The Martian polar layered deposits (PLD), composed primarily of water ice with a small fraction of dust, form the upper structure of Mars’ northern plains. The thickness and dust content of these deposits are crucial for understanding Martian climatic cycles and historical evolution. The SHAllow RADar (SHARAD) instrument provides high-resolution subsurface data. However, its vertical resolution (~8.4 m in ice, ε'ice=3.15) may exceeds the thicknesses of individual layer in Martian PLD, complicating quantitative analysis. Radar echoes from thin dusty ice layers are influenced by multiple reflections between layer boundaries, leading to nonunique solutions for dust content and thickness. To address this, we propose a composite inversion model, CNN-BiGRU, which integrates a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BiGRU) for inversion of layer thickness and dust content. We designed multiple base models to leverage hierarchical stratigraphic features and lateral continuity in the North Polar Layered Deposits (NPLD). Training on simulated data generated via the Maxwell-Garnett and multi-reflection models reduced average relative errors by 41.02% (thickness) and 7.13% (dust content). Application to SHARAD data yielded an average dust content of 24% and layer thicknesses of 4–7 m, consistent with prior observations. These results suggest the reflective layer were formed approximately 18,000 years ago.

Archaeology and Cultural Heritage play a crucial role in fostering the diversity and sustainable development of human culture. Remote sensing data provides valuable insights into underground sites. In this paper, an optical image is employed to classify land cover and extract the Region of Interest (ROI) mask for focused analysis. Moreover, the underground structures are calculated by combining Synthetic Aperture Radar (SAR) data with the Two-Scale Model (TSM). The TSM aids in extracting ground scattering factors while mitigating surface clutter interference. Validation of this method in the Lagash region of southern Iraq demonstrates alignment with known underground structures, as corroborated by published literature.

Rainfall changes the geometric undulation of wind-driven sea surface, thereby altering the traditional relationship between wind vector and backscattering coefficient, which poses some challenges for microwave remote sensing measurement and inversion. This article proposes a new sea spectrum model based on the wind-driven Apel spectrum combining some correction factors of rain-generated ring waves, rain-induced damping, and rain-induced roughness. Then, this new spectrum is used to compute the backscattering coefficients using a two-scale method (TSM) with approximate cutoff wavenumber in the Ku-band. Comparison with the observational data of HY-2B scatterometer indicates that the simulated results after rainfall correction are more consistent with satellite observation. The simulation error for vertical polarization decreases from 2.574 to 1.9200 dB, while the error for horizontal polarization decreases from 3.2965 to 2.8153 dB. In addition, the comparison with the empirical geophysical model function (GMF) models also confirmed the accuracy and reliability of the new spectrum of rain-impacted sea surface.

Dust particles in Martian atmosphere can significantly reduce visibility. This article proposes a physically guided neural network approach for dust removal of Martian images by incorporating atmospheric scattering model into the cycle-consistent generative adversarial network (Cycle-GAN) framework. The network consists of two primary modules: dust removal and dust addition, both of which combine neural networks and physical models. The dust-removal process estimates the scattering coefficient and transmission map and then physically implements dust removal with the atmospheric scattering model. The dust-addition process estimates the scene depth, which is then combined with the atmospheric scattering coefficients obtained by the dust-removal process to compute the transmission map. The transmission map is substituted into the atmospheric scattering model for dust addition of clean images. Moreover, the additional loss of transmission map and scattering coefficient furtherly enhances the consistency constraints. Martian clear and dust images collected by the Mars Curiosity rover are used to train and evaluate the new dust-removal approach. Extensive ablation experiments demonstrate the effectiveness of incorporating the physical model and the additional loss functions. Furthermore, the scattering coefficients learned by the network are validated with the Mie scattering theory, ensuring the physical plausibility of the estimated parameters. The key advantage of this approach is that it does not require paired images of the same scene with or without dust, which is often a limiting factor for supervised dust-removal techniques. By incorporating the physical scattering model into the Cycle-GAN framework, the network can learn to restore clear images from dust-affected ones in a more realistic and consistent manner.

On 15 May 2021, Zhu Rong Rover successful landed on the southern area of the flat Utopia Plain. The subsurface penetrating radar is used for the exploration of Mars regolith thickness and subsurface shallow geological structures. In past eight months, the rover traveled about 950 meters, and the subsurface penetrating radar has collected a large amount of echo data. This paper presents a set of process methods to analyze the high-frequency channel data. First, the predictive deconvolution algorithm is used to eliminate multiple reflections tailing of radar wavelet. Then the wavelet transform is used to remove the random noise, and the two-state SVD decomposition and reconstruction are used to remove the direct wave of the surface and suppress the fusion noise. After these processing, the signal-to-noise ratio of echo signal and data resolution is improved. The processed B-scan images in some areas show abnormal signal within 40ns. Suppose the relative permittivity of Mars' soil is 3.5, it is corresponding to the abnormal structure within 3m depth. Combined with the optical image of the navigation and terrain cameras, we infer that these abnormal structures may be the buried stones or cavities.

