Detecting the onset of snow-melt using SSM/I data and the self-organizing map

  • Authors:

  • M. Takala;J. Pulliainen;M. Huttunen;M. Hallikainen

  • Affiliations:

  • Finnish Meteorogical Institute, Sodankylä Arctic Research Centre, FI-99600 Sodankylä, Finland;Finnish Meteorogical Institute, Sodankylä Arctic Research Centre, FI-99600 Sodankylä, Finland;Finnish Environment Institute, FIN-00251 Helsinki, Finland;Helsinki University of Technology, Laboratory of Space Technology, 02015 HUT, Finland

  • Venue:
  • International Journal of Remote Sensing
  • Year:
  • 2008

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Abstract

In this paper, we present an algorithm to estimate the onset of seasonal snow-melt using space-borne microwave radiometer data. We have earlier developed a simple model called a Channel Difference Algorithm (CDA) to estimate the beginning of the snow-melt. The new algorithm, the SOM Detection Algorithm (SDA), is based on the use of an artificial neural network system a called Self-Organizing Map (SOM). The purpose of this research is to develop a robust and simple algorithm feasible for operative use. The algorithm is tested using SSM/I data with hydrological predictions as reference data. The reference data covers two winters, 1997 and 1998, and is for the boreal forest zone in Finland. The results are promising. The SDA is able to estimate the beginning of the final snow-melt well, especially if the snow water equivalent exhibits large values. Using low-pass filtering for the SDA estimated time series, the estimation can be improved.