An Adaptive Neural Network System for Improving the Filtration of Non-Snow Pixels from SSM/I Images

摘要

Snow-cover parameters are being increasingly used as input to hydrological models. Having an accurate estimation of snow cover characteristics during the snowmelt season is indispensable for an efficient hydrological modeling and snowmelt runoff forecasting. Direct measurements of snow depth at a single station are generally not very useful in making estimates of distribution over large areas, since the measured depth may be highly unrepresentative of the neighboring area because of drifting or blowing. Additionally, the traditional field sampling methods and the ground-based data collection are often very sparse, time consuming, and expensive compared to the coverage provided by remote sensing techniques. At present, most of hydrological models that require snowpack information are using maps obtained by gridding standard point gauge measurements or derived from physicaily/statistically-based models. In this paper, we used an adaptive neural network system to generate the spatial distribution of snow accumulation from multi-channel SSM/I data in the Northern Midwest of the United States. Five SSM/I channels were used in this experiment (19H, 19V, 22V, 37V, and 85V). Three snow days with high snow accumulation and no precipitation have been selected during the 2001/2002 winter season. Snow depth measurements have been collected from the National Climatic Data Center (NCDC) through the Cooperative Observer Network for the U.S. snow Monitoring. The snow depths have been compiled and gridded into 25 km × 25 km grid to match the final SSM/I resolution.