Search Results (1,940)

Soil organic matter (SOM) is essential for ecosystem health and agricultural productivity. Accurate prediction of SOM content is critical for modern agricultural management and sustainable soil use. Existing digital soil mapping (DSM) models, when processing temporal data, primarily focus on modeling the changes in input data across successive time steps. However, they do not adequately model the relationships among different input variables, which hinders the capture of complex data patterns and limits the accuracy of predictions. To address this problem, this paper proposes a novel deep learning model, 2-Channel Network (2C-Net), leveraging sequential multi-temporal remote sensing images to improve SOM prediction. The network separates input data into temporal and spatial data, processing them through independent temporal and spatial channels. Temporal data includes multi-temporal Sentinel-2 spectral reflectance, while spatial data consists of environmental covariates including climate and topography. The Multi-sequence Feature Fusion Module (MFFM) is proposed to globally model spectral data across multiple bands and time steps, and the Diverse Convolutional Architecture (DCA) extracts spatial features from environmental data. Experimental results show that 2C-Net outperforms the baseline model (CNN-LSTM) and mainstream machine learning model for DSM, with R² = 0.524, RMSE = 0.884 (%), MAE = 0.581 (%), and MSE = 0.781 (%)². Furthermore, this study demonstrates the significant importance of sequential spectral data for the inversion of SOM content and concludes the following: for the SOM inversion task, the bare soil period after tilling is a more important time window than other bare soil periods. 2C-Net model effectively captures spatiotemporal features, offering high-accuracy SOM predictions and supporting future DSM and soil management. Full article

The Shizhuyuan polymetallic deposit in Hunan Province, China, is a world-class ore field rich in tungsten (W), tin (Sn), molybdenum (Mo), and bismuth (Bi), now facing resource depletion due to prolonged exploitation. This study addresses the limitations of traditional geological prediction methods in complex terrain by integrating multi-source datasets—including γ-ray spectrometry, high-precision magnetometry, induced polarization (IP), and soil radon measurements—across 5049 samples. Unsupervised factor analysis was employed to extract five key ore-indicating factors, explaining 82.78% of data variance. Based on these geological features, predictive models including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were constructed and compared. SHAP values were employed to quantify the contribution of each geological feature to the prediction outcomes, thereby transforming the machine learning “black-box models” into an interpretable geological decision-making basis. The results demonstrate that machine learning, particularly when integrated with multi-source data, provides a powerful and interpretable approach for deep mineral prospectivity mapping in concealed terrains. Full article

Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change. Full article

In the process of large-scale water conservancy and hydropower station construction in the southwest region of China, obtaining the deep overburden pressuremeter modulus E_m is of great significance for the calculation of foundation bearing capacity and dam foundation settlement. However, due to the complex nature of the soil properties in deep overburden layers, conducting deep-hole pressuremeter tests is challenging, time-consuming, and costly. In order to efficiently and accurately obtain the pressuremeter modulus of deep overburden, this paper takes the deep overburden in the river valley where a large hydropower station dam is located in the southwest region as the research object. It proposes a method based on data-driven prediction of the pressuremeter modulus and combines it with the physical mechanism to carry out the reliability analysis of the prediction results. By constructing a database of soil physical and mechanical parameters, including the pressuremeter modulus, the prediction performance of Random Forest (RF), Support Vector Regression (SVR), and BP Neural Network on the pressure modulus was evaluated. The Particle Swarm Optimization (PSO) was utilized for hyperparameter optimization to enhance the reliability of prediction results. The results indicate that the RF and PSO-RF models exhibit a comprehensive advantage for accurately predicting the pressuremeter modulus. The prediction results of the model for new data have a strong correlation with the results calculated by the Menard formula, which demonstrates the reliability of the model. Therefore, establishing the relationship between the conventional physical and mechanical parameters of deep overburden and the pressuremeter modulus, and predicting the pressuremeter modulus based on data-driven methods, has significant engineering value for obtaining the pressuremeter modulus of deep overburden efficiently, economically, and reliably. It also holds significant importance for the extended application of machine learning in the field of soil parameter prediction. Full article

