Terrestrial Ecology and Land Cover/Change Science Review: Basis of Successful Land Product Validation Jeffrey L. Privette NASA’s 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

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

What We Knew Going In… Excellent knowledge of point issues Limited knowledge of site (25 km 2 ) issues spatial and temporal sampling scaling of point measurements error budgeting Poor knowledge of global sampling issues except inherently cost-prohibitive without strategic planning

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

Strategic Plan 1: Focus 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

EOS Land Validation Core Sites

Solutions Data “stockpiles” for EOS Core Sitesstockpiles 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 ORNLWWW metadata catalog Field investigator upkeep and control of native data Accommodates 6-month public release policy for EOS Validation Investigators Strategic Plan 2: Simple, Fast Data Provision

/1999/2000/2001 EOS Land Product Validation FTP Server /GLCTS/Landsat7 /”site name” /Geocover/MODIS/ASTER /”site name” Level-0R Level-1G MOD09A1 MOD13A2 MOD43B3 AST_L1B Accessible from Land Validation Web Site or ORNL DAAC Mercury

or enter specific coordinates 2. Spatial Search (use cursor to define area of interest) Mercury Searching 3. Temporal Search 1. Fielded Search (using picklists for Project, Site PI, Parameter, etc.)

Strategic Plan 3: International Buy-in Committee on Earth Observing Satellites (CEOS) Composed of the world’s 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

Strategic Plan 3: International Buy-in Committee on Earth Observing Satellites (CEOS) Composed of the world’s 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

WGCV Subgroup on Land 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)

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

ASTER Spectral Calibration Accuracy with Time

Case Study II: BigFoot/FLUXNET

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

Example: Comparing Landsat and MODIS LAI Products BigFootMODLAND

Generalizing BigFoot’s Approach Over FLUXNET Data from Flux Towers Micro-meteorological data (hourly/daily) Incident PAR Air Temperatures Vapor Pressure Rainfall Wind Land surface properties (seasonal/annual) Land cover Leaf area index Soil carbon Soil nitrogen Biomass (stem) Fluxes : CO2, water and heat MODIS PRODUCTS MOD-17 8-d GPP MOD-17 ANPP Ecosystem models are used to relate flux measurements to MODIS derived products VALIDATION Based on Nemani/2/28/00

FLUXNET Validation Web Site: FLUXNET Validation Web Site: Online Access to FLUXNET Data

Case Study III: LAI Network/Myneni Joint activity of CEOS LPV & MODLAND MODIS LAI and FPAR Open-invitation workshops from to 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 needsnetworks

LAI Product Evaluation month pathfinder Leverage existing field campaigns field campaigns Workshop: Rome, June 2001 MODLAND coordinating Landsat ETM+ acquisitions and MODLAND LAI subsets

LAI Product Evaluation: Participating Sites

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

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

“Under-represented” Climate Combinations for North America and Asia

Concerns and Recommendation EOS underscored two relevant lessons: Sensor characteristics change throughout their lifetimes, including prelaunch Algorithms change 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

Status of ASTER Data Products