Articles | Volume 16, issue 6
Research article
15 Jun 2022
Research article |  | 15 Jun 2022

Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements

Bas Altena, Andreas Kääb, and Bert Wouters

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Cited articles

Altena, B. and Kääb, A.: Elevation change and improved velocity retrieval using orthorectified optical satellite data from different orbits, Remote Sens., 9, 300,, 2017. a
Altena, B., Scambos, T., Fahnestock, M., and Kääb, A.: Extracting recent short-term glacier velocity evolution over southern Alaska and the Yukon from a large collection of Landsat data, The Cryosphere, 13, 795–814,, 2019. a, b
Anthony, S. M. and Granick, S.: Image analysis with rapid and accurate two-dimensional Gaussian fitting, Langmuir, 25, 8152–8160,, 2009. a, b
Bhattacharya, S., Charonko, J., and Vlachos, P.: Particle image velocimetry (PIV) uncertainty quantification using moment of correlation (MC) plane, Meas. Sci. Technol., 29, 115301,, 2018. a
Brinkerhoff, D. and O'Neel, S.: Velocity variations at Columbia Glacier captured by particle filtering of oblique time-lapse images, arXiv preprint arXiv:1711.05366, (last access: 1 June 2022), 2017. a
Short summary
Repeat overflights of satellites are used to estimate surface displacements. However, such products lack a simple error description for individual measurements, but variation in precision occurs, since the calculation is based on the similarity of texture. Fortunately, variation in precision manifests itself in the correlation peak, which is used for the displacement calculation. This spread is used to make a connection to measurement precision, which can be of great use for model inversion.