The present study performed classification global aerosols based on particle linear depolarization ratio (PLDR) and single scattering albedo (SSA) provided from AErosol RObotic NETwork (AERONET) Version 3.0 and Level 2.0 inversion products of 171 AERONET sites located in six continents. Current methodology could distinguish effectively between dust and non-dust aerosols using PLDR and SSA. These selected sites include dominant aerosol types such as, pure dust (PD), dust dominated mixture (DDM), pollution dominated mixture (PDM), very weakly absorbing (VWA), strongly absorbing (SA), moderately absorbing(MA), and weakly absorbing (WA). Biomass-burning aerosols which are associated with black carbon are assigned as combinations of WA, MA and SA. The key important findings show the sites in the Northern African region are predominantly influenced by PD, while south Asian sites are characterized by DDM as well as mixture of dust and pollution aerosols. Urban and industrialized regions located in Europe and North American sites are characterized by VWA, WA, and MA aerosols. Tropical regions, including South America, South-east-Asia and southern African sites which prone to forest and biomass-burning, are dominated by SA aerosols. The study further examined the impacts by radiative forcing for different aerosol types. Among the aerosol types, SA and VWA contribute with the highest (30.14 +/- 8.04 Wm-2) and lowest (7.83 +/- 4.12 Wm-2) atmospheric forcing, respectively. Consequently, atmospheric heating rates are found to be highest by SA (0.85 K day-1) and lowest by VWA aerosols (0.22 Kday-1). The current study provides a comprehensive report on aerosol optical, micro-physical and radiative properties for different aerosol types across six continents.

The paper presents new radar maps of the south polar region of the Moon at 4.2 cm wavelength with an average spatial resolution of 90 m. The maps are based on radar images obtained in 2023 using the 64-m TNA-1500 antenna of the Bear Lakes Satellite Communications Center of the Special Design Bureau of the Moscow Power Engineering Institute and the 13.2-m RT-13 radio telescopes at the Svetloe and Zelenchukskaya observatories of the Institute of Applied Astronomy of the Russian Academy of Sciences. Radar images are formed in a specific coordinate system relating the Doppler frequency shift with the propagation time delay of the echo components, which makes it difficult to tie them to selenographic coordinates. In this paper, an original method for converting echo Doppler frequency and time delay to selenographic latitude and longitude is proposed, using bilinear interpolation by ephemeris nodal values, taking into account long integration times. The accuracy of the reference of the maps constructed in this way was assessed and compared with the LROC WAC global optical map of the Moon and mosaics of permanently shadowed regions from LROC NAC. It is shown that radar maps at 4.2 cm wavelength contain features of the lunar surface that are hidden in optical images and are located in the regolith at depths of up to 1 m or in permanently shadowed regions of the south polar region of the Moon. The maps of the lunar echoes specular and diffuse polarization components, as well as a map of the distribution of circular polarization ratios, are available on the Internet at http://luna.iaaras.ru/ and can be useful for studying the geological history of the Moon, searching for ice deposits, and selecting safe landing sites when planning future lunar missions.

Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere. This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau, China, as well as in the related ecological and hydrological processes. However, the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques. Thus, this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected (GRD) data, the polarization decomposition parameters of Sentinel-1A single-look complex (SLC) data, the normalized difference vegetation index (NDVI) based on Sentinel-2B data, and the topographic factors based on digital elevation model (DEM) data. By combining these parameters with a machine learning model, we established a feature selection rule. A cumulative importance threshold was derived for feature variables, and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination (R2) and the unbiased root mean square error (ubRMSE). The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion, and the SHapley Additive exPlanations (SHAP) method was used to analyze the importance of these variables. The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion. Compared to the unfiltered model, the optimal feature combination led to a 0.09 increase in R2 and a 0.7% reduction in ubRMSE. Ultimately, the optimized model achieved a R2 of 0.87 and an ubRMSE of 5.6%. Analysis revealed that soil particle size had significant impact on soil water retention capacity. The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable, demonstrating a significant positive correlation. Moreover, the microtopographical features of hummocks interfered with soil moisture estimation, indicating that such terrain effects warrant increased attention in future studies within the permafrost regions. The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau, but also exhibits high computational efficiency (with a relative time reduction of 18.5%), striking an excellent balance between accuracy and efficiency. This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data, offering critical insights for ecological conservation, water resource management, and climate change adaptation on the Qinghai-Xizang Plateau.