It is important to detect the subsurface structure of Mars by low-frequency synthetic aperture radar (SAR). Most researches of the subsurface structure of Mars, especially the underground water bearing minerals, were focused on the south polar layered deposits. This paper studies the subsurface structure of Tenuis Mensa (a flat-topped prominence with cliff-like edges in Mars north pole). We analyzed the radar echo of this region detected by the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) from April 2008 to March 2018. After ionospheric compensation for these echoes, we found a subsurface structure centered at longitude 265.04 and latitude 83.18, with a diameter of about 30km. The reflected signal of underground stratification can be seen by observing the range echoes in these areas.

When detecting the underground structure of Mars using an orbital subsurface investigation radar, it is essential to compensate for the ionosphere distortion of the received echo. The existing algorithms, such as the contrast method (CM) and the phase-gradient-autofocus (PGA) algorithm, can mainly compensate for the defocus of the echo, but it is difficult to compensate for the group delay at the same time, resulting in the deviation between the echo position and the terrain profile. This article analyzes the interference of Martian ionosphere to the echo of subsurface investigation radar and proposes a dual-band difference (DBD) ionosphere compensation algorithm. First, the group delay model of radar echo under ionospheric interference is established. Then, the equivalent plasma frequency of the ionosphere is calculated according to the group delay difference of echoes in two bands, and the Taylor coefficients of the phase error are calculated. Finally, the echoes are accurately compensated with the Taylor series model of phase shift. This algorithm can correct the defocus and sidelobe interference and simultaneously compensate for the group delay, which is greatly significant to accurately locate the subsurface structure of Mars. The experiments on the simulation data and the echo of the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) show the feasibility of this method. Compared with the traditional way, this algorithm is suitable for both high- and low-frequency situations, and the high computational efficiency without iteration makes on-orbit real-time ionosphere compensation possible. Moreover, with the effective correction of topographic offset, the total electron content (TEC) can also be accurately retrieved.

This article presents a new approach to analytically compute high-frequency (HF) electromagnetic (EM) scattering from an arbitrary undulating rough surface according to the statistical characteristics of its geometric fluctuation. The integral expression of the scattering coefficient is derived from Kirchhoff approximation (KA) and extended to arbitrary spectra. Two methods are presented to calculate the integral: 1) the discrete Fourier transforming method based on the autocorrelation function (ACF) (KAC) and 2) the convolution method based on the power spectrum density function (KAW). For some analytically expressed spectra, the scattering coefficients can be calculated directly by the KAW and KAC methods according to its power spectrum density (PSD). The analytical expressions of classical KA for Gaussian and exponential spectrum are used to validate the proposed methods. For the case of unknown undulation height spectra of rough surfaces, the ACF is statistically calculated first, and then the KAC and KAW methods are used to calculate the average scattering coefficients numerically. Moreover, the numerical results show that the KAW method takes up more memory to retain enough energy of the ACF for accurate scattering coefficients. The two new methods are effective in the range of θifrom 0° to 45° and have broad application prospects for real scenarios.

Based on the probability distribution function of the stones, the model of the stones is established using the inverse function method of Monte Carlo method. Then the full polarization radar echo is calculated according to the Maxwell integration equations, using method of moment accelerated with vector SMCG algorithm. Then the circular polarization ratio is calculated according to Mini-RF radar observation configuration. The simulation results show that random stones distribution causes surface roughness and backscattering echo enhancement. Moreover, the multiple scattering of stones can cause anomalous CPR in radar echoes, especially for the stones with size larger than wavelength.

Interferometric synthetic aperture radar (InSAR) uses phase difference of radar echoes, either from multiple passes along the same trajectory or from multiple displaced phase centers on a single pass, to generate interferogram. Scattering correlation in the angular dimension is a critical factor determining the quality of InSAR interferogram. It can be modeled with the angular correlation function (ACF). In this letter, the ACF of a 1-D rough surface under incidence of a tapered wave, namely, a pencil beam, is studied numerically for correlation analysis of InSAR. An analytic ACF is first derived based on the first-order small perturbation method. It is then validated statistically by the method of moment of electromagnetic scattering. Analysis of the ACF simulations indicate that the ACF of backscattering from a randomly rough surface exhibits a shape of sinc function, which depends on tapering parameter g, interferometric incidence angles θ1and θ2. Several numerical simulations of different rough surface spectrums demonstrate that the analytical ACF fits well with numerical results as long as g is the larger several correlation lengths l.

