Glacier surface velocity estimation using repeat TerraSAR-X images: Wavelet- vs. correlation-based image matching

Abstract For the observation and monitoring of glacier surface velocity (GSV), remote sensing is an increasingly suitable tool thanks to the high temporal and spatial resolution of the data. Radar sensors have the specific advantage over optical sensors of being nearly weather and time-independent. Two image pairs separated by 11 days, acquired with the high-resolution spotlight (HS) and stripmap (SM) modes of the German sensor TerraSAR-X, were used to estimate GSV over Switzerland’s Aletsch Glacier. The SM mode covers larger ground swaths, making it more suitable for glacier-wide observations, while the HS images cover less area but offer the highest-possible spatial resolution, approximately 1 × 1 m on the ground. The images were acquired during the summer to maximise feature visibility by minimal snow cover. GSV estimation was performed using two methods, the comparison of which was a major goal of this study: traditional cross-correlation optimisation and a dense image matching algorithm based on complex wavelet decomposition. Each method was found to have unique advantages and disadvantages, but it was concluded that for GSV monitoring, cross-correlation is probably preferable to the wavelet-based approach. While it generates fewer estimates per unit area, this is not necessarily a critical requirement for all glaciological applications, and the method requires less initial “tuning” (calibration) than the wavelet algorithm, making it a slightly better tool in operational contexts. Also, the use of the highest-resolution spotlight datasets is recommended over stripmap mode images when large-area coverage is less critical. The comparative lack of visible features at the resolution of the stripmap images made reliable GSV estimation difficult, with the exception of several small areas dominated by large crevasses.