Terrestrial Ecology and Land Cover/Change Science Review: Basis of Successful Land Product Validation

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Terrestrial Ecology and Land Cover/Change Science
ReviewBasis of Successful Land Product
Validation

• Jeffrey L. Privette
• NASAs Goddard Space Flight Center
• Warren Cohen, John Dwyer, S. Tom Gower, Simon
  Hook, Chris Justice, Alfredo Huete, Jeff
  Morisette, Ranga Myneni, Dick Olson, Steve
  Running, David Starr

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Challenge

• One of the five primary objectives of the Earth
  Science Enterprise
• Identify and measure the primary causes of
  change
• Impossible to accomplish without knowledge of
  measurement uncertainties
• No precedent for global validation of land
  products

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What We Knew Going In

• Excellent knowledge of point issues
• Limited knowledge of site (25 km2) issues
• spatial and temporal sampling
• scaling of point measurements
• error budgeting
• Poor knowledge of global sampling issues
• except inherently cost-prohibitive without
  strategic planning

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EOS Response

• EOS Validation Program Office/D. Starr
• Guidance from Instrument Teams
• Priorities (e.g., low level products first)
• Approaches
• Community resources
• 1997 NRA for EOS Validation Investigators
• Shared responsibility with product developers

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Strategic Plan 1Focus Sites

• EOS Land Validation Core Sites
• Facilitate resource sharing and synergies in
  validating multiple sensors and products
• Built on existing stations, resources, experts
• FLUXNET, AERONET, LTER, etc.
• Long-term duration
• Stratified by 6 biomes of Myneni/Running
• Jointly nominated by Instrument and (newly
  selected) Validation PIs

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EOS Land Validation Core Sites
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Strategic Plan 2Simple, Fast Data Provision

• Solutions
• Data stockpiles for EOS Core Sites
• Unique online ftp directories at EROS Data Center
• 200 km x 200 km MODLAND product subsets
• Landsat 7 ETM scenes (coordinated by MODLAND)
• SeaWiFS, ASTER, UMD/CRESS, others joined
• Centralized field data registry
• WWW metadata catalog and data linking at ORNL
• Field investigator upkeep and control of native
  data
• Accommodates 6-month public release policy
  forEOS Validation Investigators

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EOS Land Product Validation FTP Server

• Accessible from Land
• Validation Web Site
• or ORNL DAAC Mercury

/1999
/2000
/2001
/site name
/site name
/site name
http//modis-land.gsfc.nasa.gov/val/ http//mercur
y.ornl.gov/
/GLCTS
/Landsat7
/Geocover
/MODIS
/ASTER
AST_L1B
Level-0R Level-1G
MOD09A1 MOD13A2 MOD43B3
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Mercury Searching
1. Fielded Search (using picklists for Project,
Site PI, Parameter, etc.)
or enter specific coordinates
2. Spatial Search (use cursor to define area of
interest)
3. Temporal Search
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Strategic Plan 3International Buy-in

• Committee on Earth Observing Satellites (CEOS)
• Composed of the worlds space agencies
• Coordinates civilian observations from space
• Working Group for Calibration and Validation
  (WGCV)
• Facilitates and encourages standards,
  efficiencies, best practices
• Four Subgroups
• Synthetic Aperture Radar
• Microwave Sensors
• Infrared and Visible Optical Sensors (IVOS)
• Terrain Mapping
• No mechanism for coordinating or advancing
  validation of higher level land products

Calibration emphasis
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Strategic Plan 3International Buy-in

• Committee on Earth Observing Satellites (CEOS)
• Composed of the worlds space agencies
• Coordinates civilian observations from space
• Working Group for Calibration and Validation
  (WGCV)
• Facilitates and encourages standards,
  efficiencies, best practices
• Five Subgroups
• Synthetic Aperture Radar
• Microwave Sensors
• Infrared and Visible Optical Sensors (IVOS)
• Terrain Mapping
• Land Product Validation

Calibration emphasis
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WGCV Subgroup onLand Product Validation

• Chartered in 2000 with strong NASA advocacy
• Founding focus Support of Global Observations
  of Forest Cover (GOFC/GOLD) needs (fire, land
  cover and biophysical products)
• Multinational endorsement and participation
• particularly from Europe and Japan (ENVISAT,
  ADEOS2/GLI/ POLDER2)

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Case Study 1 ASTER/Hook

• MODIS and ASTER thermal infrared systems and
  products
• Approach
• Verification of sensor calibration at simple
  sites
• Determination of lower- to higher level product
  uncertainties at realistic sites
• Data from ground, aircraft, other sensor and
  lunar looks
• Sites 6 in U.S., 3 in Australia

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ASTER Spectral Calibration Accuracy with Time
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Case Study IIBigFoot/FLUXNET
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BigFoot Site-based Sampling and Modeling
Methodology
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BigFoot Site-based Sampling and Modeling
Methodology

• Parameters v. Products
• Landcover 1,2
• LAI 1,2
• FPAR 1,2
• NPP 1,2
• GPP 1,3,4
• NEP 1,3,4
• 1 MODIS
• 2 field measure
• 3 modeled
• 4 tower/eddy flux

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Example Comparing Landsat and MODIS LAI Products
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Generalizing BigFoots Approach Over FLUXNET
MODIS PRODUCTS MOD-17 8-d GPP MOD-17 ANPP
VALIDATION
Based on Nemani/2/28/00
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Online Access to FLUXNET Data
FLUXNET Validation Web Site http//daac.ornl.gov/
FLUXNET/data.html
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Case Study IIILAI Network/Myneni

• Joint activity of CEOS LPV MODLAND
• MODIS LAI and FPAR
• Open-invitation workshops from 1998- to 2000.
  Findings
• Several foreign groups sampling LAI, some
  addressing scaling
• Strong interest in cross-comparisons, technical
  exchange and trouble-shooting
• Regional networks could be developed for MODLAND
  LAI validation needs

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LAI Product Evaluation

• 12-18 month pathfinder
• Leverage existing 2000-2001 field campaigns
• Workshop Rome, June 2001
• MODLAND coordinating Landsat ETM acquisitions
  and MODLAND LAI subsets

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LAI Product Evaluation Participating Sites
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Validation What Works?

• Dedicated science team liaisons for validation
• Simplified data access (dedicated archives)
• Strong communication between Product PIs and
  Validation PIs
• Algorithm or product familiarity
• Strong attention to error budgeting
• Satellite constellations
• International partnerships and coordination
• Networks, networks, networks
• Duration (mission succession), consistency

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Validation To Be Resolved

• Global Representivity
• How many points in time, space and biome?
• Scaling
• Infancy for many products
• Communication of Results
• Formal venues lacking
• User Input
• Better knowledge greater cost
• How much is enough?
• Uncertainty Embedding
• Product data layer with pixel-by-pixel
  uncertainties

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Under-represented Climate Combinations for
North America and Asia
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Concerns and Recommendation

• EOS underscored two relevant lessons
• Sensor characteristics change throughout their
  lifetimes, including prelaunch
• Algorithms change throughout their lifetimes
• Reprocessing happens.
• Throughout NPP/NPOESS era (present to 2020),
  NASA is purchasing products from industry
• Is NASA ready to conduct performance
  verification?
• Could NASA credibly challenge a vendor on an
  underperforming high level product?
• Permanent NASA Office on Calibration and
  Validation

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Status of ASTER Data Products