Dielectric inversion of lunar permanently shadowed region (PSR) of moon poles has been studied for estimation of possible water-ice content. The Campbell model was directly applied to mini-SAR data for inversion on the Hermite-A crater region. However, this letter presents quantitative analysis that the lunar surface topography, i.e., surface roughness and slopes, and underlying dielectric media, and so on, can significantly affect this inversion. The model is actually degenerated into a half-space model without topographic account. This letter presents a two-layer model of Kirchhoff-approximation surface/small perturbation approximation subsurface to take account of all these topographic factors for PSR dielectric inversion.

The main objective of the Chinese Chang'E-3 (CE-3) lunar satellite is to achieve soft-landing and roving exploration on the lunar surface. A multibeam radar in the lunar lander is implemented to measure the echoes from the lunar rough surface during its descending and to derive the speed of the lander. In this paper, numerical simulation of multiangular radar echoes and speed inversions from Doppler frequency are presented. An area of the Lunar Sinus Iridum bay, as landing site, is specifically selected. The rough surface described with the real DTM data is first divided into triangular patches for numerical Kirchhoff approximation calculation. The radar echoes of multiangular radar beams of CE-3 during the landing are numerically simulated. The echo phase and the Doppler frequency are then derived to obtain the vertical speed.

This paper presents a hybrid iterative algorithm of analytic Kirchhoff Approximation (KA) and numerical method of moment (MoM) for scattering computation from a three-dimensional (3D) perfect conducting target above a randomly rough surface. The coupling integral equations (IEs) are derived based on the Green’s function and the boundary conditions. The MoM with the Conjugate Gradient (CG) approach is used to solve the target’s IE, and the KA is applied to scattering from the rough surface. The coupling iteration takes account the interactions between the target and the underlying rough surface. Convergence of the hybrid KA-MoM algorithm is numerically validated. Since is only one numerical integral of induced current on the target performed by KA computation, much memory and computation time is reduced. Bistatic scattering from a PEC cubic or spheroid target above a Gaussian rough surface are numerically simulated.

A hybrid algorithm, combining analytic Kirchhoff approximation (KA) and numerical method of moments (MoMs), is developed to solve the coupling electric-field integral equations (EFIEs) of scattering from a perfect electric conducting (PEC) object above a randomly rough PEC surface under TE-polarized plane-wave incidence. The MoM with the conjugate gradient approach is used to first solve the EFIE of the object. The surface fields on the rough surface are analytically expressed using the KA method, and large memory and computations for those fields are greatly reduced. An iterative approach of the surface fields induced on both object and rough surface is then utilized to take into account interactions between the object and underlying rough surface. Convergence of this hybrid algorithm is numerically validated. Making use of Monte Carlo realization, bistatic scattering from a 2-D PEC cylindrical object above a PEC rough surface is well simulated by this hybrid KA-MoM algorithm

The difference field RCS (d-RCS) has been defined to analyze the scattering from the target above a rough surface. The electric field integral equations (EFIEs) of the difference induced current on the rough surface, the induced electric current and magnetic current on the dielectric target under a TE wave incidence are derived. A small portion of the rough surface towards the target along the specular direction is taken to compute the scattering contribution from the rough surface towards the target, which improves the computation speed. A numerical iterative approach is developed to solve the EFIEs and bistatic d-RCS. The surface length for iterations is dependent on the scattering angle and discussed for comparison with Johnson's method. Using the Monte-Carlo method to generate the P-M (Pierson-Morkowitz) ocean-like rough surface, bistatic d-RCS of the dielectric target, e.g. a cylinder or a square column, above the rough surface is numerically simulated. The induced electric and magnetic currents on the dielectric target, and the difference induced current on the rough surface are numerically discussed.

The difference field radar cross section (d-RCS) has been defined to analyze the scattering from the target above a rough surface, which takes account of scattering from the target and multiinteractions of the target and underlying rough surface. The d-RCS removes the effect of the finite illuminated surface length under the tapered wave incidence. In this paper, the electric field integral equations (EFIEs) of the difference-induced current J/sub sd/ on the rough surface and the induced current J/sub o/ on the target are derived. A small section of rough surface toward the target in the specular direction is taken to speed up computation of the scattering contribution E/sub s0/ from the moderate rough surface to the target. Then, an iterative approach is developed to solve the EFIEs of the induced currents, directly, and yields the bistatic d-RCS. A finite rough surface length for numerical iteration is taken, corresponding to the dependence on the maximum scattering angle. Using the Monte Carlo method to generate rough surface, the bistatic d-RCS of the target, e.g., a cylinder or a square column, above a Pierson-Morkowitz rough surface is numerically simulated. The induced currents on the target and the d-RCS are discussed, and compared with the case of the target in free-space.