Search Results (371)

Accurate estimation of soil surface moisture (SSM) is essential for various applications, including hydrological modeling, precision agriculture, and drought monitoring. With advancements in SAR data acquisition and modeling techniques, accurate retrieval of SSM has become increasingly feasible. The sensitivity of C-band (5.36 GHz) SAR data to the orientation of surface roughness with respect to the sensor look angle significantly influences surface soil moisture estimation results. Hence, to address this research gap, a modified eigen-space transformation was employed to obtain optimized surface roughness estimates, known as synthetic surface roughness (SSR). The results of the proposed SSR showed adequate statistical significance with the field-scale surface roughness values (r = 0.77, RMSE = 0.08) for all orientation angles, compared with the cross-polarization ratio (CPR) (r = 0.70, RMSE = 0.35) and Inverse of Anisotropy (A_I) (r = 0.59, RMSE = 0.49) measures. The modified Dubois model was inverted to retrieve the dielectric constant (ε_SSR) using SSR as a roughness proxy, demonstrating reliable performance for surface roughness conditions up to 4 cm and in situ dielectric constant values up to 8.5 (~29% volumetric soil moisture). However, exceeding this threshold leads to an underestimation of ε_SSR (Bias= −0.43), and hence a new framework, the optimized dielectric model (ε_ODM), was introduced using a piecewise-constrained angular projection correction. The volumetric moisture content (mv_ODM) retrieved from ε_ODM was promising (r = 0.86, RMSE = 0.04, Bias = 0.02) across a wide range of soil moisture conditions when compared with widely adopted backscattering models, namely Mod. Dubois, Calibrated IEM, Oh, and Mod. Oh. Full article

Clarifying how vegetation restoration regulates wind erosion, sediment redistribution, and soil improvement is essential for ecological management in desert photovoltaic power stations. This study was conducted in a high-clearance flexible-support photovoltaic power station at the edge of the Kubuqi Desert. Three restoration measures were compared: reed mulch combined with Atriplex canescens planting along the panel front edge (M1), A. canescens planting along the panel front edge alone (M2), and reed mulch combined with grass seeding (M3). The panel front-edge zone (QY), under-panel zone (BX), and pedestal zone (JZ) were used as functional units to analyze surface sediment grain-size characteristics, soil moisture, soil nutrients, windbreak efficiency, aerodynamic roughness length, and cumulative sand-fixing efficiency. All restoration measures altered the surface sediment structure, with Mz ranging from 2.005 to 2.364 and D₀ from 1.459 to 1.935. Soil moisture ranged from 0.58% to 4.34%, with the highest value occurring in the 20–30 cm layer of QY under M1. M1 also showed higher soil organic matter in QY and JZ, reaching 1.87 and 1.16 g·kg⁻¹, respectively. Windbreak efficiency decreased with height under all measures. M1 maintained the highest and most stable values, decreasing only from 61.16% at 10 cm to 55.52% at 100 cm. The total cumulative sand-fixing efficiency was also highest under M1 (233.66%), while M2 (215.05%) and M3 (214.58%) showed comparable total effects but different zonal responses. Wind-eroded materials shifted from fine-sand dominance toward a higher relative contribution of medium sand, reflecting the reduction in finer transported fractions rather than true grain coarsening. The novelty of this study lies in linking wind-erodible sediment redistribution, soil water and nutrient responses, and windbreak–sand-fixing performance across internal functional zones of a flexible-support photovoltaic array. These results indicate that vegetation restoration in desert photovoltaic power stations should be configured by functional zone, with composite interception at the panel front edge, structural maintenance in the under-panel zone, and cover-based sand trapping in deposition-prone areas. Full article

