Snow wetness inversion algorithm using polarimetric scattering power decomposition model
A new snow wetness estimation methodology is proposed for full-polarimetric Synthetic Aperture Radar (SAR) data. Surface and volume are the dominant scattering components in wet-snow conditions.
The generalized four component polarimetric decomposition with unitary transformation (G4U) based generalized surface and volume parameters are utilized to invert snow surface and volume dielectric constants using the Bragg coefficients and Fresnel transmission coefficients respectively.
M. Surendar, A. Bhattacharya, G. Singh, Y. Yamaguchi, and G. Venkataraman. “Development of a snow wetness inversion algorithm using polarimetric scattering power decomposition model.”International Journal of Applied Earth Observation and Geoinformation 42 (2015): 65-75.
The snow surface and volume wetness are then estimated using an empirical relationship.
The effective snow wetness is derived from the weighted averaged surface and volume snow wetness.
The weights are derived from the normalized surface and volume scattering powers obtained from the generalized full-polarimetric SAR decomposition method.
Snow Surface Dielectric Constant using polarimetric SAR data
A novel methodology is proposed for the estimation of snow surface dielectric constant from polarimetric SAR (PolSAR) data.
The dominant scattering type magnitude proposed in Touzi et. al., is used to characterize the scattering mechanism over the snowpack.
Whereas two methods have been used to obtain the optimized degree polarization of a partially polarized wave:
The Touzi optimum degree of polarization given in Touzi et. al., 1992. The maximum (Pmax) and the minimum (Pmin) degree of polarizations are obtained along with the optimum transmitted polarizations (χtopt, χropt).
The Adaptive Generalized Unitary (AGU) transformation based optimum degree of polarization (mEopt) proposed in Bhattacharya et. al., 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 criteria to select the maximum number of pixels with surface dominant scattering. These pixels were then used to invert the snow surface dielectric constant.
S. Manickam, A. Bhattacharya, G. Singh, and Y. Yamaguchi. “Estimation of Snow Surface Dielectric Constant From Polarimetric SAR Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 1 (2017): 211-218.