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MODIS Land Collection 5 Changes


All MODLAND products are stored using HDF internal compression to reduce disk space usage. Specific changes to individual products are listed in the following sections.

1. Land Surface Reflectance (PGEs 11 and 21)

  • Reduce striping in the SWIR bands using Liam Gumley's algorithm to reduce noise in the retrieved aerosol optical thickness and subsequently in the surface reflectance.
  • Refine the internal masks (cloud and cloud shadow masks) and expand the mask to include flagging of pixels adjacent to clouds where aerosol optical thickness retrieval is disabled.
  • Use the new version of 6S with improved polarization handling to create atmospheric correction Look up tables (LUT).
  • Modify LUT format to improve the accuracy atmospheric parameters interpolation.
  • Use dynamic aerosol models and ocean bands to improve aerosol retrieval and correction over land.
  • Refine atmospheric correction - aerosol retrieval and correction in costal area.
  • Improve consistency between 250m and 500m composited products.

2. L2G Surface reflectance (PGEs 12, 13, 14, 15)

  • Reduce number and size of surface reflectance products to make them more useable
    • size reduced by eliminating most pixels in scan overlap region and by simplifying method of moving between resolutions
    • number of files reduced from 7 to 2 per tile
    • old products kept as internal products to minimize downstream changes
    • archived size reduced from 250 to 120 GB/day (w/o internal compression)
  • Create an interim L2G brightness temperature product for use in downstream products such as VCC and Burned Area.

3. MODAGG (PGE22)

  • Improve observation filtering and screening.

4. BRDF/Albedo (PGE23)

  • Produce BRDF/Albedo at 500m
    • will continue to produce 1km version
    • may only produce parameters (not albedo and NBAR) at 500m to save space
    • will use new L2G surface reflectance format
  • Produce only combined product
    • will contain only Terra data before Aqua launch
  • Format changes for usability (QA bits; 4D to 3D arrays)
Click here for details

5. VIs (PGEs 25,26,35)

  • EVI improvements to address bright targets and dynamic range
  • New filtering to deal with previous collection's spatial discontinuities
  • Address "green" water bodies issue
  • Produce 16-day products with Terra and Aqua staggered (out of phase) by 8-days
  • Structural changes:
    • rework QA to make information more useful
    • (possibly) eliminate 500m product
  • Issues/concerns:
    • should incorporate feedback from DAAC regarding "day of year"
    • details of staggered product(s) need to be worked out
Click here for details

6. LAI/FPAR (PGEs 33 and 34)

  • Algorithm refinements were targeted to improve quality of LAI/FPAR retrievals and consistency with field measurements over all biomes but with major focus on woody vegetation.
  • Old (C3) 6 biome LAI/FPAR biome map was replaced with new (C4) 8 biome map. Broadleaf and needle leaf forests classes were spitted into deciduous and evergreen subclasses.
  • Refinement of the LUTs of the LAI/FPAR algorithm for all 8 biomes:
    • new stochastic RT model was utilized, which allows a better representation of canopy structure and spatial heterogeneity intrinsic to woody biomes
    • the parameters of the new LUTs were selected to ensure consistency between simulated and measured MODIS surface reflectances and to minimize anomalies in LAI retrievals (LAI overestimation and algorithm failure over medium/dense vegetation) and inconsistency between LAI and FPAR retrievals (correct LAI with FPAR being overestimated over sparse vegetation) noted in a former version of the product.
    • biome-dependent uncertainties, that is, threshold on allowable discrepancies between simulated and MODIS surface reflectances, were introduced: 20% for Red and 5% for NIR for herbaceous vegetation, and 30% for Red and 15% for NIR for woody vegetation.
    • Analysis of the prototype of Collection 5 product showed a much higher retrieval rate from the algorithm and improved consistency with field data over savannas (Fig. 1), broadleaf (Fig. 2) and needle leaf forests (Fig. 3).
  • Development of two new Terra-Aqua combined products (8- and 4- days, Fig. 4).
Click here for details