Soil microplastics undergo aging, but how aging modifies their effects on soil bacterial communities remains unclear. Here, we conducted a 180-day incubation experiment with no PVC (CK), pristine PVC microplastics (IP), and UV-aged PVC microplastics (AP, 0.5%, w/w). UV aging markedly altered PVC surface properties: roughness increased from approximately 12.9 to 21.8 nm, water contact angle decreased from 91.44° to 82.38°, and the O/C ratio increased from 0.37 to 0.43. Bacterial richness indices were largely unchanged, whereas Shannon diversity decreased under AP, indicating reduced community evenness. Bray–Curtis analysis showed significant community separation among treatments (PERMANOVA: R² = 0.364, p = 0.003), with UV aging further altering the trajectory of PVC-induced community reorganization. At the genus level, AP was associated with enrichment of Methylobacillus and lower robustness in exploratory co-occurrence network analysis, suggesting a distinct bulk-soil bacterial response compared with IP. Functional prediction further suggested that AP and IP were associated with different predicted pathway profiles, with AP showing higher predicted representation of pathways related to carbon metabolism, respiratory energy metabolism, potential prokaryotic carbon fixation, environmental sensing, cellular maintenance, and antimicrobial-resistance-associated categories, whereas IP was mainly associated with transport- and communication-related predicted functions. These predicted functional patterns require further validation using metagenomic, qPCR, transcriptomic, biochemical, or chemical approaches. Overall, these findings highlight the need to consider the UV aging status of PVC microplastics when evaluating their effects on soil bacterial communities. Full article

Freeze–thaw (F/T) processes play a critical role in the regulation of soil hydrothermal dynamics, land–atmosphere energy exchange, and ecosystem functioning. The spaceborne global navigation satellite system reflectometry (GNSS-R) has shown great potential for land surface F/T state detection; however, its monitoring capability remains limited by spatial resolution, revisit interval, observation coverage, and complex land surface conditions. In this study, Tianmu-1 (TM-1) multi-GNSS-R observations were used to detect daily land surface F/T states over the mid-to-high latitudes of the Northern Hemisphere. First, surface reflectivity observations from multi-GNSS, including the Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), Galileo, and GLONASS, were fused using a weighted averaging method based on the number of specular reflection points. Then, TM-1 multi-GNSS-R reflectivity was used as the primary remote-sensing input, while vegetation water content (VWC), surface roughness, and snow cover information were introduced as auxiliary environmental variables. The Soil Moisture Active Passive (SMAP) F/T product was used to provide supervised reference labels for developing Bayesian-optimized extreme gradient boosting (XGBoost) models for F/T state classification. Evaluation against SMAP F/T reference labels showed that the multi-GNSS fusion model achieved an area under the curve (AUC) of 0.853 and an overall accuracy of 77.3% without incorporating snow cover information, outperforming the single-GNSS models. After incorporating snow cover information, the AUC increased to 0.959, and the overall accuracy reached 89.3%. Shapley additive explanations (SHAP) analysis further showed that snow cover made the largest contribution to the final model output, suggesting that its improvement effect may reflect both physical snow-related surface information and seasonal contextual information. An independent point-based comparison with in situ observations from the international soil moisture network (ISMN) showed that the TM-1 F/T classification accuracy reached 85.2% after incorporating snow cover information, which was comparable to that of the SMAP product. These results demonstrate that TM-1 multi-GNSS-R observations have promising potential for detecting land surface F/T states during the autumn–winter freezing development period, and that integrating multi-GNSS-R reflectivity with snow cover information can substantially improve classification performance and spatial consistency within the available observation period. Full article

