In Search of the Statistical Properties of High-Resolution Polarimetric SAR Data for the Measurements of Forest Biomass Beyond the RCS Saturation Limits

The purpose of this letter is to present the results on the study of searching effective parameters that describe the relation between high-resolution synthetic aperture radar (SAR) images and forest parameters. The study is based on the non-Gaussian texture analysis of the polarimetric airborne Pi-SAR data over coniferous forests in Hokkaido, Japan. The radar cross section (RCS) in terms of a forest biomass is first analyzed. It is found that the
$hboxL$
-band RCS increases steadily with the biomass and saturates at approximately 40 tons/ha. These results are similar to the previous studies. The probability density function of the image amplitude is then investigated, and among Rayleigh, log-normal, Weibull, and
$K$
-distributions, the
$K$
-distribution is found to fit best to the
$hboxL$
-band data of all polarizations, although the Weibull distribution fits equally well. Further, the correlation between the tree biomass and the order parameter of the
$K$
-distribution in the cross-polarization images is found to be very high, and the order parameter increases consistently with the biomass to approximately 100 tons/ha, which is well beyond the saturation limit of the
$hboxL$
-band RCS. Thus, the order parameter of the
$K$
-distribution can be a promising new parameter to estimate the forest biomass from high-resolution polarimetric SAR data in a much wider range than the conventional RCS method.