Previous studies have reported the existence of water ice in the lunar polar regions, but estimations of water ice using different methods vary in certainty, precision, location, and abundance. Spectral analysis is one of the major ways for estimating lunar water ice abundance. However, low spatial resolution and signal-to-noise ratio are the disadvantages of hyperspectral images. In this study, the images captured by the multi-band imager (MI), characterized by higher spatial resolution and signal-to-noise ratio than hyperspectral images, onboard the Japanese Moon orbiter Selenological and Engineering Explorer (SELENE), are used to retrieve water ice in lunar polar regions. We analyzed reflectance in near-infrared bands after topographic correction to reduce the misinterpretation of the properties of the lunar surface. Through qualitative spectral analysis and quantitative water ice retrieval, the water ice abundance of sunlit areas in Shackleton Crater, de Gerlache Rims 1 and 2, Connecting Ridge, Connecting Ridge extension, and Peak Near Shackleton are obtained. The sunlit inner wall of Shackleton Crater has the highest possibility to contain water ice among the four regions, the estimated abundance ranges from 2 to 3 wt.%, which is consistent with previous studies in terms of order of magnitude. Reproducibility test suggests that the parallax effect of MI is small to ensure robust conclusions. When artificial noise was introduced, water ice abundance variations were limited to 1 wt.% in only a few areas, revealing that the results exhibit robustness against noise interference.

The precise detection of water-ice distributions within the permanently shadowed regions (PSRs) of the lunar south polar region is of paramount importance. We applied a polarimetric method for water-ice detection (PM4W) that utilizes Mini-RF data. The PM4W method incorporates several key radar scattering properties with topographical and environmental characteristics to detect water-ice within the lunar south polar region of 87 degrees S-90 degrees S. The method successfully identified 1578 water-ice containing pixels (each representing a 30 m x 30 m area) in the lunar shallow subsurface (1-3 m) at the south polar region, of which 1445 (similar to 91%) are spatially clustered in 29 PSRs. When comparing Mini-RF with M3 (each point representing a 280 m x 280 m area) using a buffer-based fuzzy assessment method, we found a pixel consistency of 60% and area consistency of 11%, which can be attributed to the differences in spatial resolution, positioning accuracy, and depth sensitivity. Moreover, over 90% of the water-ice pixels detected by Mini-RF are located within PSRs, accounting for 0.025% of their total area. In contrast, only 68% of the pixels detected by M3 are within PSRs, covering 0.760% of the PSRs area, which is approximately 30 times greater than the Mini-RF detections. The finer spatial resolution of the Mini-RF enables it to reveal previously undetectable features that align with the environmental mechanisms of water-ice storage. Our work contributes to assessing the potential presence of water-ice in vital exploration areas, providing pertinent indications for future lunar probes to identify water-ice on the Moon directly.

This paper presents a novel micropolar-based hypoplastic model to reproduce the stress-strain relationship of face mask chips-sand mixtures (MSMs) and their localized deformation. Based on a critical state hypoplastic model, a non-polar hypoplastic model for MSMs is first developed with modifications and new features: (1) the cohesion induced by face mask chips is considered by introducing an additional stress tensor into the Cauchy stress tensor; (2) the initial stiffness variation in MSMs is described with a modified tangential modulus; and (3) the effective skeleton void ratio concept is introduced to capture the initial and critical void ratio variations in MSMs. The model is then extended to its micropolar terms by incorporating the micropolar theory, which includes an internal length parameter and a couple stress induced by particle rotation, with the advantage of overcoming the mesh dependency problem in the conventional finite element method (FEM) based simulations. Moreover, the new micropolar hypoplastic formulations are implemented into a FEM code. The onset and evolution of shear bands in MSMs are investigated by simulating a series of biaxial tests on both pure sand and MSMs. Numerical results are also compared to experimental observations, demonstrating that the developed micropolar hypoplastic model can adeptly capture the shear band propagation in MSMs and their mechanical responses.

