Snow depth estimation based on multipath phase combination of GPS triple-frequency signals

Kegen Yu, Wei Ban, Xiaohong Zhang, Xingwang Yu

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Snow is important to the ecological and climate systems; however, current snowfall and snow depth in situ observations are only available sparsely on the globe. By making use of the networks of Global Positioning System (GPS) stations established for geodetic applications, it is possible to monitor snow distribution on a global scale in an inexpensive way. In this paper, we propose a new snow depth estimation approach using a geodetic GPS station, multipath reflectometry and a linear combination of phase measurements of GPS triple-frequency (L1, L2, and L5) signals. This phase combination is geometry free and is not affected by ionospheric delays. Analytical linear models are first established to describe the relationship between antenna height and spectral peak frequency of combined phase time series, which are calculated based on theoretical formulas. When estimating snow depth in real time, the spectral peak frequency of the phase measurements is obtained, and then the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that the proposed method shows an improvement with respect to existing methods on average.

Original languageEnglish
Article number7110570
Pages (from-to)5100-5109
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume53
Issue number9
DOIs
Publication statusPublished - 1 Sep 2015
Externally publishedYes

Keywords

  • Global Navigation Satellite Systems multipath reflectometry (GNSS-MR)
  • multipath phase combination
  • snow depth estimation
  • triple-frequency signals

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