This paper discusses the methodology of Synthetic Aperture Radar (SAR) data analysis for studying various aspects of
snow characteristics viz snow dielectric constant, snow wetness and snow density. ENVISAT- Advanced Synthetic
Aperture Radar (ASAR), single look complex (SLC) data have been processed for backscattering coefficient image
generation. ASAR Backscattering coefficient images have been calibrated and processed into terrain corrected images.
Corrected backscattering images are despeckled using Frost filter technique. The estimation of snow pack characteristics
is optimal at different incidence angles. The relation between snow characteristics like wetness, and snow density and
radar backscatter has been studied and the importance of radar backscatter to infer various snow characteristics has been
emphasized. This investigation shows the backscattering coefficient is inversely correlated to snow wetness and density.
The correlation between the backscattering coefficients and snow wetness and snow density were observed as 0.8 and
0.92 respectively. 14.74 % and 13.31% part of the study area was found affected by layover and low or grazing local
incidence respectively in ENVISAT-ASAR IS6 image. In this study, the wetness range was found to vary from
0.05% to 10.28% by volume and mean absolute error was found to be 0.64% by volume and snow density range varies
from < 0.1 to 0.48 gm/cc and mean absolute error for density was found 0.032 gm/cc. At higher elevation to moderate
elevation estimated snow wetness was observed to be 0.05 - 4% by volume, increasing to 4-10.28 % by volume at
moderate to lower elevation.
This paper discusses the methodology of Synthetic Aperture Radar (SAR) data analysis for studying snow porosity and its effect on electric property of snow. ENVISAT- Advanced Synthetic Aperture Radar (ASAR) single polarized, single look complex (SLC) data have been processed for backscattering coefficient image generation. Incidence angle image has been extracted from the ASAR header data using interpolation method. These images were multi-looked 5 times in azimuth and 1 time in range direction. ASAR Backscattering coefficient images have been calibrated. The estimation of snow porosity is optimal at different incidence angles. The effect of snow porosity on snow wetness, snow density and backscattering coefficient is studied in detail. The correlation coefficient between estimated and measured porosity is observed to be 0.88 and absolute error was 0.045.
The snow wetness in the Himalayan snow covered region is an important parameter, for the snow melt runoff modeling
and forecasting. The main objective of the study is to estimate snow wetness in parts of Himalayan snow covered
regions. Snow surface backscattering can expressed as function of permittivity of snow. Reflectivity at the air snow
interface increases greatly with wetness and volume scattering decreases abruptly. ENIVISAT-ASAR dual polarization
(HH and VV) data have been used to investigate permittivity and snow wetness in sub Himalayan region. Raw data have
been processed for backscattering coefficient (BSC) image generation for HH and VV polarization. BSC image is georeferenced
and topographically corrected using high precision digital elevation model (DEM). The BSC images are
despeckled using adaptive filter technique. For this study Physical optics Model (POM) for surface scattering based
inversion model has been used. Physical Optics Model based inversion model gives the permittivity which can be further
related for estimating snow wetness. A comparison was done between inversion model estimated snow wetness and field
values of snow wetness in the study region. Comparison with field measurement showed that the correlation coefficient
for snow wetness estimated from ASAR data was 0.8 at 95% confidence interval. The snow wetness ranges from
0-15% by volume.