Expansive soil is highly susceptible to water-softening and desiccation cracking under alternating wet–dry conditions, often resulting in severe geotechnical and geological hazards. To mitigate these undesirable engineering properties, a composite improvement approach utilizing biochar and sisal fiber was employed. The shear strength and cracking characteristics of the improved expansive soil were systematically investigated through direct shear tests and desiccation cracking tests on specimens prepared with varying biochar contents, sisal fiber contents, and fiber lengths. Scanning electron microscopy (SEM) was further conducted to elucidate the underlying microstructural mechanisms. The results indicated that the individual addition of biochar or sisal fiber enhanced the shear strength of the expansive soil. Increasing the biochar content from 4% to 10% yielded an 8–19% strength gain, whereas raising the sisal fiber content from 1.5‰ to 6‰ led to a more substantial 36–110% improvement. Conversely, extending the fiber length from 10 mm to 30 mm diminished the shear strength by 11–21%. Higher biochar contents progressively increased the internal friction angle (from 14.84° to 18.52°) but were accompanied by a decline in cohesion (from 9.0 kPa to 4.0 kPa). In contrast, increasing the sisal fiber content markedly enhanced cohesion (from 4.7 kPa to 50.3 kPa) while marginally reducing the internal friction angle (from 15.2° to 12.8°). In terms of crack suppression, a 10% biochar content achieved an 86.8% reduction in crack ratio, while 6‰ sisal fiber yielded a 72.4% reduction. Range analysis revealed that crack length and crack ratio were most sensitive to biochar content, whereas crack width was predominantly governed by fiber content. Notably, surface cracking was completely eliminated in the composite specimen prepared with 10% biochar, 4.5‰ sisal fiber, and a fiber length of 20 mm. Microstructural analysis revealed that biochar particles featured rough surfaces and abundant internal pores, while the sisal fibers formed a randomly interwoven network within the soil matrix. The synergistic interplay between the rigid biochar skeleton and the flexible fiber network contributed to the substantial enhancement in both mechanical strength and crack resistance. Full article

This study evaluates the shear strength and geomechanical behavior of the soil–concrete interface in carbonate sands from the coast of Ceará, with particular relevance to offshore wind turbine foundations. Three sands with different calcium carbonate (CaCO₃) contents, namely, 10.6%, 22.0% and 30.0%, were tested under applied normal stresses of 50, 100 and 200 kPa. Conventional direct shear tests were carried out to determine soil–soil shear behavior, whereas controlled-interface tests were performed using cementitious specimens with smooth and rough surfaces. The soil–soil tests indicated effective internal friction angles (φ′) between 35° and 38°, with no cohesion. No direct correlation was observed between shear strength and CaCO₃ content. Instead, the results indicate that particle size distribution, particularly the proportion of finer fractions, exerted the main control on mechanical behavior. Within the three materials tested, no monotonic trend between CaCO₃ content and shear strength was identified, a finding that should be confirmed with a larger sample set. At the soil–concrete interface, shear stress mobilization depended on surface roughness, with the rough surface mobilizing higher shear stresses than the smooth surface. The ratio between the interface friction angle and the soil effective internal friction angle (δ/φ′) ranged from 0.963 to 0.994 for rough surfaces and from 0.859 to 0.951 for smooth surfaces. These findings show the need for site-specific characterization of carbonate sands and for explicit consideration of interface conditions in offshore foundation design, thereby reducing unnecessary structural oversizing. Full article

Unmanned aerial vehicle (UAV) multispectral imagery provides a promising approach for field-scale retrieval of soil organic matter (SOM) during the bare-soil period. However, tillage-induced surface heterogeneity is often overlooked. This heterogeneity may alter soil spectral responses and model performance. This study examined the effects of surface tillage status on UAV-based SOM retrieval in farmland. UAV multispectral imagery and 108 topsoil samples were collected during the bare-soil period. The SOM values ranged from 1.37 to 30.95 g/kg. Analyses were conducted under three tillage-status settings: undifferentiated tillage status, plowed-leveled status, and plowed-unleveled status. Spectral and textural features were extracted and selected using a genetic algorithm. These features were then used to develop SOM retrieval models with random forest regression, extreme gradient boosting, and support vector regression. For the six original multispectral bands, the correlations between SOM and band reflectance differed among tillage-status settings. They were weak under the undifferentiated tillage status. They were significantly negative under the plowed-leveled status and significantly positive under the plowed-unleveled status. Texture-derived indicators and standard normal variate analysis suggested that the positive correlations under the plowed-unleveled status may be partly associated with surface-structure-related spectral amplitude effects. Integrating textural features improved the overall test-set accuracy metrics. However, statistically detectable reductions in absolute prediction error were mainly observed under the plowed-unleveled status. On the random-split held-out test set, the highest $R^2$ values reached 0.84 and 0.85 under the plowed-leveled and plowed-unleveled statuses, respectively. These results indicate that surface tillage status is an important source of surface heterogeneity. It should therefore be explicitly considered in UAV-based SOM retrieval under the present study conditions. Full article