7. GPP/NPP (PGEs 36, 37 and 38)

  • Modification of parameters in Biome Property Look-Up Table (BPLUT) to agree with GPP derived from measurements at eddy flux towers and synthesized NPP.
  • Alteration of growth respiration module in the collection 004 to make it is 25% of annual NPP, rather than being dependent on the annual maximum LAI.
  • Replacement of the constant Q10 quotient of 2.0 for vegetation maintenance respiration calculation with a variable Q10, which changes with air temperature.
  • Spatially non-linear interpolation of coarse resolution meteorological data into 1-km MODIS pixel level, instead of nearest neighbor sampling, to increase the accuracy of meteorological data input at pixel level.
  • Limitations of the collection 005 comprise two aspects. First, the BPLUT may be subject to change if collection 005 MODIS FPAR/LAI or new version of meteorological data have large changes. Second, 8-day data and annual NPP are subject to incorrect estimates caused by contaminated FPAR/LAI. The in-house reprocessing is required to clean these contaminated inputs, and users are encouraged to obtain the improved annual MODIS NPP from website of the principal investigator or Dr. Running directly.
Click here for details

8. Land surface temp./emissivity (PGEs 16, 31)

  • Change the grid size in the coarse resolution product (MOD11B1) where the day/night algorithm is used to 6km x 6km.
  • Increase the number of sub-ranges of view zenith angle to 2x8 for the whole swath.
  • Use DEM slope.
  • Option for use of combined Terra and Aqua data in day/night algorithm.
  • Remove cloud-contaminated LSTs using constraints from last 32 days of data.
  • Implement an empirical optical leak correction for band 32.
Click here for details

9. Snow cover (PGEs 07, 43 and 45)

  • Add fractional snow algorithm for Terra.
  • Introduce Limits snow detection based on surface temperature to reduce false detection.
  • Add shadowed land screening under low illumination conditions to avoid false detections.
  • Replace current QA bits with QA index
  • New monthly CMG
Click here for details

10. Sea ice (PGEs 08 and 44)

  • Eliminate "Sea Ice by IST" and "Combined Sea Ice" fields (SDSs)
  • Produce monthly sea ice CMG
Click here for details

11. Land cover (and water mask) and phenology (PGEs 40 and 41)

  • Produce 500m LCOV products (from 500m NBARs)
    • will require large revision and update of training site database
  • Revise classifications
    • will add new layers - heritage layers will be retained
    • depends on progress on a number of thematic issues - will probably happen in C6 timeframe
  • Expect a 6 month production schedule for LCOV (based on 12 months of data)
  • Expect a 3 month production schedulde for phenology
  • Issues/concerns:
  • Land/water mask integration

12. Fire, Thermal Anomalies and Burn Scar (PGEs 29, 30, 80 and 86)

  • Fire and Thermal Anomalies
    • Changed calculation of fire radiative power (FRP) to produce more useful output; users no longer need to multiply FRP by pixel area.
    • Changed long name of SDS "FP_power".
    • Changed calculation of detection confidence to more accurately identify questionable fire pixels.
    • Added two new SDSs to daily fire product ("MaxFRP" and "sample") to Level 3 product.
    • Changed SDS name from the unwieldy "most confident detected fire" to "FireMask" in Level 3.
  • Burn area
    • Intermediate product: replace snow with fill value
Click here for details

13. Vegetation cover change and continuous fields (PGEs 66 and 72)

  • Adjusted the compositing algorithm to utilize updates to upstream data quality flags.
  • Changed compositing algorithm to allow the brightness temperature inputs to be used instead of Land Surface Temperature.
  • Eliminated cirrus tests in the compositing algorithm.
  • Updated the water identification algorithm.
  • Expanded VCC production to regions beyond +/- 30 for deforestation.
  • VCF product (produced at SCF)
    • production for all data years
    • product resolution becomes 250m (500m product discontinued)
    • tree, herbaceous and bare cover continued
    • leaf type and longevity added for all data years
    • additional continuous fields added for water and crops
Click here for details and examples


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Curator: Demi Feng
NASA Official: Edward Masuoka
Last Updated: Apr. 10, 2014

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