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.

Soil heavy metal pollution caused by mining in mining areas seriously affects crop yield and causes human diseases. It is necessary to prevent soil heavy metal pollution from damaging health. Hyperspectral remote sensing can rapidly and dynamically acquire continuous spectra signals of ground objects, which provides a new idea for developing soil heavy metal content monitoring based on remote sensing. Aiming at the typical lead-zinc mining area in Laiyuan County, Hebei Province, soil samples from the mining area and surrounding areas are collected on-site, and the reflectance spectra of soil were obtained using SVC HR-1024i spectrometer (350 similar to 2 500 nm). Through the spectral data smoothing, first derivative (FD), multivariate scattering correction (MSC), standard normal variate transform (SNV), first derivative after multivariate scattering correction (MSC+FD), and first derivative after standard normal variatetrans form (SNV+FD), six kinds of spectral transformations were performed. The difference index (DI), ratioindex (RI), and normalized difference index (NDI) methods were used to extract the The optimal independent variables for different heavy metal elements were selected to increase the practical features of inversion modeling. Random forest algorithm and partial least squares regression method were used to establish prediction models for three heavy metals: cadmium (Cd), lead (Pb) and zinc (Zn) in soil. (Pb), and zinc (Zn). The R-2 of the optimal models reached 0.90, 0.91, and 0.84, respectively, which confirmed the validity of this research method. This study can provide a basis for the inversion modeling of soil heavy metal content in lead-zinc mining areas and a method reference for detecting soil heavy metal content in mining areas.