Analysis of the Performance of the MODIS LAI and FPAR Algorithm

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Analysis of the Performance of the MODIS LAI and
FPAR Algorithm
N. Shabanov, W. Yang, B. Tan, H. Dong, R. B.
Myneni, Y. Knyazikhin /Boston University S. W.
Running, J.Glassy, P. Votava, R. Nemani
/University of Montana, NASA Ames Research Center
MODIS Science Team Meeting, BWI Airport
Marriott, Baltimore, MD, July 15-16, 2003
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Roadmap of the Presentation

• Status of the MODIS TERRA and AQUA LAI/FPAR
• Analysis of the MODIS Terra LAI/FPAR Collection 3
  Data Time Series from November 2000 to December
  2002
• Assessment of the Performance of the MODIS LAI
  Algorithm as a Function of the Input
  Uncertainties Case Study with Grasses
• Analysis of the Performance of the MODIS LAI/FPAR
  Algorithm over Broadleaf Forests

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1. Status of the MODIS Terra and Aqua LAI/FPAR
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Status

• TERRA LAI/FPAR (MOD15A2)
• Collection 3-- Generation completed. Coverage
  November 2000 December 2002, Validation status
  Validated Stage 1, QA status Inferred
  Passed
• Collection 4-- Generation In progress. Released
  to public as of March 7, 2003. Coverage March
  2000 December 2001 and January 2003 present.
  Year 2002 will be reprocessed before end of this
  year. Validation status Provisional, QA
  status Inferred Passed
• AQUA LAI/FPAR (MYD15A2)
• Product generated since October 24, 2002. Global
  coverage available since January 2003. Data are
  available for internal evaluation only
• Currently new AQUA LUTs are under testing by
  LDOPE QA team. After end of testing AQUA LAI/FPAR
  product is planned for public release
• Documentation
• PI website ( http//cybele.bu.edu ), FLUXNET site
  with ASCII subsets of LAI, EDC DAAC site were
  updated with material for collection 4 (including
  user guide)

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Validation
Biome validated by July, 2002 Transect validated by July, 2004
Grasses/ Cereal Crops Konza, USA SAFARI 2000 wet season Gourma, Mali
Shrubs Puechabon, France
Broadleaf Crops Bondville, USA
Savannas SAFARI 2000 wet season Australia (planned)
Broadleaf Forests Harvard Forest, USA Jaervselja, Estonia Siberia, Russia
Needle Forests Ruokolahti, Finland Flakaliden, Sweeden Siberia, Russia

• All 6 biomes have been sampled at field
• Collaborators from Europe (VALERI), Jeff Privette
  team, BigFoot team
• Continue to analyze field data and compare with
  collection 3 and 4

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TERRA MODIS LAI/FPAR Collection 4 Improvements
LUT Tuning for the Main and Back-up Algorithms

• Non-physical peaks at high LAI values for
  herbaceous vegetation (biome 1 - 4) were removed
• Validation feedback (BigFoot) improved agreement
  with field measurements (KONZA, grasses, ARGO,
  crops, etc.)
• Retrievals with main algorithm increased by 20
  compared to collection 3 data
• Collection 3 green
• Collection 4 red

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TERRA MODIS LAI/FPAR Collection 4 Improvements
(Cont.)

• Improvements to QA Scheme
• Reduced redundancy between MODLAND and SCF_QC
  quality flags
• SCF_QC is more clearly structured Main (M),
  Saturation (S), and Back-up (B)
• Update for Input Land Cover
• At-launch AVHRR based IGBP land
• cover was replaced with 6-bime land
• cover generated from one year of
• MODIS data
• Cross-walking from IGBP to 6-biome
• was eliminated
• New LC has less uncertainties
• New 8-day compositing scheme
• Compositing over best quality retrievals, instead
  of all retrievals

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MOD15A2, Collection 4 Data, July 20-27, 2001
TERRA MODIS LAI/FPAR Collection 4 Improvements
(Cont.)

• Achievements
• Spatial coverage of main algorithm increased by
  20 due to LUTs tuning and new compositing
  scheme
• M72, S5, B23 (collection 4)
• MS60, B40 (collection 3)
• Improved consistency with field observations over
  herbaceous vegetation
• Future Improvements (collection 5)
• Decrease dominance of back-up algorithm
  retrievals over woody vegetation (broadleaf and
  needle leaf forests)
• Further improve agreement with with field data
• Research on retrievals under snow condition
  (resolve needle leaf forests seasonality)

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2. Analysis of the MODIS Terra LAI/FPAR
Collection 3 Data Time Series from November 2000
to December 2002
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Statement of the Problem

• Objective
• Collection 3 MODIS TERRA LAI/FPAR product
  provides about two years of data time series,
  valuable of the assessment of the product
  quality. We performed analysis of the product
  spatial coverage, seasonality of LAI and FPAR for
  different vegetation types. Special attention was
  given to retrievals under snow and cloudy
  conditions.
• Data Used
• MOD15A2, 8-day LAI composite, collection 3,
  November 2000- December 2002

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Retrieval Index Seasonality
RI by biomes
RI by latitudinal band

• Retrieval Index, RI Pixels (Main algorithm) /
  Pixels (Main Back-up algorithm)
• Main algorithm fails significantly less on
  herbaceous vegetation (grasses cereal, shrubs,
  broadleaf crops and savannas), compared to woody
  vegetation (broadleaf and needle leaf forests)
• Strong seasonality in retrievals for latitudes gt
  50 degrees North is due to snow and other factors

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LAI Seasonality
LAI by biomes
LAI by latitudinal band

• The LAI/FPAR profiles for each biome type and
  latitude band have the expected shape.
• Needle leaf forests show high seasonality, which
  is also pronounced for the highest latitudinal
  band.

