[Significance] The characteristics of the lunar surface, including its mineral compositions, geological formations, environmental factors, and temperature variations, are essential for advancing our understanding of the Moon. These features provide a wealth of scientific data for lunar research, such as resource distribution, environmental characteristics, and evolutionary history. Spectral imagers, which detect mineral compositions in a nondestructive way, play a crucial role in analyzing the mineral compositions of the lunar surface and have become key payloads in scientific exploration missions. With the increasing demand for high-precision lunar exploration data and advancements in spectral imaging technology, there is a growing trend toward acquiring lunar remote sensing data with higher spatial and spectral resolution across a broad spectral range. This trend is shaping the future of lunar orbit exploration, allowing for unprecedented detail in probing the Moon's surface. However, the higher resolution of spatial and spectral data also introduces significant challenges in data processing. [Progress] This paper begins by summarizing existing lunar spectral orbit data, including payload parameters and associated scientific findings. It then explores specific technical challenges in the data processing chain, such as pre-processing and the calculation of lunar surface parameters. Mapping surface compositions through spectral remote sensing is particularly complex due to the mixing of minerals within rocks, which can obscure clear spectral signatures. To address these challenges, various theoretical and empirical approaches have been developed. This paper proposes technical methods and potential solutions to overcome these obstacles.[Conclusions] In conclusion, detailed studies of lunar surface characteristics and the acquisition of high-resolution spectral data are vital for advancing lunar science. Lunar hyperspectral data are expected to support manned lunar exploration and scientific research by enabling the identification of various minerals on the Moon's surface and determining their abundance through hyperspectral observations. Advances in spectral imaging technology and the development of solutions for processing high-resolution data will significantly enhance lunar and planetary science capabilities. These efforts will pave the way for deeper insights into the Moon's geology and potential resource utilization.

[Significance] Lunar remote sensing is a critical method to ensure the safety and success of lunar exploration missions while advancing lunar scientific research. It plays a significant role in understanding the Moon's geological evolution and the formation of the Earth-Moon system. Accurate lunar topographic maps are essential for mission planning, including landing site selection, navigation, and resource identification. These maps also provide valuable data for studying planetary processes and the history of the solar system. [Progress] In recent years, with growing global interest and investment in lunar exploration, remarkable progress has been made in remote sensing technology. These advancements have significantly improved the precision, resolution, and coverage of lunar topographic mapping. Various lunar remote sensing missions, such as China's Chang'e program, NASA's Lunar Reconnaissance Orbiter, and missions by other space agencies, have acquired substantial amounts of multi-source, multi-modal, and multi-scale data. This wealth of data has laid a solid foundation for technological breakthroughs. For instance, high-resolution laser altimetry, optical photogrammetry, and synthetic aperture radar have provided detailed datasets, enabling refined mapping of the Moon's surface. However, the dramatic increase in data volume, complexity, and heterogeneity presents challenges for effective processing, integration, and application in topographic mapping. This paper provides a comprehensive overview of the current state of lunar topographic remote sensing and mapping, focusing on the implementation and data acquisition capabilities of major lunar remote sensing missions during the second wave of lunar exploration. It systematically summarizes the latest research progress in key surveying and mapping technologies, including laser altimetry, which enables precise elevation measurements; optical photogrammetry, which reconstructs surface features using high-resolution imagery; and synthetic aperture radar, which provides unique insights into topographic and subsurface structures. [Prospect] In addition to reviewing recent advancements, the paper discusses future trends and challenges in the field. Key recommendations include enhancing sensor functionality and performance metrics to improve data quality, optimizing the lunar absolute reference framework for consistency and accuracy, leveraging multi-source data fusion for fine-scale modeling, expanding scientific applications of lunar topography, and developing intelligent and efficient methods to process massive amounts of remote sensing data. These efforts will not only support upcoming lunar exploration missions, such as China's manned lunar landing program scheduled for 2030, but also contribute to a deeper understanding of the Moon and its relationship with Earth.

