We present the proposed strategic study, ‘Integrated elements for Martian life signature exploration’, to support the sampling and identification of any potential biosignatures in compliance with the engineering constraints of the Tianwen-3 mission.

Radargrams obtained by the Yutu‐2 rover reveal that the post‐mare deposits at the Chang'E‐4 landing site are ∼45 m thick, consistent with estimations based on orbital observations. Besides obvious ejecta from Finsen, polarized interpretations exist on whether or not the Alder crater is another contributor to the post‐mare deposits, although predicted thicknesses of ejecta from all potential source craters are not adequately large compared to observations. We recognize that the rover has been exploring along a shared crater wall of two secondaries from Finsen. Mechanics of secondary impacts suggests that the discontinuous layers of coarse and fine materials observed in the radargram are older than Finsen. Cross‐cutting relationships and crater density comparisons show that Alder is older than the mare basalts, and the Orientale basin was the major source for the post‐mare materials. Lower‐crust materials excavated by Orientale constituted a substantial portion of the surface regolith detected by the rover.

Impact craters are the dominant features on the Moon, and their degradation with time is the most common geological process on this body. This work is aimed at detailed quantitative characterization of this process. We quantitatively characterize crater materials by (1) the topographic roughness calculated from Lunar Orbiter Laser Altimeter and (2) rock abundances and nighttime soil temperatures derived from Diviner measurements. In contrast to preceding works, we separately consider crater material subunits: central peaks, floors, walls, and continuous ejecta to study their degradation in detail. We mapped totally 4,770 individual crater material subunits. All subunits of the youngest craters are characterized by increased roughness, rock abundance, and soil temperature. These parameters decrease with age and tend toward equilibrium, stable state; the continuous ejecta reach equilibrium more rapidly than other subunits. Kilometer‐baseline roughness of crater walls in the lunar maria is higher than in the highlands. Rock abundances and soil temperatures of the walls and floors of simple craters in the maria are also higher than in the highlands. We attribute these trends to differences in the mechanical properties of the target materials. Although the properties studied are not exact proxies for age, they can be used to assess individual age predictions; for example, several craters that were erroneously classified as Copernican have been detected. We also found that the unusual thermophysical signature of the walls of the Late Imbrian crater Bonpland D is due to recent regolith flows that could have been caused by a strong, shallow seismic event.

Yutu-2 rover conducted an exciting expedition on the 41st lunar day to investigate a fin-shaped rock at Longji site (45.44°S, 177.56°E) by extending its locomotion margin on perilous peaks. The varied locomotion encountered, especially multi-form wheel slippage, during the journey to the target rock, established unique conditions for a fin-grained lunar regolith analysis regarding bearing, shear and lateral properties based on terramechanics. Here, we show a tri-aspect characterization of lunar regolith and infer the rock’s origin using a digital twin. We estimate internal friction angle within 21.5°−42.0° and associated cohesion of 520-3154 Pa in the Chang’E-4 operational site. These findings suggest shear characteristics similar to Apollo 12 mission samples but notably higher cohesion compared to regolith investigated on most nearside lunar missions. We estimate external friction angle in lateral properties to be within 8.3°−16.5°, which fills the gaps of the lateral property estimation of the lunar farside regolith and serves as a foundational parameter for subsequent engineering verifications. Our in-situ spectral investigations of the target rock unveil its composition of iron/magnesium-rich low-calcium pyroxene, linking it to the Zhinyu crater (45.34°S, 176.15°E) ejecta. Our results indicate that the combination of in-situ measurements with robotics technology in planetary exploration reveal the possibility of additional source regions contributing to the local materials at the Chang’E-4 site, implying a more complicated geological history in the vicinity.

At present, the selection of lunar landing areas is mostly determined by experts’ argumentation and experience. Generally, it is artificially limited to a small zone, and there are few effective quantitative models for landing areas. Under the premise that big data, artificial intelligence, and other technologies are becoming increasingly mature, with in-depth analysis and the mining of lunar-related digital data, it is possible to automatically optimize the landing zones in the whole moon. Factors such as engineering constraints, scientific goals, and resource requirements are comprehensively considered. This paper proposes a new method that strategically applies the weights of evidence (WoE) and fractals to optimize the landing area of the detector in the whole moon. The method takes the thickness of the lunar crust, roughness, slope, digital elevation model, gravity gradient, iron oxide distribution, and lunar soil optical maturity as evidence layers, and known landing sites as the target layer. After all moon data are divided into grids, the prior probability of each evidence factor, the in-cell weight of each evidence factor, and the Bayesian posterior probability are calculated. According to the semi-parabolic distribution in the fuzzy distribution, the fuzzy membership degree of the impact crater radius is presented and the complexity of the number of impact craters in a cell is calculated. The distribution complexity of impact craters in each cell is calculated according to the fractal. The result of the weights of evidence is further constrained by the complexity of the number of cells and the complexity of the distribution, and the posterior probability map of suitable landings is finally obtained. When comparing and analyzing the posterior probability map of the landing zones with the known landing points and the artificially preferred landing zones, it is found that 84% and 82.6% fall within the suitable landing zones, respectively. Among them, the first gradient is 58% and 58.7%, and the second gradient is 26% and 23.9%. The results at different resolutions are relatively stable and are consistent with the distribution of craters or basins in the lunar mantle and the spatial distribution of olivine, which proves the effectiveness and feasibility of this method. This method is a typical application of lunar big-data-driven knowledge discovery and will help promote the transformation of lunar landing area selection from traditional qualitative analyses to automated intelligence optimization.