HR: 0800h
AN: IN31A-1142    [Abstracts]
TI: Synergy Between Ground Measurements and High Spatial Resolution Imagery to Validate Medium Spatial Resolution Land Surface Product
AU: * Garrigues, S
EM: garrigue@pop600.gsfc.nasa.gov
AF: University of Maryland, NASA GSFC Mail Code 614.4, Greenbelt, MD 20771 United States
AU: Morisette, J
EM: jeff.morisette@nasa.gov
AF: NASA GSFC, Mail Code 614.5, Greenbelt, MD 20771 United States
AU: Baret, F
EM: baret@avignon.inra.fr
AF: INRA CSE, Agroparc-Domaine St Paul, Avignon, 84000 France, Metropolitan
AU: Privette, J
EM: jeff.privette@nasa.gov
AF: NASA GSFC, Mail Code 614.4, Greenbelt, MD 20771 United States
AB: Currently, several biophysical variables such as the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and the surface albedo are derived from remote sensing observations acquired with medium spatial resolution sensors (250m to 7km). Because of their high temporal frequency and their important spatial coverage, these products are very useful to describe the mass and energy fluxes between the earth surface and the atmosphere. Since these variables are required for an important range of investigations and applications, it is important to assess their accuracy. Validation activity consists in evaluating by independent means the quality of the land surface products estimated from coarse resolution sensors. Validation methods consist in generating a ground truth map of these products at high spatial resolution. These maps are produced by using ground measurements of the biophysical variable and radiometric data from a high spatial resolution sensor (10m-30m). The relationship between a biophysical variable and radiometric imagery, called the transfer function, allows extending the local ground measurements to the entire high spatial resolution image. The resulting biophysical variable map is aggregated to be compared with the medium spatial resolution satellite biophysical products. Several geometrical issues influence the validation results: - the registration accuracy of the local ground measurements relative to the high spatial resolution image - the difference of spatial support between the ground measurement support and the high spatial resolution pixel - the registration accuracy between the high and medium resolution images - the point spread function (PSF) associated to the medium spatial resolution image This work proposes a methodology to account for these sources of uncertainties within the validation process. First, these problems are investigated at the field measurement scale. The registration accuracy between ground measurement located by GPS and high spatial resolution pixel is modeled by a Gaussian random variable. For each possible relative position, the ground measurement is related to the radiometric data for the surrounding area weighted by the ground measurement spatial support area. A Monte Carlo simulation scheme accounting for the positional accuracy provides the probability distribution function of the parameters defining the transfer function. Second, registration error is investigated when aggregated high spatial resolution image is compared with coarse resolution image. The registration error is minimized by getting the best geometrical match between the two images using correlation techniques. Finally, the importance of the PSF associated to the medium spatial resolution biophysical products is evaluated using data from MODIS and VEGETATION sensors. This investigation is applied to several validation sites expressing a range of spatial heterogeneity.
DE: 3200 MATHEMATICAL GEOPHYSICS (0500, 4400, 7833)
DE: 3252 Spatial analysis (0500)
DE: 3265 Stochastic processes (3235, 4468, 4475, 7857)
DE: 3275 Uncertainty quantification (1873)
DE: 3294 Instruments and techniques
SC: Earth and Space Science Informatics [IN]
MN: Fall Meeting 2005