A Method for Automatic Three-Dimensional Reconstruction of Ice Sheets by Using Radar Sounder and Altimeter Data

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Ilisei, Ana-Maria, Bruzzone, Lorenzo
• Format: Article in Journal/Newspaper
• Language: English
• Published: country:USA 2018
• Subjects: altimeter; ice sheet; radar sounder; remote sensing; Three-dimensional (3-D) reconstruction; Computers in Earth Science; Atmospheric Science; Byrd; Byrd Glacier; Antarc*; Antarctica; Ice Sheet
• Online Access: http://hdl.handle.net/11572/218216; https://doi.org/10.1109/JSTARS.2017.2787199; https://ieeexplore.ieee.org/abstract/document/8275482

Understanding the processes occurring at the ice sheets requires reliable three-dimensional (3-D) models of the ice sheet geometry. To address this challenge, we propose a technique for the 3-D reconstruction of the ice sheet geometry that uses radar sounder (RS) and altimeter (ALT) data to automatically identify the scale (or grid size) for interpolation. Existing studies derive the interpolation scale empirically, by qualitatively analyzing the RS data sampling and often neglecting the surface topography effects. Our method initially performs the interpolation of RS data at several potential scales. At each scale, it uses the ordinary kriging interpolation method that enables the quantitative analysis of both the RS data sampling and the surface topography. The optimal scale for the estimation of the surface map is identified according to an objective criterion that minimizes the difference to a subset of reference ALT data. Thereafter, the identified optimum scale on the surface is used to estimate the bedrock and ice thickness maps. Thus, the technique is a best-effort approach to the reconstruction of the ice sheet geometry, given the reference surface data and in the absence of reference bedrock data. Results obtained by applying the method to RS and ALT data acquired over the Byrd Glacier in Antarctica, in four regions characterized by different RS sampling and surface topography, confirm its effectiveness. Moreover, they point out that the method could be used for guiding future RS surveys, since the identified optimal scales are typically larger than those needed for addressing specific science objectives.