Abstract
Abstract. In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images.
Thereby, much emphasis has been put on the fast processing of large data volumes and products with complete spatial coverage.
The metadata of such measurements are often highly simplified when the measurement precision is lumped into a single number for the whole dataset, although the error budget of image matching is in reality neither isotropic nor constant over the whole velocity field.
The spread of the correlation peak of individual image offset measurements is dependent on the image structure and the non-uniform flow of the ice and is used here to extract a proxy for measurement uncertainty.
A quantification of estimation error or dispersion for each individual velocity measurement can be important for the inversion of, for instance, rheology, ice thickness and/or bedrock friction.
Errors in the velocity data can propagate into derived results in a complex and exaggerating way, making the outcomes very sensitive to velocity noise and outliers.
Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focusing on satellite data.
The approach is based upon Gaussian fitting directly on the correlation peak and is formulated as a linear least-squares estimation, making its implementation into current pipelines straightforward.
The methodology is demonstrated for Sermeq Kujalleq (Jakobshavn Isbræ), Greenland, a glacier with regions of strong shear flow and with clearly oriented crevasses, and Malaspina Glacier, Alaska.
Directionality within an image seems to be the dominant factor influencing the correlation dispersion.
In our cases these are crevasses and moraine bands, while a relation to differential flow, such as shear, is less pronounced on the correlation spread.
Funder
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
European Space Agency
Subject
Earth-Surface Processes,Water Science and Technology
Reference39 articles.
1. Altena, B. and Kääb, A.: Elevation change and improved velocity
retrieval using orthorectified optical satellite data from different orbits,
Remote Sens., 9, 300, https://doi.org/10.3390/rs9030300, 2017. a
2. 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, https://doi.org/10.5194/tc-13-795-2019, 2019. a, b
3. Anthony, S. M. and Granick, S.: Image analysis with rapid and accurate
two-dimensional Gaussian fitting, Langmuir, 25, 8152–8160,
https://doi.org/10.1021/la900393v, 2009. a, b
4. Bhattacharya, S., Charonko, J., and Vlachos, P.: Particle image velocimetry
(PIV) uncertainty quantification using moment of correlation (MC) plane,
Meas. Sci. Technol., 29, 115301,
https://doi.org/10.1088/1361-6501/aadfb4, 2018. a
5. 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, https://arxiv.org/abs/1711.05366 (last access: 1 June 2022), 2017. a
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