The Effect of Vegetation Cover on Snow Cover Mapping from Passive Microwave Data

Snow-cover parameters are being increasingly used as inputs to hydrological models. Having an accurate estimation of the snow cover characteristics during the snowmelt season is indispensable for an efficient hydrological modeling and for an improved snowmelt runoff forecasts. 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 to train and test the neural network system. 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 times 25 km grid to match the final SSM/I resolution. Different vegetation-related parameters (NDVI, optical depth, homogeneity) have been collected and gridded over the study area. The current results have shown a significant effect of vegetation cover properties on the mapping accuracy. Furthermore, the addition of vegetation related information to the mapping process has shown to have a positive impact on mapping performance, especially for areas with shallow snow cover (less than 5 cm)