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.

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.

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.

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.

The characterization of the lunar surface and subsurface through the utilization of synthetic aperture radar data has assumed a pivotal role in the domain of lunar exploration science. This investigation concentrated on the polarimetric analysis aimed at identifying water ice within a specific crater, designated Erlanger, located at the lunar north pole, which is fundamentally a region that is perpetually shaded from solar illumination. The area that is perpetually shaded on the moon is defined as that region that is never exposed to sunlight due to the moon's slightly tilted rotational axis. These permanently shaded regions serve as cold traps for water molecules. To ascertain the presence of water ice within the designated study area, we conducted an analysis of two datasets from the Chandrayaan mission: Mini-SAR data from Chandrayaan-1 and Dual-Frequency Synthetic Aperture Radar (DFSAR) data from Chandrayaan-2. The polarimetric analysis of the Erlanger Crater, located in a permanently shadowed region of the lunar north pole, utilizes data from the Dual-Frequency Synthetic Aperture Radar (DFSAR) and the Mini-SAR. This study focuses exclusively on the L-band DFSAR data due to the unavailability of S-band data for the Erlanger Crater. The crater, identified by the PSR ID NP_869610_0287570, is of particular interest for its potential water ice deposits. The analysis employs three decomposition models-m-delta, m-chi, and m-alpha-derived from the Mini-SAR data, along with the H-A-Alpha model known as an Eigenvector and Eigenvalue model, applied to the DFSAR data. The H-A-Alpha helps in assessing the entropy and anisotropy of the lunar surface. The results reveal a correlation between the hybrid polarimetric models (m-delta, m-chi, and m-alpha) and fully polarimetric parameters (entropy, anisotropy, and alpha), suggesting that volume scattering predominates inside the crater walls, while surface and double bounce scattering are more prevalent in the right side of the crater wall and surrounding areas. Additionally, the analysis of the circular polarization ratio (CPR) from both datasets suggests the presence of water ice within and around the crater, as values greater than 1 were observed. This finding aligns with other studies indicating that the high CPR values are indicative of ice deposits in the lunar polar regions. The polarimetric analysis of the Erlanger Crater contributes to the understanding of lunar polar regions and highlights the potential for future exploration and resource utilization on the Moon.

The transient electromagnetic method (TEM) can capture an induced polarization (IP) signature of subsurface ice. Using numerical modeling of a horizontally layered earth, we investigate how IP in TEM can be exploited for subsurface ice detection on Earth, Mars, and the Moon. In the model we implement electrical parameters from laboratory measurements of ice, planetary regolith simulants, and terrestrial soil from the literature. In contrast to currently applied forward models, we include two Cole-Cole relaxation terms to model the dielectric relaxation of adsorbed water or salt hydrate in addition to the relaxation of ice. On Earth, IP signals of shallow layers of silt mixed with 44-100 vol% ice embedded in resistive host layers of 3 k Omega m can be detected. Both at mid (45 degrees N) and lower (35 degrees N) latitudes on Mars, meter thick layers of massive ice can be detected at 10 m depth if the ice contains salts. Corresponding layers of 60 vol% ice mixed with Martian regolith simulant show similar detectability. For IP signals of lunar ice to be detected in ice volume fractions of 7.4%-46%, a development in TEM technology is required, including mitigation of early time interference, or enhancing the signal to noise level.

When a long distance HVDC transmission system discharges current into the earth through its grounding electrode, ground potential differences appear in a large area. And therefore part of the DC current may flow into nearby pipelines which may be dangerous to the equipment and personnel, and may aggravate corrosion. In this paper, an equivalent circuit based on the method of moments is introduced to calculate the current and potential distributions along a pipeline with damaged anticorrosive coating. The current-dependent electrochemical polarization potential between soil and the metal pipe, due to the damage of the anticorrosive coating, is taken into account by using the Newton-Raphson scheme. The circuit is verified through a reduced scale experiment. By examining the circuit, the effect of the damaged anticorrosive coating on the leakage current and the pipe potential with respect to soil along the pipeline is analyzed.

