The physical properties of lunar regolith are crucial for exploration planning, hazard assessment, and characterizing scientific targets at global and polar scales. The dielectric constant, a key property, offers insights into lunar material distribution within the regolith and serves as a proxy for identifying volatile-rich regoliths. Miniature radio frequency (Mini-RF) on the Lunar Reconnaissance Orbiter (LRO) provides a potential tool for mapping the lunar regolith's physical nature and assessing the lunar volatile repository. This study presents global and polar S-band Mini-RF dielectric signatures of the Moon, obtained through a novel deep learning inversion model applied to Mini-RF mosaics. We achieved good agreement between training and testing of the model, yielding a coefficient of determination (R2 value) of 0.97 and a mean squared error of 0.27 for the dielectric constant. Significant variability in the dielectric constant is observed globally, with high-Ti mare basalts exhibiting lower values than low-Ti highland materials. However, discernibility between the South Pole-Aitken (SPA) basin and highlands is not evident. Despite similar dielectric constants on average, notable spatial variations exist within the south and north polar regions, influenced by crater ejecta, permanently shadowed regions, and crater floors. These dielectric differences are attributed to extensive mantling of lunar materials, impact cratering processes, and ilmenite content. Using the east- and west-looking polar mosaics, we estimated an uncertainty (standard deviation) of 1.01 in the real part and 0.03 in the imaginary part of the dielectric constant due to look direction. Additionally, modeling highlights radar backscatter sensitivity to incidence angle and dielectric constant at the Mini-RF wavelength. The dielectric constant maps provide a new and unique perspective of lunar terrains that could play an important role in characterizing lunar resources in future targeted human and robotic exploration of the Moon.

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 ( 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 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 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. (c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

Polarimetric synthetic aperture radar (SAR) is an effective technique to retrieve physical properties of planetary surfaces, such as the dielectric constant and surface roughness. Dielectric properties of lunar regolith are quite attractive for future lunar SAR missions. In this paper, we investigate the dielectric properties of lunar regolith by the Mini-RF SAR data. First, a new model of dielectric constant inversion for hybrid polarimetric SAR is proposed, in which the hybrid polarimetric scattering similarity parameter is first introduced. Second, the dielectric constants of Apollo 14, 16, 17 and Chang'E-5 landing sites are estimated through the proposed model. The inversion results fit well with the laboratory measurements of lunar samples, with an estimated root mean square error (RMSE) of 0.53. In addition, we analyze the dielectric properties of regolith on crater floors in different geologic settings, including the lunar maria, highlands, and permanently shadowed regions (PSRs) near the lunar poles. The results indicate that for craters with diameters of 5-25 km, the real part of the dielectric constant of regolith fines increases with crater depth-to-diameter (d/D) ratio, while no apparent correlation is found with crater diameter. Furthermore, the average dielectric constant of regolith fines is 3.01 in PSRs, which is less than that in the lunar maria and highlands (3.43 and 4.13, respectively). This implies that craters in PSRs may possess a looser regolith material compared to the mid-latitude craters. In a word, the proposed method is useful for estimating the dielectric properties of lunar regolith, and it is promising for future lunar SAR applications.

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 Recon-naissance 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 polari-metric data Barnes decomposition technique was applied which is based on wave dichotomy. Eigenvector and Eigenvalue-based decom-position 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 com-pact/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.(c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

Ground-penetrating radar (GPR) is a convenient geophysical technique for active-layer soil moisture detection in permafrost regions, which is theoretically based on the petrophysical relationship between soil moisture (theta) and the soil dielectric constant (epsilon). The theta-epsilon relationship varies with soil type and thus must be calibrated for a specific region or soil type. At present, there is lack of such a relationship for active-layer soil moisture estimation for the Qinghai-Tibet plateau permafrost regions. In this paper, we utilize the Complex Refractive Index Model to establish such a calibration equation that is suitable for active-layer soil moisture estimation with GPR velocity. Based on the relationship between liquid water, temperature, and salinity, the soil water dielectric constant was determined, which varied from 84 to 88, with an average value of 86 within the active layer for our research regions. Based on the calculated soil-water dielectric constant variation range, and the exponent value range within the Complex Refractive Index Model, the exponent value was determined as 0.26 with our field-investigated active-layer soil moisture and dielectric data set. By neglecting the influence of the soil matrix dielectric constant and soil porosity variations on soil moisture estimation at the regional scale, a simple active-layer soil moisture calibration curve, named CRIM, which is suitable for the Qinghai-Tibet plateau permafrost regions, was established. The main shortage of the CRIM calibration equation is that its calculated soil-moisture error will gradually increase with a decreasing GPR velocity and an increasing GPR velocity interpretation error. To avoid this shortage, a direct linear fitting calibration equation, named as upsilon-fitting, was acquired based on the statistical relationship between the active-layer soil moisture and GPR velocity with our field-investigated data set. When the GPR velocity interpretation error is within +/- 0.004 m/ns, the maximum moisture error calculated by CRIM is within 0.08 m(3)/m(3). While when the GPR velocity interpretation error is larger than +/- 0.004 m/ns, a piecewise formula calculation method, combined with the upsilon-fitting equation when the GPR velocity is lower than 0.07 m/ns and the CRIM equation when the GPR velocity is larger than 0.07 m/ns, was recommended for the active-layer moisture estimation with GPR detection in the Qinghai-Tibet plateau permafrost regions.

Currently, the community lacks capabilities to assess and monitor landscape scale permafrost active layer dynamics over large extents. To address this need, we developed a concept of a remote sensing based Soil Inversion Model for regional Permafrost (SIM-P) monitoring. The current SIM-P framework includes a satellite-based soil process model and a soil dielectric model. We are also working on incorporating a radar scattering model for Arctic tundra into the SIM-P framework. A unified soil parameterization scheme was developed to harmonize key soil thermal, hydraulic and dielectric parameters in the soil process and radar models that can be used in the joint soil-radar inversion framework. The soil parameter retrievals of the SIM-P framework include soil organic content (SOC) and active layer thickness (ALT). Initial tests of SIM-P using in-situ soil permittivity observations showed reasonable accuracy in predicting site-level SOC and soil temperature profiles at an Alaska tundra site and ALT in Arctic Alaska. SIM-P will be further tested using airborne P- and L-band radar data collected during NASA's Arctic Boreal Vulnerability Experiment (ABoVE) to evaluate the sensitivity of longwave radar to active layer properties.

There have been many investigations regarding water-ice depositions on the lunar surface and it is always been challenging. The previous studies were based on the circular polarization ratio (CPR). However, the CPR has proved to be inefficient in making distinctive classification of smooth (water-ice) and rough surface. Therefore, instead of using single polarimetric parameter CPR, it is required to analyze the CPR>1, along with other significant physical and electrical properties for better textural classification. In this paper, we have established the relationship between icy region and rough region based on physical property that is surface roughness measured with the help of fractal dimension method ('D') and electrical properties like real part of dielectric constant (epsilon'), imaginary part of dielectric constant (epsilon''), real (n) and imaginary (k) part of refractive index, skin depth (d) and reflectivity (R). The whole investigation indicates that the textural classification of the lunar surface with the help of physical and electrical properties gives superior results as compared to the single polarimetric parameter CPR.

Interest in the Moon started to increase at the beginning of the 21st century, and henceforth, more and more attention has been paid to the content and distribution of water ice in the lunar polar regions. The existence of water or ice in the regolith can apparently change its dielectric features. Therefore, in this article, the Dobson model is adopted and improved according to the Moon's environmental features, to construct the relationship between the volumetric water ice content and the dielectric constant. Thereafter, a lunar regolith dielectric distribution map is generated based on the improved Dobson model and the Clementine UVVIS data. The map indicates that the imaginary part of the dielectric constants in the lunar mare is much higher than that in the highlands. However, the maximum dielectric constants occur at the north- and south-pole regions, whose values are apparently bigger than those in the middle and low latitudes. Then, an abnormal map of the dielectric constant is gained if the threshold is put as 0.053 7, which is the highest value in the middle and low latitudes. The statistical results indicate that the number of abnormal pixels is 110 596, and the average is about 0.057 9. Assuming that the mean dielectric constant in the lunar mare is the normal dielectric constant at the south and north poles and E > (1)=11.58+i0.057 9 is the abnormal one, the volumetric water ice content can be evaluated using the advanced Dobson model. The results show that the average volumetric water ice content is about 1.64%, and the total area is about 25 294 km(2), where 10 956 km(2) belongs to the north pole and the rest is in the south pole.

The existence, formation and content of water ice in the lunar permanently shaded region is one of the important questions for the current Moon study. On October 9, 2009, the LCROSS mission spacecraft impacted the Moon, and the initial result verified the existence of water on the Moon. But the study on formation and content of water ice is still under debate. The existence of water ice can change the dielectric constants of the lunar regolith, and a microwave radiometer is most sensitive to the dielectric parameters. Based on this, in this paper, the radiation transfer model is improved according to the simulation results in high frequency. Then the mixture dielectric constant models, including Odelevsky model, Wagner and landau-Lifshitz model, Clau-sius model, Gruggeman-Hanai model, etc., are analyzed and compared. The analyzing results indicate that the biggest difference occurs between Lichtenecker model and the improved Dobson model. The values estimated by refractive model are the second biggest in all the models. And the results from Odelevsky model, strong fluctuation model, Wagner and Landau -Lifshitz model, Clausius model and Bruggeman-Hanai model are very near to each other. Thereafter, the relation between volume water ice content and microwave brightness temperature is constructed with Odelevsky mixing dielectric model and the improved radiative transfer simulation, and the volume water ice content in Cabeus crater is retrieved with the data from microwave radiometer onboard Chang'e-1 satellite. The results present that the improved radiative transfer model is proper for the brightness temperature simulation of the one infinite regolith layer in high frequency. The brightness temperature in Cabeus crater is 69.93 K (37 GHz), and the corresponding volume water ice content is about 2.8%.