Desertification threatens the sustainability of dryland ecosystems, yet many existing monitoring frameworks rely on static maps, coarse spatial resolution, or lack temporal forecasting capacity. To address these limitations, this study introduces PyGEE-ST-MEDALUS, a novel spatiotemporal framework combining the full MEDALUS desertification model with deep learning (CNN, LSTM, DeepMLP) and machine learning (RF, XGBoost, SVM) techniques on the Google Earth Engine (GEE) platform. Applied across Tebessa Province, Algeria (2001–2028), the framework integrates MODIS and Sentinel-1/-2 data to compute four core indices—climatic, soil, vegetation, and land management quality—and create the Desertification Sensitivity Index (DSI). Unlike prior studies that focus on static or spatial-only MEDALUS implementations, PyGEE-ST-MEDALUS introduces scalable, time-series forecasting, yielding superior predictive performance (R² ≈ 0.96; RMSE < 0.03). Over 71% of the region was classified as having high to very high sensitivity, driven by declining vegetation and thermal stress. Comparative analysis confirms that this study advances the state-of-the-art by integrating interpretable AI, near-real-time satellite analytics, and full MEDALUS indicators into one cloud-based pipeline. These contributions make PyGEE-ST-MEDALUS a transferable, efficient decision-support tool for identifying degradation hotspots, supporting early warning systems, and enabling evidence-based land management in dryland regions. Full article

Quantifying deep soil (10–100 cm) and non-growing-season soil respiration (SR) is crucial for refining carbon (C) cycle models, yet the regulatory mechanisms governing these processes remain unclear. The novelty of this study lies in its focus on deep soils and non-growing seasons to elucidate how soil properties regulate SR under these special conditions. We conducted an on-site field experiment in the Caragana korshinskii plantation, measuring SR at soil depths of 0–10 cm, 10–50 cm, and 50–100 cm during the non-growing season and growing. The results suggested that the annual cumulative soil CO₂ fluxes reached 510.1 (0–10 cm), 131.5 (10–50 cm), and 45.3 g CO₂·m⁻² (50–100 cm). These emissions during the non-growing season accounted for 33%, 31%, and 32%, respectively. The soil physical properties (temperature, moisture, bulk density) explained the greatest variation in SR during growing and non-growing periods, followed by the biological properties (α-diversity, root biomass) and chemical properties (soil organic C, ammonium nitrogen, total C/nitrogen ratio). Depth-specific analysis demonstrated that soil physical properties explained the most SR variance at three depths with independent contributions of 78.9% (0–10 cm), 89.7% (10–50 cm), and 76.9% (50–100 cm). These values exceeded the independent contributions of chemical properties (70.3%, 70.9%, 60.0%) and biological properties (54.9%, 45.1%, 41.6%) at the corresponding depths. Overall, deep soil and non-growing season SR represent important C emission sources; excluding them may therefore substantially overestimate net C sequestration potential. Full article

Due to its benefits and efficiency, mechanized sugarcane harvest is a common practice in Brazil; however, continuous traffic of agricultural machinery leads to soil compaction at the end of each harvest cycle. Hence, this study evaluated whether machine traffic affects soil physical and hydraulic properties, root growth, and crop productivity in sugarcane areas during different harvest cycles. Four treatments were performed consisting of an area planted with different stages (years) of sugarcane crop: T1 = after the first harvest—plant cane (area 1); T2 = after the second harvest—first ratoon cane (area 2); T3 = after the third harvest—second ratoon cane (area 3); T4 = after fourth harvest—third ratoon cane (area 4). Five sampling sites were considered in each area, constituting five replicates collected from four layers. Two collection positions were considered: wheel track (WT) and planting row (PR). Soil physical properties, root system, productivity, and biometric characteristics of the sugarcane crop were evaluated at depths of 0.00–0.05 m, 0.05–0.10 m, 0.10–0.20 m, and 0.20–0.40 m. Traffic during the sugarcane crop growth cycles affected soil physical and hydraulic properties, showing sensitivity to the effects of the different treatments, producing variations in root growth and crop productivity. Plant cane cycle showed lower soil penetration resistance, bulk density, microporosity, higher saturated soil hydraulic conductivity, and macroporosity when compared with the other cycles studied. In the 0.10–0.20 m layer, all treatments produced higher soil penetration resistance and density, and lower saturated soil hydraulic conductivity. Dry biomass, volume, and root area were higher for the plant cane cycle in the 0.00–0.05 m and 0.05–0.10 m layers compared with the other crop cycles. Root dry biomass is directly related to crop productivity in layers up to 0.40 m deep. Sugarcane productivity was affected along the crop cycles, with higher productivity observed in the plant cane and first ratoon cane cycles compared with the second and third ratoon cane cycles. Full article