Detection of water-ice deposits using synthetic aperture radar (SAR) is a cost-effective, and efficient approach to understand lunar water resources. As water is crucial to supporting human-based space exploration, current and near upcoming lunar missions are primary concentrated on mapping and quantification of water ice exposures on surface and subsurface levels. The circular polarization ratio greater than one (CPR >1) derived using the orbital radar observations is considered as an important SAR derived parameter for water-ice detection. This study aims to investigate 14 craters near the lunar poles with high CPR (CPR >1), as identified in previous studies, using the L-band (24 cm) dual frequency synthetic aperture radar (DFSAR) onboard Chandrayaan-2. In addition to CPR, we computed the degree of polarization (DOP) after applying parallax error correction that helps in reducing misinterpretation. Our findings are based on orthorectified DFSAR calibrated data analysis. We found that the CPR of crater interiors is not significantly different from that of their surroundings, and this pattern is consistent throughout all the 14 craters selected. Further, we also found a linear inverse relationship between CPR and DOP for the interior and exteriors of the craters, with R-2 0.99, indicating a strong correlation between these two parameters. We found only 2 % of total pixels are above CPR > 1, which indicates that there is less possibility of homogeneous water-ice but the possibility of water-ice mixed with the subsurface regolith cannot be ruled out.

The Moon, as the celestial body closest to Earth, is a prime target for human deep space exploration. China's Lunar Exploration Project IV aims to explore and sample the lunar polar region's water-containing regolith. To effectively simulate the characteristics of this watery lunar regolith, this paper proposes a deep low-temperature preparation system. The feasibility of the system design is validated through theoretical and methodological analysis, including cold source selection, heat dissipation analysis, and energy consumption calculations. Subsequently, a deep low-temperature aqueous lunar regolith preparation system was developed, and tests were conducted to verify its performance. The results confirm that the system can generate water-containing lunar regolith at -238 degrees C and maintain its temperature during drilling. This capability is significant for subsequent research on the drilling performance of deep low-temperature watery lunar regolith.

Water ice, extensively detected in the lunar south polar region, represents a valuable resource for future lunar base construction and energy utilization. To gain a comprehensive understanding of the origin, distribution, and properties of water ice in the lunar polar regions, on-site measurement is essential. In alignment with this goal, China's Chang'E 7 mission, scheduled for launch in 2026, aims to explore water ice within permanently shadowed regions of the lunar south pole through drilling and in-situ measurement of water content. This work presents the design and development of a thermal-vacuum regolith environment simulator, specifically created to test the performance of a robotic drill under conditions simulating the icy lunar regolith of the lunar polar environment. The simulator comprises a vacuum acquisition system, a cryogenic cooling system, and a preparation system for icy lunar regolith simulant. Additionally, the simulator can effectively adjust the position of the lunar regolith container and visually monitor it. The vacuum acquisition system provides a lowpressure environment suitable for drilling tests with icy lunar regolith simulant, while the cryogenic cooling system refrigerates the simulant to a temperature as low as 95 K (- 178 degrees C). The regolith preparation system, moreover, enables controlled mixing and compaction of regolith simulant to specific bulk densities and water contents. To enhance testing efficiency in simulated thermal-vacuum environments, the simulator includes a rotation mechanism that allows multiple drilling tests within a single environmental setup by adjusting the position of the regolith container. Experimental validation confirms the capacity of the simulator to replicate conditions similar to those in lunar polar regions. Specifically, the vacuum acquisition system can pump the chamber to a pressure in the order of 10 -1 Pa when loaded with icy lunar regolith simulant, and the cryogenic cooling system can refrigerate the regolith simulant with water contents of 0.5 wt% and 4 wt% to 95 K. This work can provide essential ground-testing support and technical validation for the upcoming drilling and sampling tasks of the Chinese Chang'E 7 mission.

Remote sensing plays an increasingly important role in agriculture, especially in monitoring the quality of agricultural crops. Optical sensing is often limited in Central Europe due to cloud cover; therefore, synthetic aperture radar data is increasingly being used. However, synthetic aperture radar data is limited by more difficult interpretation mainly due to the influence of speckles. For this reason, its use is often limited to larger territorial units and field blocks. The main aim of this study therefore was to verify the possibility of using satellite synthetic aperture radar images to assess the within-field variability of winter wheat. The lowest radar vegetation index values corresponded to the area of the lowest production potential and the greatest damage to the stand. Also for VH and VV polarizations, the highest values corresponded to the area of the lowest stand quality. Qualitative changes in the stand across the zones defined by frost damage and production potential were assessed with the help of the logistic regression model with resampled data for 10, 50, and 100 m pixel size. The best correlation coefficients were achieved at a spatial resolution of 50 m for both options. The F-score still yielded a promising result ranging from 0.588 to 0.634 for frost damage categories. The regression model of the production potential performed slightly better in terms of the F-score, recall, and precision at higher resolutions. It was proved that modern statistical methods could be used to reduce problems associated with speckles of radar images for practical purposes.