[Objectives] The identification and classification of lunar impact craters are critical for selecting spacecraft landing sites and estimating the Moon's geological age. However, the complex morphological features created by impact processes post significant challenges to studying micro-scale lunar surface features, which are often indivisible at the pixel level. Addressing these challenges requires a scale-adaptive approach that incorporates micro-scale characteristics to refine lunar impact crater classification maps. [Methods] This study introduces a scale-adaptive algorithm based on geomorphons for the automatic classification of micro-scale lunar surface features. First, terrain parameters are optimized to define local ternary patterns of lunar geomorphology. These patterns are then used to determine lunar geomorphons. Next, the geomorphons are aggregated according to rules based on relief amplitude and slope to identify lunar impact geomorphic units on a larger scale. Finally, a classification map of lunar impact craters in the Gagarin Crater region is constructed using the identified geomorphons. [Results] The proposed method successfully identifies the optimal parameters for adaptively scaling lunar geomorphons by incorporating the unique characteristics of lunar surface features. Using a four-parameter constraint window, lunar geomorphons are refined at locally optimal spatial scales through the computation of local ternary patterns integrated with the theory of lunar geomorphological evolution. The results reveal that the generated maps of lunar geomorphons exhibit significant spatial aggregation, well-defined classification boundaries, and high accuracy in representing lunar impact craters. The method effectively captures the internal structural details of impact craters, providing a pixel-level depiction of their morphological features. The multi-scale identification of impact craters achieves a precision of 88.24%, a recall of 84.96%, and an F1 score of 86.57%. A classification schema for impact craters was established, including simple pit, small-scale bowl, small-scale flat bottom, small-scale central peak, medium flat bottom, medium central peak, large ring plain, and giant complex. [Conclusions] This method demonstrates robustness and high efficiency in crater identification, offering multi-scale geomorphological units and serving as a foundational tool for scale-based lunar scientific research. It provides technical support for identifying and classifying multi-scale lunar impact craters, contributing to advancements in lunar morphological and geological analysis.

[Objectives] Korolev basin is located within the Feldspathic Highlands Terrane and in the highest topographic area of the moon. It serves as the only farside landing site for the Lunar Geophysical Network mission to conduct geophysical measurements. The study of microwave thermal radiation characteristics of surface materials in Korolev basin can provide new and significant scientific references for the mission. [Methods] The microwave radiometer on Chang'e-1/2 satellites have made the first on-orbit passive microwave brightness temperature measurements of lunar regolith. The microwave radiometer data are sensitive to the temperature and composition of regolith, enabling the characterization of shallow regolith's thermal properties. Therefore, based on the data from the microwave radiometer on Chang'e-2 satellite, this study employed the centroid interpolation method based on Delaunay tetrahedralization to generate brightness temperature (TB) maps and then generate brightness temperature difference (dTB) maps. These maps were then combined with (FeO+TiO₂) abundance map generated from Clementine UV/VIS data, rock abundance map generated from Diviner data, thorium distribution map, and Bouguer gravity map to evaluate the microwave thermal radiation properties of the surface regolith in Korolev basin. [Results] (1) The four-quadrant analysis and profile analysis methods are employed, revealing that the TB behaviors of the central and northwestern part of Korolev basin are similar to those observed in Compton-Belkovich (C-B) region, both exhibiting microwave warm anomalies; (2) Along the inner rim of Korolev basin, there is a distribution of regions with low TB both at day and night; (3) In the western part of the basin, there is a region where the Diviner data don’t detect the presence of rocks, while the TB maps here show characteristics affected by rocks. Considering the different sensitivities of microwave and thermal infrared data to rock size, rocks not identified by thermal infrared data are distributed in this region. [Conclusions] (1) Radioactive elements are likely to be enriched at the bottom of Korolev basin, while the surface Th abundance cannot represent the Th abundance in the deep; (2) The impact events have exposed the purest anorthosite from the subsurface, leading to the occurrence of low TB anomalies. These findings provide new and important scientific references for further investigating the thermal state of the lunar highlands and the thermal activity of the shallow crust..