Trees are vital to both environmental health and human well-being. They purify the air we breathe, support biodiversity by providing habitats for wildlife, prevent soil erosion to maintain fertile land, and supply wood for construction, fuel, and a multitude of essential products such as fruits, to name a few. Therefore, it is important to monitor and preserve them to protect the natural environment for future generations and ensure the sustainability of our planet. Remote sensing is the rapidly advancing and powerful tool that enables us to monitor and manage trees and forests efficiently and at large scale. Statistical methods, machine learning, and more recently deep learning are essential for analyzing the vast amounts of data collected, making data the fundamental component of these methodologies. The advancement of these methods goes hand in hand with the availability of sample data; therefore, a review study on available high-resolution aerial datasets of trees can help pave the way for further development of analytical methods in this field. This study aims to shed light on publicly available datasets by conducting a systematic search and filter and an in-depth analysis of them, including their alignment with the FAIR—findable, accessible, interoperable, and reusable—principles and the latest trends concerning applications for such datasets. Full article

The scope of this research was to quantify the mid-term impact of different soil tillage on carbon/nitrogen agronomical key context under optimal growing conditions of the European moderate continental climate. A large-scale on-farm experiment was established in winter wheat/soybean two-crop long-term cultivation without fertilization on fertile Luvic Chernozem. Four treatments were conducted: (T1) ‘Deep Loosening’ with tillage depth of 50 cm, (T2) ‘Plowing’ to 30 cm, (T3) ‘Strip-Till’ with tillage depth of 20 cm, and (T4) ‘No-Till’; the tillage frequency at T1 and T2 was reduced and applied to soybean only, therefore, once per 2 years during the trial period 2020/21–2024/25. Unlike the crop yield, which decreased with tillage intensity decreasing (21.38 > 19.30 > 18.88 > 18.62 t/ha in dry matter cumulatively; T2 > T3 > T1 > T4), the carbon/nitrogen key agronomical parameters either increased (root nodules count/weight: thus confirmed convergent, occasionally reverse indicators; soil compaction: penetrometric resistance) or differed in varying patterns and extent (soil chemical indicators). In fertile Chernozem soils, tillage and indicators have different importance within the nexus studied; plowing still gives the most stable yields. To improve nitrogen fixing, farmers’ practices need to balance yield vs. soil health, including eliminating soil compaction. Full article

In recent years, controlling the integrity of shallow-buried natural gas pipelines within surface subsidence zones caused by high-intensity underground longwall mining in the Daniudi Gas Field of China’s Ordos Basin has emerged as a critical challenge impacting both mine planning and the safe, efficient co-exploitation of coal and deep natural gas resources. This study included field measurements and an analysis of surface subsidence data from high-intensity longwall mining operations at the Xiaobaodang No. 2 Coal Mine, revealing characteristic ground movement patterns under intensive extraction conditions. The subsidence basin was systematically divided into pipeline hazard zones using three key deformation indicators: horizontal strain, tilt, and curvature. Through ABAQUS-based 3D numerical modeling of coupled pipeline–coal seam mining systems, this research elucidated the spatiotemporal evolution of pipeline Von Mises stress under varying mining parameters, including working face advance rates, mining thicknesses, and pipeline orientation angles relative to the advance direction. The simulations further uncovered non-synchronous deformation behavior between the pipeline and its surrounding sand and soil, identifying two distinct evolutionary phases and three characteristic response patterns. Based on these findings, targeted pipeline integrity preservation measures were developed, with numerical validation demonstrating that maintaining advance rates below 10 m/d, restricting mining heights to under 2.5 m within the 260 m pre-mining influence zone, and where geotechnically feasible, the maximum stress of the pipeline laid perpendicular to the propulsion direction (90°) can be controlled below 480 MPa, and the separation amount between the pipe and the sand and soil can be controlled below 8.69 mm, which can effectively reduce the interference caused by mining. These results provide significant engineering guidance for optimizing longwall mining parameters while ensuring the structural integrity of shallow-buried pipelines in high-intensity extraction environments. Full article

This study examines the kinematic behavior of a large-scale colluvial landslide in a coastal low-elevation forest, where rainfall, geological formations, and hydrological conditions drive substantial slope displacement. The landslide comprises a colluvial layer overlying mudstone, with downslope movement toward the coastline induced by gravitational forces and infiltration. Using GPS surveys, inclinometers, soil moisture sensors, and numerical modeling, the temporal and spatial patterns of displacement were analyzed. Maximum horizontal displacements reach 8.1 cm/year, with deep-seated movements extending over 25 m into the mudstone. Key mechanisms include weakening of the colluvium–mudstone interface and creep within saturated mudstone, while a hydraulic barrier near the coastline restricts subsurface flow. Progressive upslope migration of the freshwater-bearing mudstone zone under annual rainfall further contributes to long-term deformation. These findings provide critical insights into the hydrologically controlled kinematics of coastal colluvial landslides. Full article

