Land Validation Campaigns
The goal of the EOS Validation Program is the comprehensive assessment of all EOS science data products. ORNL DAAC supports these assessments by compiling data such as leaf area index (LAI), and net primary productivity (NPP) for global test sites to compare with satellite-derived products. These data are unique in having ground-based observations coincident with satellite data. In addition, this information is useful to develop, calibrate, and validate ecosystem models.
The ORNL DAAC compiles, archives, and distributes more than 20 land validation data products from the following NASA-funded research projects:
and through the following resources:
Accelerated Canopy Chemistry Program (ACCP)
The Accelerated Canopy Chemistry Program (ACCP) was an investigation to determine the theoretical and empirical basis for remote sensing of nitrogen and lignin concentrations in vegetation canopies of different ecosystems conducted at various U.S.A. sites between 1991 and 1993. Available data include: Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images, laboratory chemical analysis of field samples, laboratory spectra and chemical analysis from several mini-canopy experiments, and canopy modeling data.
The goal of the BigFoot Project was to support the validation of land products from the Moderate Resolution Imaging Spectrometer (MODIS) onboard NASA's Earth Observing System (EOS) Terra satellite. Reflectance data from MODIS were used to produce several science products including land cover, leaf area index (LAI), and net primary production (NPP). The BigFoot project began in 1999, concluded in 2003, and was funded by NASA's Terrestrial Ecology Program.
EOS Land Validation
The objective of the EOS Land Validation Project is to achieve consistency, completeness, and a timely flow of data in support of the validation of EOS Land Products, especially MODIS, ASTER, MISR, and LANDSAT 7. The 31 EOS Land Validation Core Sites span a range of biome types and represent consensus amongst the instrument teams and validation investigators. Available data include site leaf are index (lai). EOS Land Validation is an active project.
FLUXNET, a network of regional networks, coordinates regional and global analysis of observations from micrometeorological tower sites that use eddy covariance methods to measure exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere. FLUXNET is comprised of more than 500 tower sites from about 30 regional networks across five continents. FLUXNET is an active project.
Prototype Validation Exercise (PROVE)
The Prototype Validation Exercise (PROVE) was a mini field campaign conducted in May 1997 near Las Cruces, New Mexico, U.S.A, at the Jornada Experimental Range. Prove sought to gain experience in the collection and use of field data for EOS product validation, to develop protocols for coordination, measurement, and data- archival, and to compile a synoptic land and atmosphere data set for testing algorithms.
MODIS Land Product Subsets
The goal of the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Product Subsets project is to provide summaries of selected MODIS Land products for community use in the validation of models and remote-sensing products and in the characterization of field sites. Output files contain pixel values in text and GeoTIFF format, with time series plots and grids for composite periods. The MODIS Land Product Subsets Project is an active project.
Multi Sensor Subsets
To understand the ecological and biogeochemical characteristics of a region, several data sets have to be assembled for analysis. In general creation of these data bundles is manual and time consuming. In 2011, ORNL DAAC UWG members who represent ORNL DAAC's diverse user community recommended that the ORNL DAAC explore the creation of data bundles/services. The data bundles are pre-created data sets organized by science themes such as land cover, carbon, and phenology. To facilitate the data bundle needs of the user community, the ORNL DAAC has created a pilot project for multi-sensor subsets.