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This review article provides an overview of various aspects of lunar exploration, including missions to the Moon, collection and analysis of lunar sample data in laboratories, and the processing and analysis of remote sensing data, particularly using radar techniques. Both remote sensing and in-situ methods are critical for advancing our understanding of the lunar surface and its properties. This review article focuses on the identification and quantification of water–ice deposits located in areas such as Permanently shadowed areas (PSRs) and the lunar poles (section " Lunar Poles and PSRs: A Special Environment "). These volatile resources have the potential to serve as valuable sources of fuel for future missions, making it crucial for the lunar community to determine their abundance and distribution. After thoroughly examining lunar samples using high-precision laboratory techniques, many preconceptions were dispelled which is highlighted in the section " Laboratory Investigation of Lunar samples ". But as in-situ observations are difficult to acquire, especially terrestrial bodies samples, remote sensing techniques allow the global understanding of the surface. The article specifically highlights the importance of understanding the electrical characteristics of the lunar surface and how radar inversion can provide valuable information in this regard. The " Conclusion " section of this review article serves as a key takeaway for readers, underscoring the critical role that both in-situ and remote sensing techniques play in advancing our understanding of the Moon. [ABSTRACT FROM AUTHOR]

To confirm the presence of water on the moon, many scientists of the world are making continuous efforts through remote sensing data of different missions. In this direction, the Dual Frequency Synthetic Aperture Radar (DFSAR) sensor of the Chandrayaan-2 mission adds a very important chapter which is the world's first Planetary SAR mission of fully polarimetric capability with L- and S-band. This study utilizes the L-band fully polarimetric DFSAR data of Chandrayaan-2 mission for the PolSAR parameters-based analysis and ice detection in permanently shadowed regions (PSRs) of the lunar South Polar craters. The PSR IDs SP_875930_3125710, SP_837670_3387710, and SP_874930_3578760 of the lunar South Pole were selected for the polarimetric analysis using DFSAR L-band. Based on previous studies ((Li et al., 2018), two out of three PSR Ids (SP_875930_3125710 and SP_874930_3578760) were easy to identify for surface ice. That is why only two PSR IDs were used for polarimetric SAR analysis of DFSAR data for surface ice characterization and detection. The hybrid polarimetric simulation was also performed to the fully polarimetric L-band data to study stokes vectors and associated child parameters for the selected study area. The analysis of polarimetric distortions confirms the persistence of the polarimetric quality of the SAR data and for this, the polarimetric distortion analysis was performed with co-pol and cross-pol channels. Wave dichotomy-based Huynen decomposition and Barnes decomposition models were implemented to the fully polarimetric quad-pol DFSAR data. The eigenvalue-eigenvector-based decomposition model was also implemented to characterize the scattering behavior of the PSRs. A high correlation was obtained between Circular Polarization Ratio (CPR), entropy, and alpha for the 200 hundred points randomly collected from the image. Diversity index also showed a high positive correlation with CPR. The polarimetric quality of the data was evaluated with the scatterplot between the cross-polarimetric channels and it was observed that the L-band quad-pol data of DFSAR satisfies the criteria for PolSAR data of a monostatic SAR system. Analysis of the results obtained from the polarimetric SAR data indicated that the high volumetric scattering and CPR for the PSR ID SP_875930_3125710 may be due to ice clusters within the permanently shadowed region. Polarimetric analysis of the PSR (SP_874930_3578760) at Howarth Crater using L-band DFSAR data shows a low amount of volumetric scattering and a low CPR for most locations in the PSR. The different ranges of CPR and volume scattering for both craters indicate that polarimetric parameters and indices should be studied in conjunction with geomorphological parameters of the lunar surface, for unambiguous identification of surface ice clusters in the PSR. [ABSTRACT FROM AUTHOR]

