Knowledge of surficial snow properties such as grain size, surface roughness, 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 combined 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 snow grain size and free-water content, while millimeter-wave backscatter is primarily dependent on free-water content and, to some extent, on the surface roughness. A neural-network based inversion algorithm has been developed that optimally combines near-infrared and millimeter-wave measurements for accurate estimation of the relevant snow properties. The algorithm uses reflectances at wavelengths of 1160 nm, 1260 nm and 1360 nm, as well as co-polarized and cross-polarized backscatter at a frequency of 95 GHz. The inversion algorithm has been tested using simulated data, and is seen to perform well under noise-free conditions. Under noise-added conditions, a signal-to-noise ratio of 32 dB or greater ensures acceptable errors in snow parameter estimation.
The University of Nebraska has recently developed a neural network inversion algorithm for the estimation of surface snow properties, viz., surface roughness, wetness, and average grain size. The algorithm uses concurrent measurements of the near-infrared reflectance and millimeter-wave backscatter of the snow surface. The performance of the inversion algorithm was found to be satisfactory under noise-free conditions. However, under operational conditions, noise is invariably present in the data, and the addition of noise causes errors in estimation. The performance of the inversion algorithm was investigated under noise-added conditions. A parameter that was varied was the signal-to-noise ratio. Inversions of free-water content and the grain size were relatively robust, while the surface roughness estimation was very sensitive to added noise. The results of our study can be useful in setting bounds for system performance for accurate snow surface parameter inversion.
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