Snow Cover Mapping using Satellite Remote ...

This paper 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 [1996] 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.