Fang2011_LAI_IGARSS.ppt

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  • Hard to estimate LAI for vegetation with snow over the canopy. How when the snow is under the canopy?
  • Fang2011_LAI_IGARSS.ppt

    1. 1. Uncertainties of global moderate resolution Leaf Area Index (LAI) products derived from satellite data Hongliang Fang a , Shanshan Wei a,b , Shunlin Liang c a LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. b Department of Geography, School of Urban and Environmental Sciences, Northeast Normal University, Changchun, Jilin Province, 130024, China. c Department of Geography, University of Maryland, College Park, Maryland, 20742, USA. IGARSS’01, Vancouver, Canada, Jul 24-27, 2011
    2. 2. Outline <ul><li>Introduction </li></ul><ul><li>Validation method </li></ul><ul><li>Results </li></ul><ul><li>Conclusions </li></ul>
    3. 3. Background <ul><li>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) </li></ul><ul><li>An Essential Climate Variable (ECV) necessary fo many process models </li></ul><ul><li>Observational requirement by the Global Climate Observation System (GCOS): ±0.5 (GCOS, 2006) </li></ul>
    4. 4. Lacaze, 2005. User Manual Masson, 2003. JOC Deng, 2006. TGRS Baret, 2007. RSE Knyazikhin, 1998. JGR Ref. NN (MCRM: PROSPECT+SAIL+PRICE/WALTHALL) Linear regression of literature LAI and NDVI SR method for forest and non-forest based on model NN (PROSPECT+SAIL+5 TYPICAL SOIL) LUT (3D model)+VI Alg. 11 directional reflectance in G, R, NIR bands and angular config. AVHRR NDVI VGT, ATSR, (MERIS) ref. SZA, TOC ref in RED, NIR, & SWIR RED, NIR Input 1996.11.5-1997.6.25; 2003.4.5-2003.10.25 1998-2007 [Sep 2006] 1999-2007 1999-present Time 10-day (L3) and monthly (HDF) Monthly Monthly 10-day 8-day Temporal 1/9°(monthly) 30” to 1D 1km, 10km, 0.25D,0.5D 1/112D 1km, 4km, 0.25° Spatial POLDER ECOCLIMAP GLOBCARBON CYCLOPES EOS Project
    5. 5. Four stages of validation defined by the Committee on Earth Observation Satellites (CEOS) Adapted From LPV/WGCV/CEOS (http://lpvs.gsfc.nasa.gov/) Systematic and global Validation results systematically updated with new releases and new data. 4 Weiss et al. (2007) Garrigues et al. (2008) Aim of our study Global Product accuracy assessed systematically and globally. Product uncertainties well established. 3 Verger et al. (2011; 2009), Luo et al. (2004) Regional-continental Validation over a widely distributed set of locations, and validation efforts; 2 Fang and Liang (2005), Cohen et al. (2006), Hill et al. (2006), Pisek and Chen (2007), Sprintsin et al. (2009) Local-regional Validation in a small number of selected locations, time periods and validation efforts 1 Support studies Scale Explanation Stage
    6. 6. Objectives <ul><li>To extend the Stage 3 validation for both MODIS and CYCLOPES LAI products with a global field measurement database. </li></ul><ul><li>To investigate whether the current global LAI products could meet the observational requirements proposed by GCOS. </li></ul><ul><ul><li>MODIS suite: Terra C4 (MOD15 C4), Terra C5 (MOD15 C5) and Terra+Aqua C5 (MCD15 C5) </li></ul></ul><ul><ul><li>SPOT/VEGETATION CYCLOPES V3.1 </li></ul></ul><ul><ul><li>Consideration of the MODIS quality control (QC) layer and the CYCLOPES status mask (SM) </li></ul></ul>
    7. 7. Outline <ul><li>Introduction </li></ul><ul><li>Validation method </li></ul><ul><li>Results </li></ul><ul><li>Conclusions </li></ul>
    8. 8. Validation schemes <ul><li>Direct comparison with in situ data collected over validation sites ( this study ); </li></ul><ul><li>Bridging method : comparison with products derived from high resolution airborne or spaceborne sensors (e.g., Landsat TM/ETM+); </li></ul><ul><li>Cross-validation with other independently obtained products; </li></ul><ul><li>Intercomparison and analysis with process model simulations. </li></ul>( http:// lpvs.gsfc.nasa.gov /; Morisette et al, 2006; Justice et al., 2002)
    9. 9. Direct field measurement <ul><li>Destructive sampling or collection of total leaf litterfall. </li></ul><ul><li>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. </li></ul><ul><li>Allometric method, based on the relationship between leaf area and the diameter at breast height (DBH). </li></ul>
    10. 10. Indirect field measurement <ul><li>Indirect contact methods, e.g, the point quadrats method. </li></ul><ul><li>LAI 2000 and hemispherical photography with no clumping correction (Effective LAI). </li></ul><ul><li>LAI 2000, TRAC and hemispherical photography with clumping correction (True LAI). </li></ul>
    11. 11. Direct validation campaigns <ul><li>BigFoot (Cohen & Justice, 1999) </li></ul><ul><li>CCRS (Fernandes et al., 2003) </li></ul><ul><li>MODLAND (Morisette et al., 2002) </li></ul><ul><li>VALERI (Baret et al., 2006) </li></ul><ul><li>CEOS LPV (Morisette et al., 2006) </li></ul><ul><li>Share ground LAI data and maps among the entire community </li></ul>
    12. 12. Global field LAI measurement sites from campaigns and literature 219 observations over 129 sites Fang et al., to be submitted.
