Inferring snow wetness using C-band data from SIR-C's polarimetric synthetic aperture radar

Abstract
In hydrological investigations, modeling and forecasting of snow melt runoff require timely information about spatial variability of snow properties, among them the liquid water content-snow wetness-in the top layer of a snow pack. The authors' polarimetric model shows that scattering mechanisms control the relationship between snow wetness and the copolarization signals in data from a multi-parameter synthetic aperture radar. Along with snow wetness, the surface roughness and local incidence angle also affect the copolarization signals, making them either larger or smaller depending on the snow parameters, surface roughness, and incidence angle. The authors base their algorithm for retrieving snow wetness from SIR-C/X-SAR on a first-order scattering model that includes both surface and volume scattering. It is applicable for incidence angles from 25/spl deg/-70/spl deg/ and for surface roughness with rms height /spl les/7 mm and correlation length /spl les/25 cm. Comparison with ground measurements showed that the absolute error in snow wetness inferred from the imagery was within 2.5% at 95% confidence interval. Typically the free liquid water content of snow ranges from 0% to 15% by volume. The authors conclude that a C-band polarimetric SAR can provide useful estimates of the wetness of the top layers of seasonal snow packs.<>

This publication has 19 references indexed in Scilit: