The precise detection of water-ice distributions within the permanently shadowed regions (PSRs) of the lunar south polar region is of paramount importance. We applied a polarimetric method for water-ice detection (PM4W) that utilizes Mini-RF data. The PM4W method incorporates several key radar scattering properties with topographical and environmental characteristics to detect water-ice within the lunar south polar region of 87 degrees S-90 degrees S. The method successfully identified 1578 water-ice containing pixels (each representing a 30 m x 30 m area) in the lunar shallow subsurface (1-3 m) at the south polar region, of which 1445 (similar to 91%) are spatially clustered in 29 PSRs. When comparing Mini-RF with M3 (each point representing a 280 m x 280 m area) using a buffer-based fuzzy assessment method, we found a pixel consistency of 60% and area consistency of 11%, which can be attributed to the differences in spatial resolution, positioning accuracy, and depth sensitivity. Moreover, over 90% of the water-ice pixels detected by Mini-RF are located within PSRs, accounting for 0.025% of their total area. In contrast, only 68% of the pixels detected by M3 are within PSRs, covering 0.760% of the PSRs area, which is approximately 30 times greater than the Mini-RF detections. The finer spatial resolution of the Mini-RF enables it to reveal previously undetectable features that align with the environmental mechanisms of water-ice storage. Our work contributes to assessing the potential presence of water-ice in vital exploration areas, providing pertinent indications for future lunar probes to identify water-ice on the Moon directly.

An increasing amount of evidence indicates that lunar water ice exists in permanently shadowed regions at the poles and will soon become an important resource for lunar exploration. However, the water ice content and distribution are still uncertain. We report a new 70-cm-wavelength radar image of the lunar south pole obtained by an Earth-based bistatic radar system consisting of the Sanya incoherent scatter radar (SYISR) and the five-hundred-meter aperture spherical radio telescope (FAST). The upper limit of water ice content (0 wt.%-6 wt.%) and its potential distribution are determined from a radar circular polarization ratio (CPR) map by considering the coherent backscatter opposition effect (CBOE) of water ice and ignoring the contribution of roughness to the CPR. This result is advantageous for future lunar exploration missions. (c) 2025 The Authors. Published by Elsevier B.V. and Science China Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

The Narrow-Angle Cameras (NACs) onboard the Lunar Reconnaissance Orbiter Camera (LROC) capture lunar images that play a crucial role in current lunar exploration missions. Among these images, those of the Moon's permanently shadowed regions (PSRs) are highly noisy, obscuring the lunar topographic features within these areas. While significant advancements have been made in denoising techniques based on deep learning, the direct acquisition of paired clean and noisy images from the PSRs of the Moon is costly, making dataset acquisition expensive and hindering network training. To address this issue, we employ a physical noise model based on the imaging principles of the LROC NACs to generate noisy pairs of images for the Moon's PSRs, simulating realistic lunar imagery. Furthermore, inspired by the ideas of full-scale skip connections and self-attention models (Transformers), we propose a denoising method based on deep information convolutional neural networks. Using a dataset synthesized through the physical noise model, we conduct a comparative analysis between the proposed method and existing state-of-the-art denoising approaches. The experimental results demonstrate that the proposed method can effectively recover topographic features obscured by noise, achieving the highest quantitative metrics and superior visual results.

Permanently shadowed regions (PSRs) on the Moon are potential reservoirs for water ice, making them hot spots for future lunar exploration. The water ice in PSRs would cause distinctive changes in space weathering there, in particular reduction-oxidation processes that differ from those in illuminated regions. To determine the characteristics of products formed during space weathering in PSRs, the lunar meteorite NWA 10203 with artificially added water was irradiated with a nanosecond laser to simulate a micrometeorite bombardment of lunar soil containing water ice. The TEM results of the water-incorporated sample showed distinct amorphous rims that exhibited irregular thickness, poor stratification, the appearance of bubbles, and a reduced number of npFe0. Additionally, EELS analysis showed the presence of ferric iron at the rim of the nanophase metallic iron particles (npFe0) in the amorphous rim with the involvement of water. The results suggest that water ice is another possible factor contributing to oxidation during micrometeorite bombardment on the lunar surface. In addition, it offers a reference for a new space weathering model that incorporates water in PSRs, which could be widespread on asteroids with volatiles.

