Identification of mountain snow cover using SSM/I and artificial neural network

The Special Sensor Microwave/Imager (SSM/I) radiometer is practical in monitoring snow conditions for its sensitive response to the changes in snow properties. A single-hidden-layer artificial neural network (ANN) was employed to accomplish this remote sensing task, with radiometric observations of brightness temperatures (Tb's) as input data, to derive information about snow. Error back-propagation learning was applied to train the ANN. After learning the mapping of SSM/I Tb's to snow classes, ANN approach showed a significant promise for identifying mountainous snow conditions. Error rates were 3% for snow-free, 5% for dry snow, 9% for wet snow, and 0% for refrozen snow, respectively. This study indicates the potential of ANN supervised learning for the inversion of snow conditions from SSM/I observations. Further improvement on the application of ANN for large-scale snow monitoring can be expected by using more training data derived from both plains and mountain regions.