Model Based Forest Parameter Estimation from Single Baseline Pol-in-SAR Data: the Fichtelgebirge Test Case
DLR German Aerospace Center,
PO Box 1116,
Polarimetric Synthetic Aperture Radar (SAR) interferometry is a recently developed radar remote sensing technique, based on the coherent combination of radar interferometry and polarimetry. On the one hand side, SAR interferometry is a well-established SAR technique to estimate the height location of the effective scattering center in each resolution cell through the phase difference in images acquired from spatially separated antennas. The sensitivity of the interferometric phase and coherence to spatial variability of height and density of vegetation make the estimation of structural vegetation parameters from interferometric measurements a challenge. On the other hand, scattering polarimetry is sensitive to the shape, orientation and dielectric properties of scatterers. This allows the identification and separation of different scattering mechanisms occurring inside the resolution cell of natural media by employing differences in the polarization signature. In polarimetric interferometry both techniques are coherently combined to provide sensitivity to the vertical distribution of different scattering processes and make the investigation of volume scatterers a challenge. Indeed, in the last years, quantitative model based estimation of forest parameters - based on a single frequency, fully polarimetric, single baseline configuration - has been demonstrated using space- and airborne repeat pass fully polarimetric interferometry at C-, L-, and P-band. These experiments demonstrated the potential of this new technology to estimate with high accuracy key forest parameters like tree height, stand and/or canopy density, and forest ground topography. In this paper we present work performed in the frame of the ESA project on Polarimetric SAR Applications. We review the actual status of Polarimetric SAR Interferometry, and point out potential and limitations of this technique with respect to quantitative model based forest parameter inversion using several experimental Air- and Space-borne data sets.