Objective Absorbing aerosols, particularly black carbon (BC), exerts significant influence on the Earth's radiation budget by modifying both the amount and vertical distribution of solar radiation. Their climatic effects are especially pronounced in regions characterized by concentrated fossil fuel activities, such as large-scale coal mining areas. However, the spatial and temporal variability of their microphysical and optical properties introduces considerable uncertainty into regional radiative forcing assessments. The Zhundong Coalfield, located in eastern Xinjiang, China, is one such region where BC emissions from coal extraction and associated industrial activity are persistent yet under-characterized from a radiative perspective. This study aims to construct a rapid estimation framework for aerosol radiative forcing (ARF) over this region by integrating multi-band satellite observations with physically based scattering and radiative transfer models. The primary goal is to evaluate how aerosol optical depth (AOD), single scattering albedo (SSA), and particle size influence shortwave ARF at the top of the atmosphere (TOA), bottom of the atmosphere (BOA), and within the atmospheric column (ATM), and how ultraviolet-band data enhances the reliability of this estimation. Methods The research adopts a modular approach comprising aerosol property inversion and radiative transfer modeling. The aerosol inversion is based on a Mie scattering model incorporating a core-shell structure assumption, where BC forms the absorbing core and is coated by non-absorbing substances such as sulfate and nitrate. Satellite-derived aerosol products are used to constrain the model: MODIS provides AOD and SSA at visible wavelengths, while OMI contributes ultraviolet (UV) -band SSA and AOD information. Two experimental configurations are established-one based solely on MODIS data, and another integrating both MODIS and OMI-to assess the role of UV spectral information in constraining aerosol characteristics. Following inversion, the retrieved aerosol size and optical parameters are used as input to the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) model to simulate instantaneous ARF at TOA, BOA, and ATM under clear-sky conditions. Radiative forcing is calculated as the difference in net shortwave flux with and without aerosols. Multiple linear regression models are then constructed using different combinations of AOD, SSA, and core radius to quantify the relationship between these parameters and simulated ARF. Regression performance is evaluated using R (2) and RMSE statistics across both single-source and combined-source scenarios. Results and Discussions First, the inclusion of OMI UV-band data significantly improves the inversion accuracy of aerosol particle size characteristics. When only MODIS data are used, the retrieved BC core sizes are relatively narrow, mostly centered around 120 nm, and the shell diameters exhibit limited variation. However, when OMI UV observations are incorporated, the core size distribution broadens, capturing particles ranging from 90 to 160 nm, while the shell diameter spans a wider interval of 300?700 nm. This improved resolution stems from the stronger sensitivity of UVs to absorption by fine-mode particles, which enhances the model's ability to distinguish subtle differences in particle morphology. The resulting total particle size distributions-core plus shell-are more consistent with reported field measurements in coal-intensive regions. These results confirm that UV data not only improve inversion detail but also reduce the uncertainty in the wavelength in the representation of aerosol mixing states. Second, the quantitative relationship between optical parameters and ARF demonstrates clear physical consistency across TOA, BOA, and ATM layers. In both MODIS-only and MODIS-OMI configurations, AOD exhibits a strong negative correlation with TOA and BOA radiative forcing (R=-0.77 and -0.78, respectively), indicating a cooling effect due to enhanced scattering and absorption of incoming solar radiation. SSA also shows a strong negative correlation with TOA and BOA forcing (R=-0.78 and -0.62, respectively), suggesting that as the aerosol becomes more scattering-dominant, its net radiative cooling effect intensifies. Conversely, AOD shows weaker but positive correlations with ATM forcing (R=0.43), suggesting an increase in atmospheric heating when aerosol loading or absorption increases. This pattern aligns with physical expectations: absorbing aerosols like BC trap energy in the atmosphere, contributing to vertical energy redistribution. The analysis confirms that SSA has a stronger explanatory power than AOD, emphasizing its role as a key driver of radiative uncertainty forcing. Third, regression model performance improves markedly with the inclusion of SSA and core size as input parameters. Under the MODIS-only scenario, models using AOD alone yield limited explanatory power, withR (2) values of 0.59 (TOA), 0.61 (BOA), and 0.18 (ATM). Adding SSA improves the fits substantially, increasingR (2) to 0.78 (TOA) and 0.67 (BOA), and to 0.21 in the ATM. Incorporating core radius into the model yields additional gains, raisingR (2) in the ATM layer to 0.23 and lowering RMSE values across all layers. In the MODIS-OMI fusion scenario, even though the number of valid observation days decreases significantly (eg, from 2589 to 954 days at the Wucaiwan site), model performance continues to improve. For example,R (2) for ATM forcing increases from 0.18 to 0.29, and RMSE decreases from 2.04 to 1.85. These results suggest that high-spectral-resolution UV data provide greater constraint on aerosol absorption properties, thereby enabling more physically consistent radiative forcing estimates, even with reduced samples. This finding supports the robustness of UV-enhanced satellite inversion strategies in regional ARF modeling. Conclusions This study presents a data-model integration framework for estimating ARF over coal mining regions using multi-source satellite observations and physically based scattering and radiative transfer models. The combination of MODIS visible and OMI ultraviolet aerosol products improves the inversion of absorbing aerosol particle size distributions and enhances the retrieval of SSA, especially under complex mixing conditions. The constructed regression models reveal that SSA exerts a greater influence on radiative forcing than AOD, and that including particle size parameters further strengthens model reliability. Despite a reduction in observational frequency due to OMI's narrower sampling, the incorporation of UV-band information leads to consistently improved model performance across all atmospheric layers, particularly in the atmospheric column. These results highlight the critical role of spectral diversity in satellite remote sensing for accurately characterizing the radiative impacts of absorbing aerosols, and demonstrate the feasibility of applying such approaches to high-emission, data-scarce environments like the Zhundong Coalfield.