The permanently shadowed regions (PSRs) of the Moon are located at the Moon's polar regions that are permanently in shadow due to their inability to receive direct sunlight. Images of these areas are usually dark and significantly affected by noise, obscuring the lunar terrain information. Although image denoising has made considerable progress, there is still limited study on images denoising of lunar PSRs. The main challenge lies in the fact that images of PSRs are characterized by low contrast, complex noise type, and uneven illumination. The existing deep learning-based methods exhibit poor physical interpretability and cannot effectively remove complex noise. Therefore, this study introduces a novel denoising method by using combination of physical noise models and deep Learning. Specially, the physical noise model is used to simulate the noise of lunar PSRs according to the imaging principles of the lunar reconnaissance orbiter camera narrow angle camera. The improved deep learning model, which incorporates full-scale skip connections and Transformer is used to denoise the images. The proposed method is tested in 297 PRSs images with latitudes below -70 degrees and compared with state-of-the-art methods. Experimental results demonstrate that the proposed method outperforms existing methods in restoring terrain details and provides better quantitative and visual outcomes. This approach has the potential to improve the clarity of lunar PSR images and support future lunar exploration.

Lunar exploration has attracted considerable attention, with the lunar poles emerging as the next exploration hot spot for the cold trapping of volatiles in the permanently shadowed regions (PSRs) at these poles. Remote sensing via the satellite's optical load is one of the most important ways to get the scientific data of PSRs. However, the illumination conditions at the lunar poles are quite different from the low latitude areas and how to get appropriate optical signal remains challenging. Thus, simulation of the optical remote sensing process, which provides reference for the choice of satellites' imaging parameters to ensure the implementation of lunar exploration project, is of great value. In this article, an optical imaging chain modeling for the PSRs at the lunar south pole, which includes lunar 3-D topography, observing satellite's orbit, instrument's parameters, and other environmental parameters, has been built. To demonstrate the physical accuracy, some PSRs' observations acquired by narrow angle cameras (NACs) equipped on the lunar reconnaissance orbiter (LRO) are compared with the corresponding images simulated by the proposed imaging chain model. The digital value's difference between the simulated images and real captured images is generally less than 50 for 12-bit images ranging from 0 to 4095, indicating a good fit considering the uncertainty of soil's absolute reflectance and the noise in the real captured images. In addition, the impact of the imaging chain's parameters is revealed with the proposed algorithm. The simulation method will provide reference and assist future optical imaging of PSRs.

The characterization of the lunar surface and subsurface through the utilization of synthetic aperture radar data has assumed a pivotal role in the domain of lunar exploration science. This investigation concentrated on the polarimetric analysis aimed at identifying water ice within a specific crater, designated Erlanger, located at the lunar north pole, which is fundamentally a region that is perpetually shaded from solar illumination. The area that is perpetually shaded on the moon is defined as that region that is never exposed to sunlight due to the moon's slightly tilted rotational axis. These permanently shaded regions serve as cold traps for water molecules. To ascertain the presence of water ice within the designated study area, we conducted an analysis of two datasets from the Chandrayaan mission: Mini-SAR data from Chandrayaan-1 and Dual-Frequency Synthetic Aperture Radar (DFSAR) data from Chandrayaan-2. The polarimetric analysis of the Erlanger Crater, located in a permanently shadowed region of the lunar north pole, utilizes data from the Dual-Frequency Synthetic Aperture Radar (DFSAR) and the Mini-SAR. This study focuses exclusively on the L-band DFSAR data due to the unavailability of S-band data for the Erlanger Crater. The crater, identified by the PSR ID NP_869610_0287570, is of particular interest for its potential water ice deposits. The analysis employs three decomposition models-m-delta, m-chi, and m-alpha-derived from the Mini-SAR data, along with the H-A-Alpha model known as an Eigenvector and Eigenvalue model, applied to the DFSAR data. The H-A-Alpha helps in assessing the entropy and anisotropy of the lunar surface. The results reveal a correlation between the hybrid polarimetric models (m-delta, m-chi, and m-alpha) and fully polarimetric parameters (entropy, anisotropy, and alpha), suggesting that volume scattering predominates inside the crater walls, while surface and double bounce scattering are more prevalent in the right side of the crater wall and surrounding areas. Additionally, the analysis of the circular polarization ratio (CPR) from both datasets suggests the presence of water ice within and around the crater, as values greater than 1 were observed. This finding aligns with other studies indicating that the high CPR values are indicative of ice deposits in the lunar polar regions. The polarimetric analysis of the Erlanger Crater contributes to the understanding of lunar polar regions and highlights the potential for future exploration and resource utilization on the Moon.

