MODIS (the Moderate Resolution Imaging Spectrometer) is the principal high temporal frequency mapping sensor on-board NASA’s Terra satellite. The MODIS instrument acquires data in 36 spectral bands at spatial resolutions from 250 m to 1 km over the entire globe every two days. A series of land products will be produced from these data by the MODIS Land Discipline Group (MODLand). These products will include surface reflectance, spectral vegetation indices, land cover, the absorbed fraction of photosynthetically active radiation (FPAR), leaf area index (LAI), net primary productivity (NPP), and land surface temperature. These, and other MODIS products, will play an important role in measuring and monitoring surface variables. Validation of these global data products is crucial for establishing the accuracy of the data products for the scientific user community, and to provide feedback for improving the data processing algorithms.
The BigFoot field sites have active science programs concentrating on CO2, water vapor, and energy exchange using flux tower measurements. The "footprint" over which gas flux data are collected varies, but is roughly 1 km or less. For the BigFoot analysis, this footprint will be extended to 25 km2 to include multiple 1 km MODIS cells, hence the project name. BigFoot investigators will focus on validation of the MODLand land cover, LAI, FPAR, and NPP products. We will develop fine grain (25 m resolution) surfaces of land cover, LAI, FPAR, and NPP, aggregate these to 1 km resolution, then assess the similarities and differences between these surfaces and the MODLand products.
Project History
The BigFoot project grew from an LTER-sponsored workshop held in 1996.
At the most recent LTER All Scientists Meeting (Estes Park, CO 1993) one of the workshops focused on NASA/LTER interactions, which were nascent at the time. Participants in the workshop, led by John Vande Castle and Steve Running, explored validation protocols and scaling issues that lead to an improved understanding of several MODLand products. The workshop ultimately produced a proposal from 14 LTER sites and the Network Office to Diane Wickland, Manager of the Terrestrial Ecology Program at NASA.
The focus of the proposal was to conduct preliminary studies that would lead to eventual "validation" of global ecological data layers developed by the MODIS Land Discipline Group within the context of NASA’s Earth Observation System. Two proposals (including a subsequent, more refined proposal), have been funded, and we are now completing the first year of the subsequent proposal.
Our goals, in addition to providing MODLand product validation, are
- To explore the errors and information losses that accrue when extrapolating field data to coarse grain (1 km) surfaces
- Determine if there is a fundamental grain size at each site, above which error rates accelerate when modeling land cover, LAI, FPAR, and NPP
- Develop a better understanding of the climatic and ecological controls on net primary production and carbon allocation within and among biomes
- To learn how flux tower measurements of net ecosystem exchange (NEE) and field measurements of NPP co-vary, and how to translate between them using ecological models
We will be working at four field sites: a boreal forest, a tallgrass prairie, a mixed deciduous-conifer forest, and a mixed corn and soybean agricultural system.
The core BigFoot products will be: field-collected data sets of land cover, LAI, FPAR, NPP, and related variables; and 25 km2 surfaces at 25 m spatial resolution of land cover, LAI, FPAR, and NPP for each site. These surfaces will be developed from field data, Landsat imagery, image classification, geostatistical analysis, and ecosystem process models. Errors in each data layer will be characterized using independent field data. Explicit examination of scaling from field measurements, to fine grain (25 m) surfaces, to coarse grain (1 km) MODLand grids is a central theme of BigFoot. The fine grain surfaces will be aggregated to several resolutions, up to 1 km, to determine if there is a grain size above which information loss rapidly increases. Through these analyses, we hope to characterize error due to scaling differences versus error due to algorithm definitions. It is theoretically possible for any single MODIS grid cell to not accurately estimate land cover, LAI, FPAR, or NPP, but for multiple cell estimates within and across sites to be accurate. A cross-site comparison of MODLand and BigFoot surfaces will permit us to assess MODLand data product accuracy and gain an understanding of the source of errors at both the site and cross-site level.
Learn more about the: BigFoot Project