Abandoned farmlands are increasing due to socio-economic changes and land marginalization, and they require sustainable land management practices. Biocrusts are a common cover on the topsoil of abandoned farmlands and play an important role in improving soil stability and erosion resistance. The critical functions of biocrusts are known to mostly rely on their biofilaments and extracellular polymeric substances (EPS), but how these components act at microscopic scale is still unknown, while rheological methods are able to provide new insights into biocrust microstructural stability at particle scale. Here, bare soil and two representative types of biocrusts (cyanobacterial and moss crusts) developed on sandy (Ustipsamments) and sandy loam (Haplustepts) soils in abandoned farmlands in the northern Chinese Loess Plateau were collected at a sampling depth of 2 cm. Changes in the rheological properties of the biocrusts were analyzed with respect to their biofilament network and EPS contents to provide possible explanations. The rheological results showed that compared with bare soil, storage and loss moduli were decreased by the biocrusts on sandy soil, but they were increased by the biocrusts on sandy loam soil. Other rheological parameters tau max, gamma L, gamma YP, and Iz of biocrusts on both soils were significantly higher than those of bare soil, showing higher viscoelasticity. And the moss crusts had about 10 times higher rheological property values than the cyanobacterial crusts. Analysis from SEM images showed that the moss crusts had higher biofilament network parameters than the cyanobacterial crusts, including nodes, crosslink density, branches, branching ratio and mesh index, and biofilament density, indicating that the biofilament network structure in the moss crusts was more compact and complex in contrast to the cyanobacterial crusts. Additionally, EPS content of the moss crusts was higher than that of the cyanobacterial crusts on both soils. Overall, the crosslink density, biofilament density, and EPS content of the biocrusts were significantly and positively correlated with their gamma YP and Iz. The interaction between crosslink density and biofilament density contributed 73.2 % of gamma YP, and that between crosslink density and EPS content contributed 84.0 % of Iz. Our findings highlight the biocrusts-induced changes of abandoned farmland soil rheological properties in drylands, and the importance of biocrust biofilament network and EPS in maintaining abandoned farmland soil microstructural stability to resist soil water/wind erosion and degradation, providing a new perspective for sustainable management of abandoned farmlands.