Accurate high-resolution mapping of soil organic carbon (SOC) is essential for agricultural management and carbon pool assessment in arid lakeside oases, a fragile aquatic-terrestrial transition ecosystem. However, targeted high-precision SOC mapping for typical lakeside oases remains insufficient: existing models have poor adaptability to the highly fragmented oasis landscapes, and fine-resolution SOC spatial products for the representative Bosten Lake oasis are lacking. To address this inadequacy, we integrated Sentinel-2 imagery with topographic, bioclimatic, and spectral environmental covariates and developed four machine learning models (Random Forest, XGBoost, SVR with RBF kernel, Cubist) for SOC prediction, based on 153 topsoil samples (0–20 cm) collected via stratified random sampling in the study area. Model performance was validated through 5-fold cross-validation, the optimal model was selected for 10 m resolution SOC mapping, and dominant driving factors were identified via SHAP analysis. The results showed that SOC content in the study area ranged from 2.37 to 20.63 g·kg⁻¹ (mean = 10.59 g·kg⁻¹), with moderate spatial variability (CV = 34.86%). The Cubist model achieved the highest mapping accuracy (R² = 0.8166, RMSE = 1.5812 g·kg⁻¹, MAE = 0.9247 g·kg⁻¹). The generated high-resolution SOC map clearly revealed a spatial pattern of high values in the eastern well-irrigated cropland and low values in bare and salinized areas at the oasis edge. The Bare Soil Index (BSI), surface roughness, and Normalized Difference Red Edge Index 1 (NDRE1) were the dominant factors controlling SOC spatial distribution. This study mitigates the inadequacy of high-precision SOC mapping in typical arid lakeside oases, and the proposed framework is readily applicable to other fragmented arid landscapes worldwide and provides reliable spatial data and a scalable technical framework for precision agriculture and sustainable land management in similar fragile ecosystems. Full article

Rapid and accurate detection of landslide-affected areas is critical for disaster response and risk mitigation. Sentinel-1 SAR imagery offers all-weather, day-and-night observation capability, but existing deep learning approaches treat landslide detection as a single-pass segmentation problem, which limits performance in complex terrain where backscatter changes are confounded by soil moisture, surface roughness, urban double bounce, shadow, and layover effects. MAD-SAR, a rule-based agentic framework that coordinates anomaly detection, super-resolution, object detection, and semantic segmentation under a planning orchestrator and a physics-aware validation engine is proposed. The orchestrator selects specialist modules, their execution order, and the number of refinement iterations according to a scene complexity score computed from SAR-derived statistics. The physics-aware validation engine cross-checks every candidate detection against backscatter change thresholds, DEM-derived slope constraints, and radar geometry masks before any detection is committed to the output. MAD-SAR is evaluated on three Japanese disaster datasets: Hiroshima 2018, Kumamoto 2016, and Ibaraki 2019. On the held-out Ibaraki test event, the framework achieves an F1-score of 0.863 and IoU of 0.759, outperforming all baselines and reducing false alarms by 45% relative to standalone SegFormer. Ablation results confirm that each module contributes to the final performance. These results suggest that multi-module orchestration with embedded physical validation can meaningfully improve SAR-based landslide mapping, though broader validation across regions, sensor configurations, and failure mechanisms remains necessary. Full article

Bentonite slurry is frequently used as a support fluid in the construction of drilled shafts. During the piling process, the slurry acts as a sealant and slightly penetrates the nearby soil. However, the degree to which bentonite slurry penetrates the soil affects the resulting frictional capacity of the bored piles. This experimental study examines the extent of this phenomenon, arising from the formation of what is typically known as the bentonite filter cake or mud cake. The frictional properties of the filter cake are examined through three groups of direct shear tests, employing three pre-cast concrete blocks positioned on a sand layer that has been subject to bentonite slurry for varying durations. To ensure comparison, a similar pre-cast concrete block was utilized in each test series, resulting in uniform surface roughness in the concrete. A handheld surface roughness device was utilized to measure the roughness profile of each concrete block, assessing the surface roughness of all concrete surfaces. The outcomes of the direct shear test performed were subsequently normalized based on the assessed roughness of the concrete surface. Experimental results showed that the friction capacity of the soil–concrete interface for granular materials (“sand–concrete interface”) decreases with longer exposure to bentonite slurry. Specifically, the shear strength is inversely proportional to the square root of the bentonite slurry exposure time. Tests on the internal friction angle of Québec Valcartier granitic sand and the friction angles at sand–concrete interfaces with and without bentonite slurry exposure revealed that the non-exposed sand–concrete interface achieves a peak friction angle equal to 77% of the peak internal friction angle of Québec Valcartier granitic sand. This value represents 69% and 60% of the peak friction angle of the sand tested for bentonite exposure durations of 2 and 4 h, respectively. Full article

