleaf canopies and the projected needle leaf area in coniferous canopies (Myneni et al., 2002; Chen and Cihlar, 1996) • An Essential Climate Variable (ECV) necessary fo many process models • Observational requirement by the Global Climate Observation System (GCOS): ±0.5(GCOS, 2006)

  4. Four stages of validation defined by the Committee on Earth Observation Satellites (CEOS) Adapted From LPV/WGCV/CEOS (http://lpvs.gsfc.nasa.gov/)

  5. Objectives • To extend the Stage 3 validation for both MODIS and CYCLOPES LAI products with a global field measurement database. • To investigate whether the current global LAI products could meet the observational requirements proposed by GCOS. • MODIS suite: Terra C4 (MOD15 C4), Terra C5 (MOD15 C5) and Terra+Aqua C5 (MCD15 C5) • SPOT/VEGETATION CYCLOPES V3.1 • Consideration of the MODIS quality control (QC) layer and the CYCLOPES status mask (SM)

  6. Outline • Introduction • Validation method • Results • Conclusions

  7. Validation schemes • Direct comparison with in situ data collected over validation sites (this study); • Bridging method: comparison with products derived from high resolution airborne or spaceborne sensors (e.g., Landsat TM/ETM+); • Cross-validation with other independently obtained products; • Intercomparison and analysis with process model simulations. (http://lpvs.gsfc.nasa.gov/; Morisette et al, 2006; Justice et al., 2002)

  8. Direct field measurement • Destructive sampling or collection of total leaf litterfall. • Calculation through the specific leaf area (SLA: square centimeters of fresh leaf area per gram of dry foliage mass) in the laboratory. Multiplication of the SLA and total dry mass of each foliage age class to calculate the LAI. • Allometric method, based on the relationship between leaf area and the diameter at breast height (DBH).

  9. Indirect field measurement • Indirect contact methods, e.g, the point quadrats method. • LAI 2000 and hemispherical photography with no clumping correction (Effective LAI). • LAI 2000, TRAC and hemispherical photography with clumping correction (True LAI).

  10. Direct validation campaigns • BigFoot (Cohen & Justice, 1999) • CCRS (Fernandes et al., 2003) • MODLAND (Morisette et al., 2002) • VALERI (Baret et al., 2006) • CEOS LPV (Morisette et al., 2006) Share ground LAI data and maps among the entire community

  11. Global field LAI measurement sitesfrom campaigns and literature 219 observations over 129 sites Fang et al., to be submitted.

  12. MODIS and CYCLOPES quality indicators

  13. Outline • Introduction • Validation method • Results • Conclusions

  14. Statistics of field measured LAI Fang et al., to be submitted.

  15. MODIS/Terra C4 (QC<128) Main: 85.8% R2=0.435 RMSE=1.42 MODIS/Terra C5 (QC<128) Main: 92.5% R2=0.307 RMSE=1.53 VGT/CYCLYPES V3.1 (LAI<6.0) R2=0.557 RMSE=0.97 MODIS/Terra+Aqua C5 (QC<128) Main: 97.6% R2=0.526 RMSE=1.09 Field true LAI

  16. MODIS/Terra C4 (QC<128) R2=0.234 RMSE=2.08 MODIS/Terra C5 (QC<128) R2=0.290 RMSE=1.74 VGT/CYCLYPES V3.1 (LAI<6.0) R2=0.399 RMSE=1.34 MODIS/Terra+Aqua C5 (QC<128) R2=0.186 RMSE=1.63 Field effective LAI

  17. Comparison of MODIS and CYCLOPES LAI with field LAI

  18. Comparison of best MODIS (QC=0) and CYCLOPES (SM=0) with field LAI

  19. MOD15 C5 (QC<64) MCD15 C5 (QC<64) SPOT/VGT CYCLOPES 2000.1-2005.12

  20. Global Monthly Average MODIS, CYCLOPES and GLOBCARBON LAI

  21. Outline • Introduction • Validation method • Results • Conclusions

  22. MODIS LAI has improved consistently over all releases MOD15 C4↗MOD15 C5↗MCD15 C5. RMSE decreased by ~0.1 for each new release. MODIS C5 retrieved with the main algorithm (QC<64) and CYCLOPES showed similar range of uncertainties (1.0~1.2). Uncertainties for the best MODIS C5 (QC=0) and CYCLOPES (SM=0) were around 0.9-1.0. The overall mean differences between the best MODIS C5 and CYCLOPES were within 0.10. The uncertainties of current LAI products (within 1.0) are still unable to meet the accuracy requirement by GCOS (0.5). Conclusions

  23. Future work • Broadleaf crops, broadleaf trees • Complex background/understory • Low LAI (<1.0) regions: arid and semi-arid, tundra, permafrost • Beginning and ending periods of growing season • Points other than NA and Europe Seeking collaboration

  24. Thank you! Questions, comments? Hongliang Fang (方红亮） Institute of Geographical Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS) Email: fanghl@lreis.ac.cn

  25. MODIS LAI • 3D Radiative Transfer model (Myneni 1997) • Parameterization for each of 6 biomes • 25 modeled soil reflectances (Jacquemoud 1992) • Retrieval with Look-up Table method • From MODIS B1 (Red) and B2 (NIR) • Backup algorithm based on empirical NDVI-LAI relationship for each biome • True LAI http://wist.echo.nasa.gov

  26. CYCLOPES • SPOT/VGT bi-directional normalization • SAIL + PROSPECT + empirical soil description • Neural network • Nadir normalized reflectance B2 (Red), B3 (NIR), MIR • SZA 10:00 local time • Daily fAPAR • Effective LAI http://postel.mediasfrance.org

  27. GLOBCARBON • SPOT/VGT, ERS/ATSR, (ENDVISAT/MERIS) • 2 algorithms to retrieve LAIe: • LAIe = f(RSR, fBRDF) for forest classes • LAIe = f(SR, fBRDF) for other vegetation • fBRDF from modified Roujean model • Empirical clumping index • True LAI http://geofront.vgt.vito.be/geosuccess/relay.do?dispatch=LAI_info

  28. Clumping • MODIS: canopy clumping parameters for each biome • CYCLOPES: account for clumping at the landscape scale, each pixel was supposed to be made of a fraction vCover of pure vegetation and (1-vCover) of pure bare soil. (SAIL does not describe clumping at canopy level) • ECOCLIMAP LAI: (not obvious)

  29. Rice paddy with water http://spl.bnu.edu.cn

  30. Mature crop with yellow leaves Big reflectance changes but small LAI variation; photosynthesis? http://spl.bnu.edu.cn

  31. Gray/dead leaves http://spl.bnu.edu.cn

  32. Snow background

  33. Comparison of MODIS (QC<64) and CYCLOPES LAI with common field observations for 6 biome types

  34. BELMANIP • Global Partnership and a benchmark for indirect validation (Baret et al., 2006) • Use of additional networks • FLUXNET, AERONET • Eliminating replicates and sites with water >25% @ 88 km² • Adding sites to improve representativeness • Surface types • Latitudinal distribution • Longitudinal distribution http://lpvs.gsfc.nasa.gov/lai_intercomp.php

  35. BELMANIP 100 DIRECT + 218 FLUXNET + 58 AERONET + 78 COMPLET= 377 BELMANIP Baret et al., EGU, 2005

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