Estimation of Snow Surface Dielectric Constant From Polarimetric SAR Data

A novel methodology is proposed in this paper for the estimation of snow surface dielectric constant from polarimetric SAR (PolSAR) data. The dominant scattering-type magnitude proposed by Touzi et al. is used to characterize scattering mechanism over the snowpack. Two methods have been used to obtain the optimized degree polarization of a partially polarized wave: 1) the Touzi optimum degree of polarization given by Touzi et al. in 1992. The maximum (
_pmax
) and the minimum (p
_min
) degree of polarizations are obtained along with the optimum transmitted polarizations (χ
_t
^opt
, ψ
_t
^opt
). 2) The adaptive generalized unitary transformation-based optimum degree of polarization m
_E
^opt
proposed by Bhattacharya et al. in 2015. This optimum degree of polarization is obtained either by a real or a complex unitary transformation of the 3 × 3 coherency matrix. These two degrees of polarizations are used and compared in this study as a criterion to select the maximum number of pixels with surface dominant scattering. These pixels were then used to invert the snow surface dielectric constant. It has been observed that the m
_E
^opt
have increased the number of pixels for inversion by ≈9-10% compared to the original data. On the other hand, it was observed that the Touzi maximum degree of polarization p
_max
has increased the number of pixels for inversion by ≈2% compared to that of m
_E
^opt
. The proposed methodology is applied to Radarsat-2 PolSAR C-band datasets over the Indian Himalayan region. It is observed that the correlation coefficient between the measured and the estimated snow surface dielectric constant is 0.95 at 95% confidence interval with a root mean square error of 0.20.