The project is a collaboration with the U.S. Geological Survey’s Center (USGS) for Earth Resources Observations and Science (EROS). The USGS Center is the repository for satellite images taken from the Landsat series of satellites which since 1972 has been providing the longest dedicated land remote sensing data record of the Earth. Dr. Tom Loveland, who is the USGS Landsat Project Scientist, said, “This project will provide the USGS with a state-of-the-art strategy for creating the land monitoring data sets needed by the nation’s resource managers and an exciting opportunity for evaluating the next generation of Landsat processing and delivery systems. The project is timely because as of this year, Landsat images became available free of charge.”
The grant supports Roy’s innovative approach for filling the information gaps that commonly exist in Landsat data. These “gaps” result from both cloud-cover—which on global average obscures 35% of Landsat data— and the triangular data voids created by the May 2003 failure of the Landsat-7’s Scan Line Corrector. Roy’s approach uses 500-meter data from the MODIS sensor (specifically, the 16-day MODIS BRDF/Albedo product) and existing gap-free Landsat data to predict Landsat reflectance for a given date. This method works, on a per-pixel basis, by modulating the Landsat reflectance of the known date data by a value that represents the relationship between MODIS reflectance on the prediction and known dates—while taking into consideration the Landsat sensor-sun view angle geometry.
Scientists will spend the first half of the five-year period developing a prototype at South Dakota State University. Then they’ll move the system to the USGS and put it into operation.
Dr. Tom Loveland and Dr. Matthew Hansen are co-investigators on the project. Dr. Hansen said, “This project has the potential to fundamentally change the way satellite data are accessed and used. Our experience in processing and characterizing Landsat imagery will enhance the data’s utility for a whole range of users. This is a very exciting project.”
Roy, D.P., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., Lindquist, E., 2008, Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data, Remote Sensing of Environment, 112:3112-3130. (PDF file 6MB)
Ju, J. and Roy, D.P., 2008, The Availability of Cloud-free Landsat ETM+ data over the Conterminous United States and Globally, Remote Sensing of Environment, 112:1196-1211. (PDF file, 2.1MB)
Hansen, M.C., Roy, D.P., Lindquist, E., Adusei , B., Justice, C.O., Altstaat, A., 2008, A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change and preliminary results for Central Africa, Remote Sensing of Environment, 112:2495-2513. (PDF file, 4.6MB)
Woodcock, C.E., Allen, A.A., Anderson, M., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Goward, S.N., Helder, D., Helmer, E., Nemani, R., Oreapoulos, L., Schott, J., Thenkabail, P.S., Vermote, E.F., Vogelmann, J., Wulder, M.A., Wynne, R. , 2008, Free access to Landsat imagery, Science, 320:1011.
Satellites offer a wealth of information pertinent for water and food security. Landsat has long been a foundational piece of the “Space for Ag” initiative.