ESTIMATION OF SURFACE SNOW PROPERTIES USING COMBINED MILLIMETER-WAVE BACKSCATTER AND NEAR-INFRARED REFLECTANCE MEASUREMENTS

Knowledge of surficial snow properties such as grain size, surface rou ghness, and free-water content provides clues to the metamorphic state of snow on the ground, which in turn yields information on weathering processes and climatic activity. Remote sensing techniques using comb ined concurrent measurements of near-infrared passive reflectance and millimeter-wave radar backscatter show promise in estimating the above snow parameters. Near-infrared reflectance is strongly dependent on s now grain size and free-water content, while millimeter-wave backscatt er is primarily dependent on free-water content and, to some extent, o n the surface roughness. A neural-network based inversion algorithm ha s been developed that optimally combines near-infrared and millimeter- wave measurements for accurate estimation of the relevant snow propert ies. The algorithm uses reflectances at wavelengths of 1160 nm, 1260 n m and 1360 nm, as well as co-polarized and cross-polarized backscatter at a frequency of 95 GHz. The inversion algorithm has been tested usi ng simulated data, and is seen to perform well under noise-free condit ions. Under noise-added conditions, a signal-to-noise ratio of 32 dB o r greater ensures acceptable errors in snow parameter estimation.