The lunar surface comprises complex geomorphological features, which have been formed by the conjunction of processes namely impact cratering and volcanism. Geological features on the Lunar surface can be bifurcated into two main areas named Maria region and the Highland region. Taurus-Littrow valley, which was the Apollo-17 mission landing site, consisting of unique geomorphological characteristics by having a sample size of both Lunar Maria and Highland regions. The dielectric constant is a parameter that gives an approximate distribution of the constituent material of the target area. It is a complex quantity, which indicates a periodic variation of the electric field. The real part of dielectric constant indicates stored energy and the imaginary part indicates dielectric loss factor or the loss of the electric field in the medium due to continuous varying electric field. Planetary surfaces for which determining dielectric constant is an important analysis for most of the space missions, ground measurement is not feasible. This work includes the machine learning-based modeling of dielectric constant for the Apollo 17 landing site the Taurus-Littrow valley. Based on the surface roughness of the study area, two models Gaussian and Exponential have been implemented and compared for the modeled output of the dielectric constant values.The modeling approaches for dielectric characterization of the lunar surface were implemented on NASA's LRO Mini-RF SAR data and Mini-SAR hybrid-pol data of ISRO's Chandrayaan-1 mission. The coefficient of determination (r2) and the root mean square error (RMSE) of the theoretical Gaussian model was 0.995, 0.042 and the Exponential model was 0.948, 0.1349 respectively. When compared with the already calculated values of dielectric constant from Apollo 17 return samples and literature survey, the Gaussian model gives a better variation. Gaussian model was further applied to the Lunar north pole crater namely Hermite-A crater, whose distinctive geomorphological characteristics and location being lunar north pole region, makes it one of the coldest places in the Solar System and a prominent location of water ice deposits. [ABSTRACT FROM AUTHOR]

Our closest neighbor (Moon) is full of wonders and potentials that can help us learn more about the solar system and provide a habitat for space colonies. The lunar surface contains several potential volatiles implanted by the solar wind that can be used for In-Situ Resource Utilization (ISRU). Also by characterizing the lunar surface we can comprehend many solar system phenomenon. Radar has been proven to be very effective in characterizing and understanding the physical properties of the lunar surface. The success of Chandrayaan-2 (CH-2) in 2019 opened numerous doors to better understanding the lunar surface. The Dual Frequency Synthetic Aperture Radar (DFSAR), is the first L-band SAR ever used for lunar mapping. We approximated the dielectric values for the Permanently Shadowed Regions (PSRs) by combining the Integral Equation Model (IEM) and Artificial Neural Network (ANN). The two-dimensional Gaussian model proved to be more accurate when correlated with in-situ measurements made by the Apollo 17 mission out of four different configurations, but the validation process has its limitations, thus we analyzed the result from all the models. The real part of complex dielectric values of unknown craters near Malapert crater (PSRs SP 83260 0090770 and SP 841420 0059530) and Faustini crater (PSR SP 871460 0840750) were high in certain sections of the region and moderate in others. The high dielectric values of 11.2 and 10.9 were estimated by the two-dimensional Gaussian and Exponential model for the right-side walls of the PSRs SP 83260 0090770 and SP 841420 0059530, respectively. For three-dimensional Gaussian and Exponential models, the same region but with fewer pixels showed high dielectric values of approximately. However, the estimated dielectric value for the left side wall was around 3.4 for two-dimensional models and 2.3 for three-dimensional models. On the left side of the region, the PSR SP 871460 0840750 had high dielectric values of 10.6 and 10.2, as calculated by the two-dimensional Gaussian and Exponential models and approximately 13 for the three-dimensional models. The right side of the region showed approximately 3.6 dielectric values for two-dimensional models and 4 for three-dimensional models. We noticed that some pixel values showed ambiguous dielectric values (<1) during the retrieval and validation process, notably in three-dimensional models, which could be linked to incidence angle variability and model sensitivity. This indicates the model's limitation and is treated as outliers, which we rectified by optimizing the threshold. Overall, the research brought attention to the intriguing dielectric constant variation in PSRs. • Dielectric values for the Permanently Shadowed Regions (PSRs). • Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR) data. • Combining Integral Equation Model (IEM) and Artificial Neural Network (ANN). • Two- and three-dimensional Gaussian and Exponential models. [ABSTRACT FROM AUTHOR]