Efficient seedbed preparation under conservation-oriented tillage requires balanced aggregate fragmentation, surface microrelief and energy demand. This study investigated the influence of passive toothed roller parameters on soil fragmentation, surface roughness, draft force and fuel consumption during chisel tillage under medium-loam Calcisol conditions. Three configurations were compared: a chisel plow without a roller, with a slat roller and with a toothed roller. An analytical framework describing aggregate capture, tooth–soil contact frequency and resistance formation was combined with field experiments and regression-based response surface analysis. The toothed roller improved measured soil treatment indicators compared with the no-roller and slat-roller configurations due to discrete tooth–soil interaction, localized stress concentration and repeated loading of loosened aggregates. Rational parameter ranges were identified: a roller diameter of 0.45–0.46 m, 13–15 teeth, transverse spacing of 8.0–8.6 cm, a tooth height of 7.5–8.5 cm and specific load of 0.9–1.1 kN m⁻¹. Under the selected configuration, aggregates smaller than 50 mm increased from 76.1% to 88.0%, surface roughness decreased from 6.8 to 3.7 cm and residue retention remained above 60%. Fuel consumption increased to 28.4–28.5 L ha⁻¹, reflecting the additional energetic cost of fragmentation and levelling. The approach supports rational selection of passive toothed roller parameters under the tested conditions. Full article

To address the lack of long-term, wide-area surface deformation observations along the geologically complex Dangxiong–Yangbajing section of the G6 Expressway in the frozen-ground region of the Qinghai–Tibet Plateau, where conventional monitoring is insufficient, we applied Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to retrieve surface deformation within a 2.0 km corridor on both sides of the highway from 24 November 2021 to 26 December 2024, and to characterize the spatiotemporal evolution of deformation. We then integrated eight explanatory factors (slope, surface roughness, distance to rivers, distance to faults, surface soil moisture, precipitation, land surface temperature (LST), and fractional vegetation cover (FVC)). Geodetector was used to quantify their explanatory power and spatial heterogeneity with respect to deformation. The results show pronounced spatially uneven settlement along this highway segment, with maximum annual settlement rates exceeding −45 mm/a. Five settlement centers were identified, including two major pavement subsidence zones. Distance to faults and soil moisture showed higher single-factor explanatory power, whereas FVC, precipitation, and LST also contributed to deformation heterogeneity. Interaction detection further indicated that the interactions between fault-related conditions with vegetation, soil moisture, precipitation, and LST substantially enhanced the explanatory power, suggesting that the deformation pattern was associated with multi-factor coupling rather than a single dominant environmental factor. These findings demonstrate the utility of integrating SBAS-InSAR with Geodetector analysis for corridor-scale highway deformation assessment and provide a remote sensing basis for targeted hazard assessment and risk mitigation for highways in frozen-ground environments. Full article

