Accurate Geometric Correction for Normalisation of PALSAR Radiometry
David Small(1), Michael Jehle(1), Adrian Schubert(1) and Erich Meier(1)
(1) University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
In contrast to earlier satellites with SAR instruments, the ENVISAT and
ALOS platforms provide state vectors with sufficient accuracy to enable
the ASAR and PALSAR sensors to support accurate tiepoint-free
geolocation of their imagery. This enables not only map overlays and
data fusion with other sources, but also normalisation for the
systematic influence of terrain variations on individual image
radiometry. Such normalisation is necessary to move away from
dependency on single-track repeat passes for change-detection and
We briefly describe our verifications of the geometric behaviour of
PALSAR L1.1 products using available products with surveyed corner
reflector targets present in reference images.
We model and evaluate the path delays induced by the troposphere and
ionosphere on reference imagery, and compare Faraday rotation estimates
produced using fully polarimetric PLR imagery with values derived from
GNSS-network measurements. In the latter estimate, the total electron
content (TEC) of the ionosphere at the time of the PALSAR acquisition is
combined with a model of the Earth's magnetic field to estimate the
Faraday rotation induced by the ionosphere along the line of sight from
the satellite to each point on the ground.
Given accurate knowledge of the acquisition geometry of a SAR image from
one of the above sensors together with a digital elevation model (DEM)
of the area imaged, radiometric image simulation is applied to estimate
the local illuminated area for each point in the image. Rather than a
typical ellipsoid-based approximation that ignores topographic
variation, terrain-based radiometric image simulation is used as the
basis for converting from beta0 to sigma0 or gamma0 backscatter
The interpretability of PALSAR imagery with and without ellipsoid- vs.
terrain-based normalisations is compared and evaluated.