The temporal variability of microphysical parameters of pyrolysis smoke, retrieved by inverting the characteristics of aerosol scattering and extinction, has been studied. The polarization scattering phase functions and spectral extinction coefficients were measured for 65 hours in smoke aerosols produced from thermal decomposition of pine wood during low-temperature pyrolysis in the Big Aerosol Chamber (BAC) of Institute of Atmospheric Optics, Siberian Branch, Russian Academy of Sciences. The microstructure parameters (volume concentration and mean radius of particles with division into fine and coarse fractions) and the complex refractive index of pyrolysis smoke are retrieved following the developed algorithm for inverting optical measurements. The real part of the refractive index is found to be in the vicinity of n = 1.55, and the imaginary part is in the range 0.007 < kappa < 0.009; the mean radius of fine particles varies in the narrow range 0.137-0.146 mu m. During smoke aging, the particle ensemble-mean radius monotonically increased from 0.19 to 0.6 mu m mainly due to a relative increase in the content of coarse aerosol. Results of this work are important for estimation of the radiative forcing of aerosol and improvement of climate models and algorithms of remote optical sounding.

Understanding the reachability of water ice by future in-situ experiments near the lunar poles is crucial for supporting growing exploration plans and constraining the uncertainties on its genesis and distribution. To achieve this objective, we perform a thorough three-dimensional mapping of the distribution of water ice in the lunar polar regions (70 degrees onward), integrating radar, optical, and neutron detector observations from the Lunar Reconnaissance Orbiter mission (LRO). Our analysis reveals similar to 5-to-8-fold larger expanse of subsurface water ice (similar to 1-3 m depth) compared to surface water ice (up to 1 m depth) for the north and south poles, respectively. Our investigation cannot rule out the possibility of deep-seated water ice deposits in the lunar poles that remains beyond the detection capabilities of existing instruments on LRO. Moreover, we find that the extent of water ice in the northern polar region (similar to 1100 +/- 74 km(2)) is twice that in the southern polar region (similar to 562 +/- 54 km(2)). Our mapping also suggests that the dichotomous latitudinal distribution and the antipodal longitudinal distribution of water ice are likely driven by Mare volcanism and preferential cratering. We provide additional evidence that outgassing during Imbrian volcanism was probably the primary source of subsurface water ice in the lunar poles, which favors larger expanse over meteoritic sources.

High circular polarization ratio (CPR) characteristics were found in permanently shaded regions (PSRs) near the lunar poles. High CPR was regarded as a water ice index. The compact-polarimetric (CP) miniature radio frequency (Mini-RF) radar transmits left-circularly polarized signals and receives horizontally polarized ($S_{\text {HL}}$ ) and vertically-polarized ($S_{\text {VL}}$ ) echoes from the lunar surface. Statistics of the CPR data show its relations with the relative phase ($\delta$ ) between $S_{\text {HL}} $ and $S_{\text {VL}} $ and the degree of polarization ($m$ ) but few interpretations were provided. The average CPR data reach the maximum and minimum at $\delta =\pm 90{\circ }$ , respectively. As $m$ becomes very small, the CPR approaches 1. It has been found that CPR is also affected by surface roughness and incidence angle of radar waves. The CPR is now expressed in CP mode to explain the Mini-RF observation. Full-polarimetric radar echoes and CP parameters of the lunar surface are numerically simulated using the bidirectional analytic ray-tracing method. Single-bounce and multiple-bounce scattering components are included in the simulation. Radar images of the lunar crater are simulated with the digital elevation model (DEM) data. The $H-\alpha $ decomposition derived from the full-polarimetric simulation is presented to analyze $\delta $ and $m$ . Simulated radar images with different surface roughness are analyzed statistically to study the functional dependences of $\delta $ , ${m}$ , and CPR on incidence angle and roughness. Relationships among $\delta $ , $m$ , and CPR are used to analyze the effects of incidence angle, roughness, TiO2, and rock abundance on the scattering components. The CPR, $m$ , and $\delta $ of PSR craters of different ages are compared with those of nonpolar craters. The results indicate that the CPR, $m$ , and $\delta $ are unlikely to be unambiguous evidence of water ice.