The wheat powdery mildew (WPM) is one of the most severe crop diseases worldwide, affecting wheat growth and causing yield losses. The WPM was a bottom-up disease that caused the loss of cell integrity, leaf wilting, and canopy structure damage with these symptoms altering the crop's functional traits (CFT) and canopy spectra. The unmanned aerial vehicle (UAV)-based hyperspectral analysis became a mainstream method for WPM detection. However, the CFT changes experienced by infected wheats, the relationship between CFT and canopy spectra, and their role in WPM detection remained unclear, which might blur the understanding for the WPM infection. Therefore, this study aimed to propose a new method that considered the role of CFT for detecting WPM and estimating disease severity. The UAV hyperspectral data used in this study were collected from the Plant Protection Institute's research demonstration base, Xinxiang city, China, covering a broad range of WPM severity (0-85 %) from 2022 to 2024. The potential of eight CFT [leaf structure parameter (N), leaf area index (LAI), chlorophyll a + b content (Cab), carotenoids (Car), Car/Cab, anthocyanins (Ant), canopy chlorophyll content (CCC) and average leaf angle (Deg)] obtained from a hybrid method combining a radiative transfer model and random forest (RF) and fifty-five narrow-band hyperspectral indices (NHI) was explored in WPM detection. Results indicated that N, Cab, Ant, Car, LAI, and CCC showed a decreasing trend with increasing disease severity, while Deg and Car/Cab exhibited the opposite pattern. There were marked differences between healthy samples and the two higher infection levels (moderate and severe infection) for Cab, Car, LAI, Deg, CCC, and Car/Cab. N and Ant only showed significant differences between the healthy samples and the highest infection level (severe infection). As Cab, Car, and Ant decreased, the spectral reflectance in the visible light region increased. The decrease in N and LAI was accompanied by a reduction in reflectance across the entire spectral range and the near-infrared area, which was exactly the opposite of Deg. The introduction of CFT greatly improved the accuracy of the WPM severity estimation model with R2 of 0.92. Features related to photosynthesis, pigment content, and canopy structure played a decisive role in estimating WPM severity. Also, results found that the feature importance showed a remarkable interchange as increasing disease levels. Using features that described canopy structure changes, such as optimized soil adjusted vegetation index, LAI, visible atmospherically resistant indices, and CCC, the mild infection stage of this disease was most easily distinguished from healthy samples. In contrast, most severe impacts of WPM were best characterized by features related to photosynthesis (e.g., photochemical reflectance index 515) and pigment content (e.g., normalized phaeophytinization index). This study help deepen the understanding of symptoms and spectral responses caused by WPM infection.

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.

The granular and natural characteristics of soil introduce size effects to its deformation and strength properties. Therefore, investigating the phenomenon of strain localisation in soil requires a multi-scale characterisation. This study examined the intrinsic scale patterns in samples with different sizes of reinforcing particles through triaxial compression tests. Additionally, the formation mechanism of microscopic shear bands was investigated using numerical simulation methods. Drawing from the soil cell model theory, the average strain energy release coefficient was introduced to validate the transformation of the overall strain energy of the specimen after reaching the peak stress. This reflects the progressive initiation and competitive process of multiple bands. The results indicate that samples with different sizes and types of reinforcing particles exhibit various failure patterns, including single-type, 'x'-shaped, 'v'-shaped, parallel and others. The soil exhibits size effects, with the ratio of intrinsic scale to particle size decreasing as the size of reinforcing particles increases. Prior to the stress peak, non-elastic dissipation energy begins to increase, indicating the initiation of plastic deformation in the soil. Localised strain zones are activated, and after the peak, there is a sharp increase in stress within the shear bands, accompanied by rebound outside the band.

Localized rock failures, like cracks or shear bands, demand specific attention in modeling for solids and structures. This is due to the uncertainty of conventional continuum-based mechanical models when localized inelastic deformation has emerged. In such scenarios, as macroscopic inelastic reactions are primarily influenced by deformation and microstructural alterations within the localized area, internal variables that signify these microstructural changes should be established within this zone. Thus, localized deformation characteristics of rocks are studied here by the preset angle shear experiment. A method based on shear displacement and shear stress differences is proposed to identify the compaction, yielding, and residual points for enhancing the model's effectiveness and minimizing subjective influences. Next, a mechanical model for the localized shear band is depicted as an elasto-plastic model outlining the stress-displacement relation across both sides of the shear band. Incorporating damage theory and an elasto-plastic model, a proposed damage model is introduced to replicate shear stressdisplacement responses and localized damage evolution in intact rocks experiencing shear failure. Subsequently, a novel nonlinear mathematical model based on modified logistic growth theory is proposed for depicting the shear band's damage evolution pattern. Thereafter, an innovative damage model is proposed to effectively encompass diverse rock material behaviors, including elasticity, plasticity, and softening behaviors. Ultimately, the effects of the preset angles, temperature, normal stresses and the residual shear strength are carefully discussed. This discovery enhances rock research in the proposed damage model, particularly regarding shear failure mode. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/

