Development of an inversion algorithm for dry snow density estimation and its application with ENVISAT-ASAR dual co-polarization data

Abstract

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of space-borne sensor dual-polarization C-band data of environmental satellite-(ENVISAT-) advanced synthetic aperture radar (ASAR) can enhance the accuracy in measurement of snow physical parameters as compared with single polarization data measurement. This study shows the capability of Cband synthetic aperture radar (SAR) data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both horizontal-horizontal (hh) and vertical-vertical (vv) polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's semi-empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR hh and vv polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 0.024 g/cm 3 .

Snow Height Determination by Polarimetric Phase Differences in X-Band SAR Data

“…The effect that backscattering from a smooth wet surface is much lower than from rough ground was used to detect wet snow and to predict melt water runoff [23]- [26]. Algorithms based on the polarimetric backscatter signal were developed and used for snow wetness and snow density determination [27], [28].…”

Snow density retrieval using SAR data: algorithm validation and applications in part of North Western Himalaya

“…Radar back scattering from a snow surface depends upon the dielectric constant of the 20 surface (Tiuri et al, 1984;Ulaby and Stiles, 1980;Rott, 1984), its roughness properties, and the geometry of the scattering (Evans, 1963;Ambach, 1980;Nyfors, 1982;Hallikainen et al, 1986;Matzler, 1987). Many mathematical models of surface scattering have been developed, some based on physical laws, some on empirical data fitting, and some on a combination of the two (Rees, 2006;Strozzi, 1996;Strozzi and 25 Matzler, 1998).…”

“…Furthermore, this study showed the possibility of inferring snow depths from combined parameters estimated with an L-band SAR image. Snehmani et al (2010) used C-band SAR data of ASAR-APS dual polarisation for snow density 15 estimation. In this approach for developing an algorithm for snow density estimation, the volume scattering model and the small perturbation model have been used with an exponential correlation (Ulaby et al, 1986) function for the surface backscattering contribution from the snow-ground interface.…”

“…Niang et al (2007) used new inversion method for snow 20 density and snow liquid water content retrieval using C-band data from ENVISAT/ASAR alternating polarization in alpine environment. In context of Indian Western Himalayans, small research has been done using polarimetric SAR data to infer snow density, except for the recent work reported using dual polarization ENVISAT-ASAR and Advanced Land Observing Satellite (ALOS)-Phased 25 Array type L-band Synthetic Aperture Radar (PALSAR) data (Singh and Venkataraman, 2007, 2009Snehmani et al, 2010;Thakur et al, 2008Thakur et al, , 2012. None of these studies had used fully polarimetric data for inferring the snow parameters; therefore, 1930…”

“…About 10 % of total area of the Himalaya is covered with glaciers and additional area, nearly 30 % support the snow cover (Singh and Singh, 2001;Singh et al, 2011). According to estimation, there are about 9575 glaciers, cov- 20 ering an area of about 38 000 km 2 in the Indian part of the Himalaya (Raina, 2009). The snow physical parameters, such as snow wetness, which shows the degree of liquid water content in snow pack, along with snow density and Snow Water Equivalent (SWE) are most important parameters for many water resources related studies such as snowmelt runoff and snow avalanche modeling (Rees, 2006).…”