Lunar impact craters are important for studying lunar surface morphology because they are the most typical morphological units of the Moon. Impact crater descriptive indices can be used to describe morphological features and thus provide direct evidence for both the current state and evolution history of the Moon. Current description methods for lunar impact craters are predominantly qualitative, and mostly focus on their morphological profiles. Less attention is paid to the detailed morphological features inside and outside of the craters. A well-established and descriptive index system is required to describe the real morphological features of lunar impact craters, which are complex in a systematic way, and further improve study, such as heterogeneity analyses of lunar impact craters. This study employs a detailed lunar surface morphological analysis to propose a descriptive index system for lunar impact craters, including indices for the description of individual craters based on their morphological characteristics, spatial structures and basic composition (i.e., crater rim, crater wall, crater floor, central uplift, and ejecta), and indices for crater groups, including spatial distribution and statistical characteristics. Based on the proposed descriptive index system, a description standard for lunar impact craters is designed for categorising and describing these indices in a structured manner. To test their usability and effectiveness, lunar impact craters from different locations are manually detected, and corresponding values for different indices are extracted and organised for a heterogeneity analysis. The results demonstrate that the proposed index system can effectively depict the basic morphological features and spatial characteristics of lunar impact craters.

High-resolution digital terrain models (DTMs) are essential for Martian scientific exploration and engineering missions. However, traditional methods for Martian DTM generation are constrained by limitations in coverage and production efficiency, while the implicit treatment of illumination in existing deep learning methods leads to reconstructions lacking physical interpretability. To address this, we propose a Physics Fusion generative adversarial network based on the Mix Transformer architecture (PFMGAN) that incorporates physical prior knowledge to reconstruct high-precision DTMs from monocular images. This design significantly improves the accuracy and robustness of DTMs by enabling the network to effectively interpret grayscale variations caused by surface albedo and terrain undulations. Experimental results across various Martian landforms demonstrate that, by explicitly inputting solar angles and leveraging an Albedo-Aware Attention (AAA) module, PFMGAN achieves superior accuracy and robustness compared to other baseline models, with up to a 50% improvement in reconstruction accuracy in complex terrains. Furthermore, multi-scale (0.25–6 m/pixel) experimental results indicate that the proposed model is highly adaptable to reconstruction tasks across different spatial scales, consistently delivering high-quality topographic products. The results demonstrate the immense potential of PFMGAN for large-scale, high-precision Martian terrain reconstruction by leveraging the vast archive of monocular imagery.