Investigating snow wetness using dual polarization advanced synthetic aperture radar imagery

Abstract

The main objective of the study is to estimate snow wetness using ENVISAT ASAR data. Snow surface backscattering can be expressed as a function of permittivity of snow. Coding has been done for backscattering coefficient image generation using ENVISAT- Advanced Synthetic Aperture Radar (ASAR), single look complex (SLC) data with dual (HH and VV) polarization as well as single (HH) polarization data. Incidence angle images were extracted from the ASAR header data using interpolation method. These mages were multi-looked 5 times in azimuth and 1 time in range direction. ASAR backscattering coefficient images have been calibrated and processed into terrain corrected images in Universal Transverse Mercator (UTM), zone 43 north and WGS-84 datum map projection using ERDAS Imagine software. Corrected baekscattering images are despeckled using Frost filter technique. For this study Integral equation method (IEM) for first order surface scattering based inversion model has been used. A Software has been developed using integral equation method (IEM) based inversion model to estimate snow permittivity, which can be further related to 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 observed to be 0.94 at 95% confidence interval and standard error is observed as 1.28% by volume at 95% confidence interval. The comparison of ASAR derived snow wetness with ground measurements shows the average absolute error at 95% confidence interval as 2.8%. The snow wetness range varies from 0-15% by volume.