    13. 13. MODIS and CYCLOPES quality indicators B3 (NIR) saturation: ok 0 / no 1 Bit 9 B2 (red) saturation: ok 0 / no 1 Bit 8 B0 (blue) saturation: ok 0 / no 1 Bit 7 Parameter validity: ok 0 / no 1 Bit 6 Aerosol source: MODIS 0 / climatology 1 Bit 5 Aerosol status: pure 0 / mixed 1 Bit 4 Cloud/shadow: no 0 / suspected 1 Bit 3 Snow status: no 0 / snow 1 Bit 2 Land 0 / sea 1 Bit 1 SM<16 CYCLOPES SM Couldn't retrieve pixel 100=4 128  QC<255 Empirical method used (Main method failed due to problems other than geometry) 011=3 96  QC<128 Empirical method used (Main method failed due to geometry problems) 010=2 64  QC<96 Main (RT) method with saturation 001=1 32  QC<64 Main (RT) method with the best possible results 000=0 QC<32 MODIS QC (DN range) and SCF_QC (binary, decimal values) Quality description Binary, DN range
    14. 14. Outline <ul><li>Introduction </li></ul><ul><li>Validation method </li></ul><ul><li>Results </li></ul><ul><li>Conclusions </li></ul>
    15. 15. Statistics of field measured LAI Fang et al., to be submitted. 1.98 (1.61) 217 2.30 (1.57) 77 1.81 (1.61) 140 Overall 2.19 (1.43) 56 1.87 (1.10) 46 3.65 (1.88) 10 6. Needleleaf forest 3.44 (1.65) 51 3.64 (1.74) 20 3.31 (1.61) 31 5. Broadleaf forest 0.99 (1.14) 42 3.08 (2.53) 3 0.83 (0.84) 39 4. Savanna 2.36 (1.11) 4 2.36 (1.11) 4 3. Broadleaf crops 0.68 (0.70) 25 1.08 (0.79) 8 0.50 (0.58) 17 2. Shrubs 1.63 (1.09) 39 1.63 (1.09) 39 1. Grasses and cereal crops Mean (SD) n Mean (SD) n Mean (SD) n Overall Effective True Biome type
    16. 16. MODIS/Terra C4 (QC<128) Main: 85.8% R 2 =0.435 RMSE=1.42 MODIS/Terra C5 (QC<128) Main: 92.5% R 2 =0.307 RMSE=1.53 MODIS/Terra+Aqua C5 (QC<128) Main: 97.6% R 2 =0.526 RMSE=1.09 VGT/CYCLYPES V3.1 (LAI<6.0) R 2 =0.557 RMSE=0.97 Field true LAI
    17. 17. MODIS/Terra C4 (QC<128) R 2 =0.234 RMSE=2.08 MODIS/Terra C5 (QC<128) R 2 =0.290 RMSE=1.74 MODIS/Terra+Aqua C5 (QC<128) R 2 =0.186 RMSE=1.63 VGT/CYCLYPES V3.1 (LAI<6.0) R 2 =0.399 RMSE=1.34 Field effective LAI
    18. 18. Comparison of MODIS and CYCLOPES LAI with field LAI 1.34 0.399 63 1.39 0.348 56 0.82 0.005 7 CYCLOPES (effective) 0.97 0.557 111 1.05 0.629 58 0.87 0.449 53 CYCLOPES (true) 1.09 0.528 81 (97.6%) 1.05 0.599 57 (96.6%) 1.16 0.042 24 QC<64 1.09 0.526 83 1.06 0.593 59 1.16 0.042 24 MCD15 C5 1.17 0.465 98 (92.5%) 1.27 0.382 46 (88.5%) 1.09 0.221 52 (96.3%) QC<64 1.53 0.307 106 1.82 0.140 52 1.18 0.171 54 MOD15 C5 1.19 0.559 115 (85.8%) 1.13 0.718 63 (79.7%) 1.25 0.061 52 (94.5%) QC<64 1.42 0.436 134 1.50 0.481 79 1.29 0.137 55 MOD15 C4 RMSE R 2 n RMSE R 2 n RMSE R 2 n All biomes Woody Herbaceous
    19. 19. Comparison of best MODIS (QC=0) and CYCLOPES (SM=0) with field LAI 1.74 0.043 20 1.774 0.004 19 1.00 — 1 CYCLOPES ( Effective ) 0.99 0.557 76 1.12 0.655 37 0.84 0.508 39 CYCLOPES ( True) 0.898 0.542 33 0.797 0.674 21 1.053 0.083 12 MCD15 C5 1.001 0.478 78 1.012 0.509 32 0.994 0.270 46 MOD15 C5 1.101 0.534 48 1.249 0.513 25 0.914 0.087 23 MOD15 C4 RMSE R 2 n RMSE R 2 n RMSE R 2 n All biomes Woody Herbaceous
    20. 20. MOD15 C5 (QC<64) MCD15 C5 (QC<64) SPOT/VGT CYCLOPES 2000.1-2005.12
    21. 21. Global Monthly Average MODIS, CYCLOPES and GLOBCARBON LAI
    22. 22. Outline <ul><li>Introduction </li></ul><ul><li>Validation method </li></ul><ul><li>Results </li></ul><ul><li>Conclusions </li></ul>
