AI 中文总结
本文综述了无人机载探地雷达在浅层冰冻圈地下成像中的系统架构、操作策略和数据处理方法，并通过东南极莫愁湖案例验证了其高精度（冰厚误差0.03米），指出了未来挑战。
AI 中文摘要
浅层冰冻圈地下结构的精确表征对于理解冰雪动力学、评估环境灾害以及为气候相关决策提供信息至关重要。近年来，无人机（UAV）和紧凑型雷达仪器的进步使得无人机载探地雷达（GPR）成为这些环境中地下传感的非接触式替代方案。通过将安全受限的无人机平台机动性与雷达的穿透能力相结合，无人机载探地雷达能够在冰冻圈中通常传统地面方法难以到达的雪面和冰面上实现快速、广覆盖且灵活的勘测。本文综述了无人机载探地雷达在冰冻圈调查中的最新进展，涵盖系统架构、操作策略和代表性部署实践。此外，总结了关键数据处理方法在增强地下成像和重建保真度方面的作用。来自东南极洲莫愁湖的案例研究展示了这些能力，与钻探测量相比，冰厚估计误差仅为0.03米。最后，讨论了未来冰冻圈应用中剩余的挑战，特别是与扫描覆盖范围、实时数据处理和检测精度相关的挑战。
英文摘要
Accurate characterization of shallow cryosphere subsurface structures is critical for understanding snow and ice dynamics, evaluating environmental hazards, and informing climate-related decision making. Recent advances in unmanned aerial vehicles (UAVs) and compact radar instrumentation have enabled UAV-borne ground penetrating radar (GPR) as a non-contact alternative for subsurface sensing in these environments. By combining the safety-constrained mobility of unmanned aerial platforms with the penetration capability of radar, UAV-borne GPR facilitates rapid, wide-coverage and flexible surveys over snow and ice surfaces in cryosphere that are often inaccessible to traditional ground-based methods. This contribution reviews recent progress in UAV-borne GPR for cryosphere investigations, encompassing system architectures, operational strategies, and representative deployment practices. Furthermore, key data-processing methodologies are summarized for their roles in enhancing subsurface imaging and reconstruction fidelity. A case study from Mochou Lake in East Antarctica, illustrates these capabilities, yielding an ice-thickness estimation error of only 0.03 m when benchmarked against drilling measurements. Finally, the remaining challenges, particularly those associated with scanning coverage, real-time data processing, and detection accuracy, are discussed for future cryosphere applications.
CommentsIEEE Geoscience and Remote Sensing Magazine Copyright 2026 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an email to pubs-permissions@ieee.org. The final published version will be available at: https://doi.org/10.1109/MGRS.2026.3712358 upon formal publication