Soil surface roughness (SSR), referring to surface irregularities, is a key parameter for assessing soil condition and tillage outcomes. Characterizing roughness at fine scales—including clods and depressions—remains challenging for 2.5D digital elevation models (DEMs) collected at the meter scale in the field. This study presents two segmentation methods for high-resolution DEMs from an agricultural site. For clod segmentation, a wavelet-based approach from the literature was used, while a novel histogram-based method was introduced for depressions. Both methods were evaluated on natural soil surfaces with varying roughness levels and a simulated surface, with and without noise, using standard metrics (recall, precision, F1-score, IoU). The best clod segmentation results were achieved on fine seedbeds (95.2% recall, 97.3% precision, 96.2% F1-score), with slightly lower but strong performance on plowed surfaces (84.2% recall, 96.9% precision, 90.1% F1-score). Due to their lower frequency, depressions were primarily assessed visually under field conditions. For the simulated surface (with ground truth), IoU values ranged from 84.2% to 87.9% for clods and around 92% for depressions, demonstrating competitive performance. Additionally, the volume of roughness features was computed and visualized using cumulative distribution functions. These segmentation methods enable monitoring of soil surface conditions, with applications in precision agriculture, surface-water interactions, and meter-scale microwave remote sensing. Full article

Planetary exploration has increasingly relied on mobile robots known as rovers to support space development. Among various locomotion systems, legged mechanisms have attracted attention as a promising approach for achieving high mobility on rough terrain. However, the surfaces of extraterrestrial bodies such as the Moon and Mars are covered with loose regolith that easily deforms under external forces. As a result, legged rovers tend to disturb the ground surface and experience slippage due to leg-induced loading. To address this issue, a previous study proposed a novel walking method in which the rover’s leg applies vibration to the soil before stepping to compact it. Experiments confirmed that this vibration increases the soil’s bearing capacity, defined as its resistance to vertical loading. This increase is attributed to improvements in soil density and particle interconnectivity, which enhance soil shear strength. In this study, the relationship between the bearing capacity of vibration-compacted soil and its shear strength is investigated through experiments. The results reveal a clear correlation between these parameters, indicating that the bearing capacity of vibration-compacted soil can be estimated from shear strength measurements. Full article

Accurate quantification of soil moisture (m_v) is of great scientific significance for regional hydrological modeling, meteorological forecasting, and drought and flood disaster monitoring. Although C-band SAR aboard the GF-3 satellite constellation supports large-scale retrieval, existing studies are mostly confined to local validation under simple surface conditions. Its retrieval performance across varied surface roughness (s), m_v, soil texture, and topography, as well as the synergistic retrieval ability of the satellite constellation, has not been fully investigated. Therefore, this study systematically evaluated four m_v retrieval strategies using quality-controlled satellite-ground synchronous observation data from 11 arid-to-humid experimental areas (378 plots) in China: Oh94 model inversion (Strategy I), calibrated Oh94 model inversion (Strategy II), calibrated Oh94 model inversion with prior constraints on m_v and s (Strategy III), and random forest inversion (Strategy IV). Subsequently, the measured satellite backscattering coefficients (${\sigma }_{\mathrm{obs}}^0$) were compared with model simulations (${\sigma }_{\mathrm{sim}}^0$), yielding initial biases of 2.08 dB, 0.78 dB, and −0.29 dB for VV, HH, and HV polarizations, respectively, and these biases were significantly reduced to −0.01 dB, 0.00 dB, and −0.06 dB after systematic deviation correction (SDC). Overall, the root-mean-square errors (RMSE) of m_v retrieval for Strategies I–IV were 0.092, 0.078, 0.058, and 0.046 cm³·cm⁻³, respectively, while those for s retrieval were 0.620, 0.578, 0.610, and 0.403 cm. Strategy IV achieved the highest m_v retrieval accuracy owing to the robust nonlinear predictive capacity of machine learning. Nevertheless, Strategy III exhibited superior transferability in spatially independent validation, with an RMSE of 0.054 cm³·cm⁻³, outperforming Strategy IV (0.065 cm³·cm⁻³). This demonstrates that Strategy III possesses a stronger generalization ability than purely data-driven models under domain shifts. By incorporating prior constraints, Strategy III effectively mitigated radiometric inconsistencies within the satellite constellation, and m_v retrieval biases among GF-3, GF-3B, and GF-3C converged stably within 0.021 cm³·cm⁻³, with RMSE ranging from 0.046 to 0.079 cm³·cm⁻³. This study validates the feasibility of synergistic m_v retrieval over bare surfaces using the GF-3 SAR constellation, providing critical technical support for large-area operational mapping. Full article