This study aimed to address the challenges of solid waste utilization, cost reduction, and carbon reduction in the treatment of deep-dredged soil at Xuwei Port in Lianyungang city of China. Past research in this area was limited. Therefore, a curing agent made from powdered shells was used to solidify the dredged soil in situ. We employed laboratory orthogonal tests to investigate the physical and mechanical properties of the powdered shell-based curing agent. Data was collected by conducting experiments to assess the role of powdered shells in the curing process and to determine the optimal ratios of powdered shells to solidified soil for different purposes. The development of strength in solidified soil was studied in both seawater and pure water conditions. The study revealed that the strength of the solidified soil was influenced by the substitution rate of powdered shells and their interaction with cement. Higher cement content had a positive effect on strength. For high-strength solidified soil, the recommended ratio of wet soil: cement: lime: powdered shells were 100:16:4:4, while for low-strength solidified soil, the recommended ratio was 100:5.4:2.4:0.6. Seawater, under appropriate conditions, improved short-term strength by promoting the formation of expansive ettringite minerals that contributed to cementation and precipitation. These findings suggest that the combination of cement and powdered shells is synergistic, positively affecting the strength of solidified soil. The recommended ratios provide practical guidance for achieving desired strength levels while considering factors such as cost and carbon emissions. The role of seawater in enhancing short-term strength through crystal formation is noteworthy and can be advantageous for certain applications. In conclusion, this research demonstrates the potential of using a powdered shell-based curing agent for solidifying dredged soil in an environmentally friendly and cost-effective manner. The recommended ratios for different strength requirements offer valuable insights for practical applications in the field of soil treatment, contributing to sustainable and efficient solutions for soil management.

An experimental study is made to understand the deformation characteristics and failure mechanism of sands subjected to severe plastic deformation in the ploughing model setup of in-plane orthogonal cutting. The cutting experiments were performed on sands over 3 orders of strain rates. High-speed imaging and concomitant image analysis were performed using the Particle Image Velocimetry algorithm to obtain the whole field velocity measurements of the material flow. The velocity field maps of the near tool tip region demonstrate a sharp change in the motion of sand particles along with the formation of a dead zone. The effective strain rate maps show regions of intense localized plastic deformation- termed shear bands. The inclination angle of these bands evolved periodically with time and showed a decreasing trend due to an increase in the surcharge and effective depth of cut. The morphology and overall characteristics of these mesoscale structures (shear bands) do not change significantly with strain rate. The cutting force signatures were oscillatory and suggested cyclic material softening (dilation)-hardening (compaction) ahead of the tool, which is also reflected in the periodic repositioning of shear bands. The limit equilibrium-based model was adequate to predict the tool-cutting forces well, even with the significant variation in strain rates.

Soil moisture detection research, which influences crop growth, land use, and soil erosion, is receiving significant attention. This study proposes a nondestructive, integrated ultrawideband (UWB)-based framework for soil moisture measurement and prediction. The method utilizes a UWB-loaded unmanned aerial vehicle (UAV) to gather radar echo data, circumventing soil damage issues inherent in current research and equipment. We first employ time-frequency analysis methods to convert the echo signals into 2-D spectrograms, constructing datasets labeled with soil moisture. Then, a trained neural network is used to predict the soil moisture at single point. Additionally, a novel interpolation method is proposed to enhance prediction accuracy (ACC) for the ridge-furrow structure of farmland. The experimental results demonstrate that the proposed algorithm achieves a soil moisture measurement ACC of 98% in both vegetated and nonvegetated conditions, indicating strong robustness. In terms of moisture distribution prediction, the mean squared error (mse) of soil moisture spatial distribution prediction is reduced by 42% compared to traditional methods. Therefore, this system provides technical support for efficient, large-scale, and nondestructive soil information collection.

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere (NH) is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network is presented. This framework defines the landscape FT-cycle retrieval as a time-series anomaly detection problem, considering the frozen states as normal and the thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is assessed over Alaska using in situ observations, demonstrating an 11% improvement in accuracy and reduced uncertainties compared to traditional methods that rely on thresholding the normalized polarization ratio (NPR).