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LAI Retrievals Under Snow Condition

• About 50-60 of vegetated pixels north of 40
  degrees North are identified as having snow
  during peak winter period.
• The majority of snow pixels are retrieved by
  backup algorithm. Main algorithm recognize non
  vegetation signal in data.
• Cumulative LAI retrieved under snow condition is
  100 times smaller than one for snow free
  condition

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LAI Retrievals Under Cloudy Condition

• About 50 to 60 of the vegetated pixels are
  identified as cloud free, 15 as partially cloudy
  and 25-35 are cloud covered
• LAI/FPAR algorithm performs retrievals regardless
  of cloud conditions
• LAI values retrieved under cloudy condition are
  spurious. The difference between LAI retrieved
  under cloudy and cloud free conditions depends on
  biome type.

Needle Leaf Forests
Crops
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3. Assessment of the Performance of the MODIS LAI
Algorithm as a Function of Input Uncertainties
Case Study with Grasses
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Statement of the Problem

• The Problem
• As reported by BigFoot team, MOD15A2 product,
  collection 3 substantially overestimate field
  measured LAI at Konza site (grasses, 5x5 km
  area)
• a) Field measurements LAI 3
• b) MODIS product LAI 5.7 /- 0.7
• Solution Approach
• Analysis of uncertainties in LAI, collection 3,
  was performed as function of input uncertainties
  land cover misclassification and uncertainties in
  input surface reflectances
• Data Used
• MOD15A2, 8-day LAI composite, collection 3, tile
  h10v05, composite July 04-11, 2001
• MODAGAGG, daily surface reflectance, collection
  3, tile h10v05, days July 04-11, 2001
• MOD12Q1, 6-biome classification map, at launch
  and new version, tile h10v05

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Impact of Biome Misclassification on LAI
Retrievals
LC for collection 3 LAI
LC for collection 4 LAI

• MODIS LAI/FPAR algorithm references LC to select
  vegetation parameters from LUTs.
  Misclassification leads to errors in LAI
  estimation
• Collection 3 LAI used MODIS at-launch IGBP LC
  (AVHRR-based), cross-walked to 6 biome LC
• Collection 4 LAI referencing MODIS 6-biome LC
  product (based on one year MODIS data)
• Significant misclassification occur at local
  scale (5x5 km) for at-launch LC this map
  predicts 24 of the pixels are grasses, while
  field measurements indicates that 64 of the
  pixels are grasses.

1200x1200 km
1200x1200 km
20x20 km
20x20 km
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Impact of Uncertainties in Surface Reflectances
on LAI Retrievals

• Definition Good data are surface reflectance
  data with MODAGAGG QA Product produced at
  ideal quality or Product produces, less than
  ideal quality
• Good quality data have lower uncertainty than
  poor quality data
• Uncertainties in LAI retrievals are proportional
  to uncertainties in surface reflectance
  variations

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How to Reduce Impact of Input Uncertainties on
LAI Retrievals?

• However averages over sufficiently large regions
  (in this case study- over the tile) can smooth
  uncertainties in retrieved LAI Due to
  uncertainties in input land cover / surface
  reflectances at the scale of few MODIS pixels
  errors in LAI are possible. Selecting larger
  spatial patches generally helps accumulate
  sufficient amount of correctly classified pixels
  and reduces errors in LAI
• Additionally, collection 4 LAI product has more
  accurate input data, and improvements to the
  algorithm were made. We got good agreement with
  field data MODIS LAI collection 4 over 5x5 km
  subset during July 04-11, 2001 is 2.97/-1.5
  (field data LAI 3).

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4. Analysis of the Performance of the MODIS Terra
LAI/FPAR Algorithm over Broadleaf Forests
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Statement of the Problem

• The Problem
• Dominance of back-up retrievals for broadleaf
  forests during summer time
• Solution Approach
• Investigate the properties of surface
  reflecatnces
• Data Used
• MOD15A2, 8-day LAI composite, collection 4, tile
  h12v04, all the data during year 2001
• MODAGAGG, daily surface reflectance, collection
  4, tile h12v04, May 01-08, 2001 and July 12-19,
  2001
• MOD12Q1, 6-biome classification map, collection
  3, tile h12v04

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Broadleaf Forests of New England
LAI July 12-19, 2001
LC
LAI May 01-08, 2001
QC May 01-08, 2001
QC July 12-19, 2001
Problem Dominance of back-up retrievals for
broadleaf forests during summer time (MODIS tile
h12v04 is shown).
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Broadleaf Forests of New England

• Collection 4 data for broadleaf forest, tile
  h12v04 are shown
• LAI/FPAR algorithm correctly captures seasonality
  in LAI
• However during summer retrievlas are performrmed
  mostly with back-up algorithm (main fails,
  picture on the bottom)
• What changes in surface reflectances are
  responsible for decrease in Main algorithm
  retrievals during transition from early spring to
  summer when LAI reaches its maximum?

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Broadleaf Forests of New England
July 12 - 19, 2001
May 01- 08, 2001
Variable May 01-08 July 12-19 Change,
Red 0.047 0.034 -27.5
NIR 0.226 0.387 71.2
NDVI 0.646 0.835 29.1
LAI 2.97 5.59 88.2
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Broadleaf Forests of New England

• Analysis of surface reflectances indicates that
  predominant location of MODIS observations (in
  Red/NIR spectral space) during summer time
  mismatch the model predictions as stored in LUTs
  of the algorithm.
• LUTs will be updated to be in agreement with
  observed values of surface reflectances. The
  proposed changes will be implemented in
  collection 5