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

Changyi Sun, Heng Da Cheng, Jeffery J. McDonnell, Christopher M.U. Neale

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3451-3454
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 1995
Externally publishedYes
EventProceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA
Duration: May 9 1995May 12 1995

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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