High-resolution Topographic Modeling for the Lunar South Pole Region Using NAC and ShadowCam Images

Abstract

Illumination conditions in the lunar polar areas are highly complex. Owing to low sun elevation angles and the lack of seasons in these areas, combined with rugged topography, this may lead to Permanently Shadowed Regions (PSRs) in craters and topographic depressions, where low temperatures allow ice to accumulate. In contrast, Long-term Illuminated Areas (LIAs) on high-standing ridges and crater rims enable almost uninterrupted illumination and solar power supply. High-resolution Digital Terrain Models (DTMs) are crucial for modelling these illumination conditions and for general support of future polar exploration missions. We choose to derive those models from images captured by the Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) for LIAs and by ShadowCam for PSRs. Here, we use our deep learning method developed previously to derive south pole DTMs, which can achieve a similar or even better effective resolution to those produced by the SFS method, stereo photogrammetry, or laser altimetry. We selected Shackleton Crater (a typical PSR) and Malapert Massif (a candidate landing site for the Artemis Program) as experimental areas to derive DTMs with resolutions of 2 meters and 1 meter, respectively. Finally, we used the DTMs to perform refined illuminated modeling and analysis to support future lunar south pole exploration missions.