    23. 23. Conclusions <ul><li>MODIS LAI has improved consistently over all releases MOD15 C4 ↗ MOD15 C5 ↗ MCD15 C5. RMSE decreased by ~0.1 for each new release. </li></ul><ul><li>MODIS C5 retrieved with the main algorithm (QC<64) and CYCLOPES showed similar range of uncertainties ( 1.0~1.2 ). </li></ul><ul><li>Uncertainties for the best MODIS C5 (QC=0) and CYCLOPES (SM=0) were around 0.9-1.0 . </li></ul><ul><li>The overall mean differences between the best MODIS C5 and CYCLOPES were within 0.10 . </li></ul><ul><li>The uncertainties of current LAI products (within  1.0) are still unable to meet the accuracy requirement by GCOS (  0.5). </li></ul>
    24. 24. Future work <ul><li>Broadleaf crops, broadleaf trees </li></ul><ul><li>Complex background/understory </li></ul><ul><li>Low LAI (<1.0) regions: arid and semi-arid, tundra, permafrost </li></ul><ul><li>Beginning and ending periods of growing season </li></ul><ul><li>Points other than NA and Europe </li></ul>Seeking collaboration
    25. 25. 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
    26. 26. MODIS LAI <ul><li>3D Radiative Transfer model (Myneni 1997) </li></ul><ul><ul><li>Parameterization for each of 6 biomes </li></ul></ul><ul><ul><li>25 modeled soil reflectances (Jacquemoud 1992) </li></ul></ul><ul><li>Retrieval with Look-up Table method </li></ul><ul><ul><li>From MODIS B1 (Red) and B2 (NIR) </li></ul></ul><ul><li>Backup algorithm based on empirical NDVI-LAI relationship for each biome </li></ul><ul><li>True LAI </li></ul>http://wist.echo.nasa.gov
    27. 27. CYCLOPES <ul><li>SPOT/VGT bi-directional normalization </li></ul><ul><li>SAIL + PROSPECT + empirical soil description </li></ul><ul><li>Neural network </li></ul><ul><ul><li>Nadir normalized reflectance B2 (Red), B3 (NIR), MIR </li></ul></ul><ul><ul><li>SZA 10:00 local time </li></ul></ul><ul><ul><li>Daily fAPAR </li></ul></ul><ul><li>Effective LAI </li></ul>http://postel.mediasfrance.org
    28. 28. GLOBCARBON <ul><li>SPOT/VGT, ERS/ATSR, (ENDVISAT/MERIS) </li></ul><ul><li>2 algorithms to retrieve LAIe: </li></ul><ul><ul><li>LAIe = f(RSR, fBRDF) for forest classes </li></ul></ul><ul><ul><li>LAIe = f(SR, fBRDF) for other vegetation </li></ul></ul><ul><li>fBRDF from modified Roujean model </li></ul><ul><li>Empirical clumping index </li></ul><ul><li>True LAI </li></ul>http://geofront.vgt.vito.be/geosuccess/relay.do?dispatch=LAI_info
    29. 29. Clumping <ul><li>MODIS: canopy clumping parameters for each biome </li></ul><ul><li>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) </li></ul><ul><li>ECOCLIMAP LAI: (not obvious) </li></ul>
    30. 30. Rice paddy with water http://spl.bnu.edu.cn
    31. 31. Mature crop with yellow leaves Big reflectance changes but small LAI variation; photosynthesis? http://spl.bnu.edu.cn
    32. 32. Gray/dead leaves http://spl.bnu.edu.cn
    33. 33. Snow background
    34. 34. Comparison of MODIS (QC<64) and CYCLOPES LAI with common field observations for 6 biome types
    35. 35. BELMANIP <ul><li>Global Partnership and a benchmark for indirect validation (Baret et al., 2006) </li></ul><ul><li>Use of additional networks </li></ul><ul><ul><li>FLUXNET, AERONET </li></ul></ul><ul><li>Eliminating replicates and sites with water >25% @ 8  8 km² </li></ul><ul><li>Adding sites to improve representativeness </li></ul><ul><ul><li>Surface types </li></ul></ul><ul><ul><li>Latitudinal distribution </li></ul></ul><ul><ul><li>Longitudinal distribution </li></ul></ul>http://lpvs.gsfc.nasa.gov/lai_intercomp.php
    36. 36. BELMANIP 100 DIRECT + 218 FLUXNET + 58 AERONET + 78 COMPLET= 377 BELMANIP Baret et al., EGU, 2005

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