[Objectives] Typical elements on the surfaces of the Earth and other celestial bodies are essentially spatial entities with time-varying properties. These spatial entities or elements can be represented in either raster or vector data formats. The detection and extrapolation of their spatial patterns rely on a common theoretical foundation of spatial analysis. The spatial patterns and potential evolution of elements of the Earth and in deep-space scenarios are closely related to time and environmental factors. Their evolution, transitions, and correlations are a blend of necessity and contingency. This implies that detecting and extrapolating spatial patterns can be abstracted as a problem of occurrence probability for a phenomenon or scenario, consisting of transition probabilities and suitability probabilities governed by complex multifactorial mechanisms. [Methods] Therefore, we categorized the extensive factors influencing the spatial patterns and evolution of typical elements on Earth and in deep-space objects into five types: Topography (T), Constraints (C), Accessibility (A), Proximity (P) and Heterogeneity (H). We further proposed a universal theoretical paradigm, named TCAPH, for extrapolating occurrence-of-probability by integrating these five common types of factors. Based on the TCAPH theoretical framework, we developed a method for projecting elements' spatio-temporal evolution considering transition probability (TCAPH-Trans) and a method for spatial suitable site selection considering suitability probability (TCAPH-Suit). [Results] To validate the constructed theoretical paradigm and methods for spatial pattern extrapolation, we applied the TCAPH-Trans method to simulate and predict the spatial patterns of typical Earth's surface elements (such as land-use change). We also used the TCAPH-Suit method to extrapolate the possibility of establishing a lunar scientific research station in the de Gerlache area of the lunar south pole. [Conclusions] Case applications demonstrate that the proposed TCAPH theoretical paradigm is effective for analyzing and extrapolating both primary and derived spatial patterns. The TCAPH-Trans and TCAPH-Suit methods can address the challenges of calculating scenario transition probabilities and site selection suitability probabilities, enabling characterization and extrapolation of spatial patterns with minimized probabilistic mapping errors. The methods and models proposed in this study are suitable for analyzing and supporting decisions for various spatial entities across different scenarios, effectively extending applications for spatial elements from Earth's surface to deep-space celestial objects. Future work will focus on developing an integrated platform system that combines spatial scene selection, spatial element analysis, and spatial pattern derivation to provide real-time and dynamic spatial pattern detection and evolution services, thereby expanding the application areas of the TCAPH model framework.

[Objectives] Rovers play an essential role in lunar exploration, serving as vital tools for scientists aiming to unravel the Moon's geological history and exploit its potential water-ice reserves. However, navigating the lunar surface with rovers presents significant safety risks due to the complex and often hazardous terrain, compounded by the lack of a consistent and reliable light source. The absence of pre-existing, high-resolution data—such as LiDAR—prior to exploration missions poses a considerable challenge in evaluating the safety of potential rover paths. Given these constraints, developing a reliable pre-assessment method is crucial for enhancing the success rate of lunar rover missions. [Methods] This paper introduces a 3D simulation method for lunar rover exploration, leveraging the Visualization Toolkit (VTK) to address these challenges. Our method integrates three critical aspects. Firstly, it offers high-resolution visualization of the lunar surface terrain, capturing intricate details down to the meter scale. Secondly, it simulates the dynamic illumination environment on the lunar surface, accounting for the varying illumination conditions due to the Moon 's rotation and orbital position. Thirdly, it models the rover's position and attitude transformations as it navigates the terrain. [Results] The effectiveness of this simulation approach is demonstrated through a case study focusing on the Shackleton Connecting Ridge region at the lunar South Pole, an area of significant interest due to its challenging topography and potential for water-ice deposits. The 3D simulation accurately depicts the undulating terrain of impact craters and allows for a thorough assessment of the rover's route safety by visualizing the potential hazards along the path. Moreover, the simulation offers an intuitive representation of the rover's movement, including real-time adjustments in position and attitude, which are critical for ensuring the rover’s stability and operational safety over long distances. Additionally, our method includes a real-time update feature for the dynamic illumination scene, enabling direct observation of how changing light conditions affect the rover's path during the mission. This capability is particularly important for assessing the feasibility of navigating through areas that may experience prolonged periods of darkness or extreme shadowing, which could impede the rover's progress or jeopardize its safety. The goal of this research is to improve the reliability and safety of future lunar rover missions by providing a robust pre-assessment tool that can verify the feasibility of proposed exploration routes. [Conclusions] This method thus offers crucial a priori information, serving as an essential guarantee for the successful execution of future lunar exploration endeavors.