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TANG Huilei, HU Guoping, CHEN Yiping, et al. Advances in lunar lava tube skylight detection[J]. Journal of Radars, in press. doi: 10.12000/JR25190
Citation: TANG Huilei, HU Guoping, CHEN Yiping, et al. Advances in lunar lava tube skylight detection[J]. Journal of Radars, in press. doi: 10.12000/JR25190
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Advances in Lunar Lava Tube Skylight Detection

DOI: 10.12000/JR25190 CSTR: 32380.14.JR25190

  • School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China

Funds:  Young Top Talents of Guangdong Pearl River Talent Program (2023QN10H526), Leading Talents of Guangdong Pearl River Talent Program (2021CX02S024)
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  • Corresponding author: HU Guoping, hugp5@mail.sysu.edu.cn
  • Received Date: 2025-09-26
    Available Online: 2026-03-05
    Abstract
  • Formed by the cooling and solidification of flowing lava during volcanic activity, lunar lava tubes are considered promising candidates for future lunar bases due to their stable and protective roofs. However, these tubes are typically buried hundreds of meters to kilometers beneath the surface, making direct detection extremely difficult. Current detection methods mainly rely on radar and gravity anomaly analysis. However, the resolution of orbital radar is insufficient to distinguish similar subsurface structures, whereas in situ lunar penetrating radar is limited by a small detection range and vulnerability to near-field interference. Gravity anomaly detection also performs poorly when identifying tubes oriented north–south or with roofs narrower than a kilometer. Skylights serve as critical indicators for locating subsurface tubes and can be identified through optical imagery and infrared radiation thermal anomalies. However, optical images are constrained by illumination conditions, making full three-dimensional reconstruction of skylights difficult. Infrared data are further limited by penetration depth and spatial resolution (320 m × 160 m), which hinders the detection of subsurface thermal anomalies and the assessment of the thermophysical properties of materials at the pit floor. To address these challenges, this paper explores the feasibility of detecting skylight thermal anomalies using microwave radiation. Owing to its penetration capability and sensitivity to dielectric properties, this approach can probe subsurface thermal features and effectively determine the material composition of the pit floor. However, a significant scale disparity exists between the kilometer-scale resolution of current data and the relatively small size of skylights. Therefore, enhancing the detection capability of passive microwave methods for 100-m-scale skylights remains a critical issue that requires immediate attention.

     

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