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<p>Global mapping of lunar surface chemistry is crucial for revealing the geological characteristics and evolutionary history of the Moon. However, existing estimates of elemental abundances rely primarily on remote sensing data calibrated with sample-based ground truth information from the lunar nearside, leaving the farside largely unconstrained and limiting the accuracy of global chemical models. Here we integrate farside ground truth data from the Chang’e-6 sampling site, together with pre-existing nearside sample data and orbital spectral datasets from the Kaguya multiband imager, to refine global chemical maps. We apply a residual convolutional neural network with a fine-tuning strategy to optimally calibrate elemental abundances across the surface. The resulting global maps constrain the extent and composition of farside terranes and reveal deep-seated materials exposed in the South Pole–Aitken basin and highlands. These refined maps offer quantitative guidance for landing site selection and future lunar exploration missions.</p>

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Refined lunar global chemistry mapping using farside ground truth information gathered by Chang’e-6

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Xuming Shi,
  • Jian Chen,
  • Juntao Wang,
  • Shijie Liu,
  • Xiaohua Tong,
  • Jinning Li,
  • Zeyi Yan,
  • Shiji Wen,
  • Huan Xie,
  • Zhiping He,
  • Junhao Chu,
  • Weida Hu,
  • Fang Wang

摘要

Global mapping of lunar surface chemistry is crucial for revealing the geological characteristics and evolutionary history of the Moon. However, existing estimates of elemental abundances rely primarily on remote sensing data calibrated with sample-based ground truth information from the lunar nearside, leaving the farside largely unconstrained and limiting the accuracy of global chemical models. Here we integrate farside ground truth data from the Chang’e-6 sampling site, together with pre-existing nearside sample data and orbital spectral datasets from the Kaguya multiband imager, to refine global chemical maps. We apply a residual convolutional neural network with a fine-tuning strategy to optimally calibrate elemental abundances across the surface. The resulting global maps constrain the extent and composition of farside terranes and reveal deep-seated materials exposed in the South Pole–Aitken basin and highlands. These refined maps offer quantitative guidance for landing site selection and future lunar exploration missions.