The spaceborne ESA-mission CHRIS/PROBA (Compact High Resolution Imaging Spectrometer-Project for On-board Autonomy) provides hyperspectral and multidirectional data of selected targets spread over the world. While the spectral information content of CHRIS/PROBA data is able to assess the biochemistry of a vegetation canopy, the directional information can describe the structure of an observed canopy.
However, a thematic analysis of the hyperspectraldirectional
data requires dedicated geometric and radiometric pre-processing of the CHRIS/PROBA acquisitions. Only careful pre-processing will provide a spatially, spectrally, directionally and temporally consistent data set – a prerequisite for subsequent quantitative and qualitative retrieval of biochemical and –physical vegetation parameters. In this study we propose and validate such a comprehensive preprocessing on a data set over rugged, mountainous terrain in the Swiss Alps.
The proposed geometric correction relies on a parametric approach taking into account the viewing geometry and geometric distortion due to the sensor, platform and topography. Potentially, this method provides high accuracy, robustness and consistent results over the full image. The performance of the geometric correction is validated relative to geolocated digital map and vector data available for parts of the CHRIS scenes.
Atmospheric correction of the hyperspectral-directional data involves the physically based radiative transfer model ATCOR. The ATCOR model corrects for the effects of the atmosphere as well as for illumination effects caused by rugged terrain influencing the satellite image. Parallel to the spaceborne data also spectrodirectional ground data have been acquired with the FIGOS Goniometer over an alpine meadow. Spectral measurements of various field targets complement the data set. The field data allow for a validation of the obtained top-of-canopy Hemispherical-Directional-Reflectance-Factor (HDRF) in its full directional resolution.