Small topographic features below the resolution of existing orbital data sets may create micro ultra-cold traps within the larger permanently shadowed regions that are present at the lunar poles. These ultra-cold traps are protected from the major primary and secondary illumination sources, and thus would create surfaces that are much colder than lower-resolution temperature maps would indicate. We examine this effect by creating a high resolution (1 m pix(-1)) terrain map based on upscaled data from the Lunar Orbiter Laser Altimeter. This map is illuminated by scattered sunlight and infrared emissions from sunlit terrain, which are then run through a thermal model to determine temperatures. We find that while most of the terrain experiences maximum temperatures around 50 K, there are a number of 1-30 m-scale ultra-cold traps with maximum temperatures as low as 20-30 K. By comparing our modeled ultra-cold trapping area to volatile abundances measured by Lunar Crater Observation and Sensing Satellite (LCROSS), we reveal a diverse environment where the surficial abundances necessary to explain the LCROSS results are strongly dependent on precisely where the impact occurred.

Understanding the reachability of water ice by future in-situ experiments near the lunar poles is crucial for supporting growing exploration plans and constraining the uncertainties on its genesis and distribution. To achieve this objective, we perform a thorough three-dimensional mapping of the distribution of water ice in the lunar polar regions (70 degrees onward), integrating radar, optical, and neutron detector observations from the Lunar Reconnaissance Orbiter mission (LRO). Our analysis reveals similar to 5-to-8-fold larger expanse of subsurface water ice (similar to 1-3 m depth) compared to surface water ice (up to 1 m depth) for the north and south poles, respectively. Our investigation cannot rule out the possibility of deep-seated water ice deposits in the lunar poles that remains beyond the detection capabilities of existing instruments on LRO. Moreover, we find that the extent of water ice in the northern polar region (similar to 1100 +/- 74 km(2)) is twice that in the southern polar region (similar to 562 +/- 54 km(2)). Our mapping also suggests that the dichotomous latitudinal distribution and the antipodal longitudinal distribution of water ice are likely driven by Mare volcanism and preferential cratering. We provide additional evidence that outgassing during Imbrian volcanism was probably the primary source of subsurface water ice in the lunar poles, which favors larger expanse over meteoritic sources.

Appropriate environmental sensing methods and visualization representations are crucial foundations for the in situ exploration of planets. In this paper, we developed specialized visualization methods to facilitate the rover's interaction and decision-making processes, as well as to address the path-planning and obstacle-avoidance requirements for lunar polar region exploration and Mars exploration. To achieve this goal, we utilize simulated lunar polar regions and Martian environments. Among them, the lunar rover operating in the permanently shadowed region (PSR) of the simulated crater primarily utilizes light detection and ranging (LiDAR) for environmental sensing; then, we reconstruct a mesh using the Poisson surface reconstruction method. After that, the lunar rover's traveling environment is represented as a red-green-blue (RGB) image, a slope coloration image, and a theoretical water content coloration image, based on different interaction needs and scientific objectives. For the rocky environment where the Mars rover is traveling, this paper enhances the display of the rocks on the Martian surface. It does so by utilizing depth information of the rock instances to highlight their significance for the rover's path-planning and obstacle-avoidance decisions. Such an environmental sensing and enhanced visualization approach facilitates rover path-planning and remote-interactive operations, thereby enabling further exploration activities in the lunar PSR and Mars, in addition to facilitating the study and communication of specific planetary science objectives, and the production and display of basemaps and thematic maps.