Studies of the lunar surface from Synthetic Aperture Radar (SAR) data have played a prominent role in the exploration of the lunar surface in recent times. This study uses data from SAR sensors from three Moon missions: Chandrayaan-1 Mini-SAR, Lunar Reconnaissance Orbiter (LRO) Mini-RF and Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR). DFSAR sensor is the first of its kind to operate at L-band and S-band in fully and hybrid polarimetric modes. Due to the availability of only L-band data out of the two bands (L-and S-band) for the study site, this study only used DFSAR's L-band data. The dielectric characterization and polarimetric analysis of the lunar north polar crater Hermite-A was performed in this study using Chandrayaan-1 Mini-SAR, LRO Mini-RF and Chandrayaan-2 DFSAR data. Hermite-A lies in the Permanently Shadowed Region (PSR) of the lunar north pole and whose PSR ID is NP_879520_3076780. Because of its location within the PSR of the lunar north pole, the Hermite-A makes an ideal candidate for a probable location of water-ice deposits. This work utilizes S-band hybrid polarimetric data of Mini-SAR and Mini-RF and L-band fully polarimetric data of DFSAR for the lunar north polar crater Hermite-A. This study characterizes the scattering mechanisms from three decomposition techniques of Hybrid Polarimetry namely m-delta, m-chi, and m-alpha decompositions, and for fully polarimetric data Barnes decomposition technique was applied which is based on wave dichotomy. Eigenvector and Eigenvalue-based decomposition model (H-A-Alpha decomposition) was also applied to characterize the scattering behavior of the crater. This study utilizes the hybrid-pol and fully polarimetric data-based Integral Equation Model (IEM) to retrieve the values of dielectric constant for Hermite-A crater. The dielectric constant values for the Hermite-A crater from Chandrayaan-1 Mini-SAR and LRO Mini-RF are similar, which goes further in establishing the presence of water-ice in the region. The values of the dielectric constant for Chandrayaan-2 in some regions of the crater especially on the left side of the crater is also around 3 but overall the range is relatively higher than the compact/hybrid polarimetric data. The dielectric characterization and polarimetric analysis of the Hermite-A indicatively illustrate that the crater may have surface ice clusters in its walls and on some areas of the crater floor, which can be explored in the future from the synergistic use of remote sensing data and in-situ experiments to confirm the presence of the surface ice clusters. [ABSTRACT FROM AUTHOR]

The Moon has a large potential for space exploration and mining valuable resources. In particular, 3He provides rich sources of non-radioactive fusion fuel to fulfill cislunar and Earth's energy demands, if found economically feasible. The present study focuses on developing advanced techniques to prospect 3He resources on the Moon from multi-sensor remote sensing perspectives. It characterizes optical changes in regolith materials due to space weathering as a new retention parameter and introduces a novel machine learning inversion model for retrieving the physical properties of the regolith. Our analysis suggests that the reddening of the soil predominantly governs the retention, along with attenuated mafic band depths. Moreover, semi-variograms show that the spatial variability of 3He is aligned with the episodic weathering events at different timescales. We also observed that pyroclastic regoliths with high dielectric constant and increased surface scattering mechanisms exhibited a 3He abundant region. For ejecta cover, the retention was weakly associated with the dielectric contrast and a circular polarization ratio (CPR), mainly because of the 3He-deficient nature of the regolith. Furthermore, cross-variograms revealed inherent cyclicity attributed to the sequential process of weathering effects. Our study provides new insights into the physical nature and near-surface alterations of lunar regoliths that influence the spatial distribution and retention of solar wind implanted 3He. [ABSTRACT FROM AUTHOR]