The Chinese Chang’E-4 mission for moon exploration has been successfully completed. The Chang’E-4 probe achieved the first-ever soft landing on the floor of Von Kármán crater (177.59°E, 45.46°S) of the South Pole-Aitken (SPA) basin on January 3, 2019. Yutu-2 rover is mounted with several scientific instruments including a lunar penetrating radar (LPR), which is an effective instrument to detect the lunar subsurface structure. During the interpretation of LPR data, subsurface velocity of electromagnetic waves is a vital parameter necessary for stratigraphic division and computing other properties. However, the methods in previous research on Chang’E-3 cannot perform velocity analysis automatically and objectively. In this paper, the 3D velocity spectrum is applied to property analysis of LPR data from Chang’E-4. The result shows that 3D velocity spectrum can automatically search for hyperbolas; the maximum value at velocity axis with a soft threshold function can provide the horizontal position, two-way reflected time and velocity of each hyperbola; the average maximum relative error of velocity is estimated to be 7.99%. Based on the estimated velocities of 30 hyperbolas, the structures of subsurface properties are obtained, including velocity, relative permittivity, density, and content of FeO and TiO₂.

For water dynamics investigation in unsaturated (vadose) zones, ground penetrating radar is a popular hydro-geophysical method because it is non-invasive for soil, has high resolution and the results have a direct link with water content. Soil water content and soil hydraulic properties are two key factors for describing the water dynamics in vadose zones. There has been tremendous progress in soil water content and soil hydraulic properties estimation with ground penetrating radar. The purpose of this paper is to provide an overview of the application of ground penetrating radar for soil water dynamics studies. This paper first summarizes various methods for the determination of soil water content. including traditional methods in the surveys of surface ground penetrating radar, borehole ground penetrating radar, and off-ground ground penetrating radar, as well as relatively new methods, such as full waveform inversion, the average envelope amplitude method, and the frequency shift method. This paper further provides a review for estimating soil hydraulic properties with GPR according to the types of ground penetrating radar data. We hope that this review can provide a reference for the application of ground penetrating radar in soil water dynamics studies in the future.

As one of the main payloads mounted on the Yutu-2 rover of Chang’E-4 probe, lunar penetrating radar (LPR) aims to map the subsurface structure in the Von Kármán crater. The field LPR data are generally masked by clutters and noises of large quantities. To solve the noise interference, dozens of filtering methods have been applied to LPR data. However, these methods have their limitations, so noise suppression is still a tough issue worth studying. In this article, the denoising convolutional neural network (CNN) framework is applied to the noise suppression and weak signal extraction of 500 MHz LPR data. The results verify that the low-frequency clutters embedded in the LPR data mainly came from the instrument system of the Yutu rover. Besides, compared with the classic band-pass filter and the mean filter, the CNN filter has better performance when dealing with noise interference and weak signal extraction; compared with Kirchhoff migration, it can provide original high-quality radargram with diffraction information. Based on the high-quality radargram provided by the CNN filter, the subsurface sandwich structure is revealed and the weak signals from three sub-layers within the paleo-regolith are extracted.

Full-polarimetric ground penetrating radar (FP-GPR) can measure the ability of an object to change the polarization of electromagnetic waves. Compared to the traditional GPR, it has a stronger capability to identify underground objects. In recent years, a series of polarization decomposition methods have been applied to the FP-GPR data processing to obtain the polarimetric attributes and enhance the capability of targets identification. Different polarimetric attributes characterize different features of a target but there is still no effective way to integrate these attributes and take their respective advantages for target classification. In this article, we propose a particle center AdaBoost (PCAD) method and achieve the multiparameter adaptive target identification. The experimental results indicate that the PCAD method can automatically select suitable parameters during the training process for different targets. Compared to the single-parameter classification and the AdaBoost methods based on the traditional average and Bagging method, the PCAD method presents higher correct rates in classification. Finally, the proposed method is applied to landmine detection. The results demonstrate that the landmine is a composite scatterer that can generate surface scattering signals on its surface and dipole and volume scattering signals from its interior; based on the color-coded two-dimensional image by PCAD, we can distinguish landmines from other targets.

The full-polarimetric ground-penetrating radar (FP-GPR) can obtain the polarimetric attributes of targets and achieve more accurate identification compared with traditional GPRs. However, in most cases, the polarimetric signals collected by GPRs are not only from the targets but also from the ground surface. According to the classical Fresnel formulas, the polarized directions of the waves will change after transmissions due to the difference in the transmission coefficients between horizontally and vertically polarized waves. This effect, called induced field rotation (IFR), will interfere with the acquisition of polarimetric attributes and also exists in the field measurements on a rough surface. In this study, we have derived the association between the measured FP-GPR data and transmission coefficients of the rough surface provided by the small perturbation method (SPM). The effects of IFRs from the rough surface on H-Alpha decomposition are analyzed later. The results show that the parameters of the rough surface will affect the values of components in the scattering matrix, but will not change the matrix and H-Alpha decomposition result; the incident angle and relative permittivity play main roles; for the H-Alpha decomposition results of three typical targets, the flat and dihedral are little influenced by IFRs, but the cylinder is seriously affected; simultaneously, a template for discriminating whether the calibration for H-Alpha decomposition is necessary is established. Both numerical and experimental tests validate the conclusions. Finally, a novel application strategy of H-Alpha decomposition is proposed, which has taken IFR from the rough surface into considerations.

China’s first Mars probe Tianwen-1 has successfully landed on the southern Utopia Planitia of Mars on May 15, 2021. The Zhurong rover is first equipped with a full-polarimetric Mars Rover Penetrating Radar (FP-RoPeR) system, aiming to map the subsurface fine structure and to find the potential underground water ice. However, different from the previous water ice detection of orbital radar, the FP-RoPeR signals will be affected by the induced field rotation (IFR) if electromagnetic (EM) waves propagate through rough interfaces. Therefore, in this article, we assess the IFR effects from rough interfaces on the circular polarization ratio (CPR) response of FP-RoPeR data, which is a significant parameter for water ice detection. The theoretical computation and numerical validation indicate that the depth, the number of rough interfaces, and relative permittivity are three vital parameters that affect the IFR effects; depth plays a more important role than the other two for FP-RoPeR system. The CPR estimation result will be with greater error in the shallow region (0–1 m). The relative error in the region of depth greater than 1 m can be guaranteed to be under 10%.