Abstract
With the current extensive availability of synthetic-aperture radar (SAR) datasets with high temporal (e.g., a repeat cycle of a few or a dozen days) and spatial resolution (e.g., in the order of ∼1 m), radar remote sensing possesses an increasing potential for the monitoring of glacier surface motion thanks to the nearly weather and time-independent advantages. This paper proposes a robust subpixel frequency-based image correlation method for dense matching and integrates the improved matching into a workflow of glacier surface motion estimation using SAR intensity images with specific pre-processing and post-processing steps. The proposed matching method combines complex edge maps and local upsampling in the frequency domain for subpixel intensity tracking, which ensure the accuracy and robustness of glacier surface motion estimation. Experiments were carried out with TerraSAR-X and Sentinel-1 images covering two glacier areas in pole and alpine regions. The results of the monitoring and investigation of glacier motion validate the feasibility and reliability of the presented motion estimation method based on subpixel gradient correlation. The comparative results using both simulated and real SAR data indicate that the proposed matching method outperforms commonly used correlation-based matching methods in terms of matching accuracy and the ability to obtain correct matches.
Highlights
As an important part of the albedo feedback mechanism of the local and global climate systems, glaciers cover approximately 10% of the Earth’s surface [1]
We develop a robust subpixel frequency-based image correlation method, called locally upsampled gradient correlation (LUGC), to achieve reliable offset tracking of synthetic-aperture radar (SAR) intensity images, and validate the effectiveness in the applications of glacier surface motion estimation
This paper presents a motion estimation method of glacier surface using repeated SAR intensity images based on subpixel gradient correlation
Summary
As an important part of the albedo feedback mechanism of the local and global climate systems, glaciers cover approximately 10% of the Earth’s surface [1]. In the context of global warming, the melting of glaciers is predicted to make a greater contribution to global sea-level rise [2]. It is estimated that the melting of glaciers accounts for about 30% of the observed sea-level rise [3]. The optical images acquired by Earth observation satellites are the most commonly used datasets for glacier monitoring. The current spaceborne SAR images (e.g., TSX Spotlight Mode) can resolve a ground sampling distance of up to nearly 1 m, which is eligible for the task of glacier monitoring [8,9]. The main methods of using SAR image data to monitor glacier dynamics can be divided into two categories: interferometric methods and offset tracking-based methods
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