Having accurate estimations of snow cover characteristics during the snow-melt season is indispensable for efficient hydrological modeling and snow-melt runoff forecasting. Direct measurements of snow depth at a single station are generally not very useful in making estimates of accumulation over large areas. 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. Moreover, 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 study areas even under the same snowfall conditions. At present, most hydrological models that require snow-pack information are using maps obtained by gridding standard point gauge measurements or data derived from physically based models. The estimation of snow depth and snow water equivalent from passive microwave measurements requires a deep understanding of surface and volume emissivity of snow-pack and its underlying ground. The measured brightness temperature of the snow-covered surface is a function of both ground and snow cover properties, includes: surface roughness, surface temperature, vegetation cover, snow cover density, snow water equivalent, and snow grain size distribution.
The study discusses neural network based approach to generate the spatial distribution of snow accumulation using multi-channel Special Sensor Microwave/Imager (SSM/I) data. Five SSM/I channels (19H, 19V, 22V, 37V, and 85V) were used to remotely sense snow accumulation during 2001/2002 winter season. Ground snow depth measurements were acquired from the National Climatic Data Center (NCDC) through the Cooperative Observer Network for snow monitoring in the United States. The snow depths were compiled and gridded into 25 km x 25 km grid to match the final SSM/I spatial resolution. Neural network based approach was tested and compared with the filtering algorithm developed by Grody and Basist  in the Northern Midwest region of the United States. The results indicate that the neural-network-based approach has a great potential in identifying snow pixels from SSM/I data by providing a significant improvement in snow mapping accuracy over the filtering algorithm.
Authors: Hosni Ghedira, AbuDhabi UAE, Juan-Carlos Arevalo
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