A novel airborne TomoSAR 3-D focusing method for accurate ice thickness and glacier volume estimation

High-altitude mountain glaciers are highly responsive to environmental changes. However, their remote locations limit the applicability of traditional mapping methods, such as probing and Ground Penetrating Radar (GPR), in tracking changes in ice thickness and glacier volume. Over the past two decades, airborne Tomographic Synthetic Aperture Radar (TomoSAR) has shown promise for mapping the internal structures of mountain glaciers. Yet, its 3D mapping capabilities are limited by the radar signal’s relatively shallow penetration depth, with bedrock echoes rarely detected beyond 60 meters. Additionally, most TomoSAR studies ignored the air-ice refraction during the image-focusing step, reducing the 3D focusing accuracy for deeper subsurface targets. In this study, we developed a novel algorithm that integrates refraction path calculations into SAR image focusing. We also introduced a new method to construct the 3D TomoSAR cube by stacking InSAR phase coherence images, enabling the retrieval of deep bedrock signals even at low signal-to-noise ratios.

We tested our algorithms on 14 P-band SAR images acquired on April 8, 2023, over Bayi Glacier in the Qilian Mountains, located on the Qinghai-Tibet Plateau. For the first time, we successfully mapped the ice thickness across an entire mountain glacier using the airborne TomoSAR technique, detecting bedrock signals at depths reaching up to 120 m. Our ice thickness estimates showed strong agreement with in situ measurements from three GPR transects totaling 3.8 km in length, with root-mean-square errors (RMSE) ranging from 3.18 to 4.66 m. For comparison, we applied the state-of-the-art 3D focusing algorithm used in the AlpTomoSAR campaign for ice thickness estimation, which resulted in RMSE values between 5.67 and 5.81 m. Our proposed method reduced the RMSE by 18% to 44% relative to the AlpTomoSAR algorithm. Based on these measurements, we calculated a total ice volume of 0.121 km${}^3$, reflecting a decline of approximately 20.92% since the last reported volume in 2009, which was estimated from sparse GPR data. These results demonstrate that the proposed algorithm can effectively map ice thickness, providing a cost-efficient solution for large-scale glacier surveys in high-mountain regions.