Urban deep excavations conducted near operational tunnels necessitate stringent deformation control. This study investigates the Baiyun Station excavation by employing a three-dimensional finite-element model based on the Hardening Soil Small-strain (HSS) constitutive law, calibrated using Phase I field monitoring data on wall deflection, ground settlement, and tunnel displacement. Material parameters for the HSS model derived from the prior Phase I numerical simulation were held fixed and used to simulate the Phase II excavation, with peak errors of less than 5.8% for wall deflection and less than 2.9% for ground settlement. The model was subsequently applied to evaluate the impacts of Phase II excavation. The key contribution of this study is a monitoring-driven HSS modeling framework that integrates staged excavation simulation with field-based calibration, enabling quantitative assessment of tunnel responses—including settlement troughs, bow-shaped wall deflection patterns, and the distance-decay characteristics of lining displacement—to support structural safety evaluations and protective design measures. The results demonstrate that the predicted deformations and lining stresses in adjacent power and metro tunnels remain within permissible limits, offering practical guidance for excavation control in densely populated urban areas. Full article

Regional ecological risk assessment (RERA) is a valuable tool for analyzing ecological risks at a broad-scale whose potential needs to be further exploited, especially when it comes to the in-depth mining of the final risk. Thus, in this research, based on RERA results acquired through land use function valuation and the ecological risk source-receptor-vulnerability framework, spatial autocorrelation analysis and geographical detector methods were employed to explore the spatial association features of regional ecological risk and its significant influencing factors in Guangxi, China. Next, a bivariate local spatial autocorrelation analysis tool was used to manifest the spatial impact directions of the important affecting factors on the final risk. The results of the study indicate that: (1) the north and west parts of Guangxi had a higher final ecological risk than that of the southeast; (2) from a percentage viewpoint, the low, medium, high, and very high levels of ecological risk accounted for 41.85%, 28.31%, 21.86%, and 7.98% of the total area, respectively; (3) the final regional ecological risk exhibited significant positive spatial correlation (Moran’s I = 0.466, p = 0.000) and the high-high association type was concentrated in the north and west parts of Guangxi while there was a low-low type in the southeast; (4) the most significant influencing factors for final risk consisted of lithology, land use ecology and production functions, slope, and soil; (5) compared with ecology and production functions, lithology, slope, and soil exhibited stronger positive influences on the final risk. Spatial association and quantitative attribution studies can increase the deepness of RERA and undoubtedly advance this field in the future. Moreover, based on the findings from the spatial quantitative attribution analysis, more explicit sustainable development countermeasures could be determined for the region. Full article

Snowmelt runoff is a major soil erosion trigger in mid-to-high latitude and altitude regions. Through runoff plot observations and simulations in the northeastern black soil region, this study reveals the key regulatory mechanism of water migration on snowmelt erosion. Results demonstrate that the interaction between thawed upper and frozen lower soil layers creates a significant hydraulic gradient during snowmelt. Impermeability of the frozen layer causes meltwater accumulation and moisture supersaturation (>47%, exceeding field capacity) in the upper layer. Freeze–thaw action accelerates vertical moisture migration and redistributes shallow moisture by increasing porosity. This process causes soils with high initial moisture to reach supersaturation faster, triggering earlier and more frequent erosion. Gray correlation analysis shows that soil moisture migration’s contribution to erosion intensity is layered: migration in shallow soil (0–10 cm) correlates most strongly with surface erosion; migration in deep soil (10–15 cm) exhibits a U-shaped contribution due to freeze–thaw front boundary effects. A regression model identified key controlling factors (VIP > 1.0): changes in bulk density, porosity, and permeability of deep soil significantly regulate erosion intensity. The nonlinear relationship between erosion intensity and moisture content (R² = 0.82) confirms supersaturation dominance. Physical structure and mechanical properties of unfrozen layers regulate erosion dynamics via moisture migration. These findings clarify the key mechanism of moisture migration governing snowmelt erosion, providing a critical scientific foundation for developing targeted soil conservation strategies and advancing regional prediction models essential for sustainable land management under changing winter climates. Full article

Straw return is essential for improving soil fertility, recycling organic matter, and sustaining productivity in rice–wheat systems. This study focuses on the conceptual design and systematic analysis of the spatial and temporal variability of straw return methods and their classification. We proposed and analyzed 36 technical models for straw return by integrating spatial distribution (depth and horizontal placement) with temporal variability (decomposition period managed through mulching or decomposers). The models of straw return were categorized into five classes: mixed burial, even spreading, strip mulching, deep burial, and ditch burial. Field experiments were conducted in Babaiqiao Town, Nanjing, China, using clay loam soils typical of intensive rice–wheat rotation. Soil properties (bulk density, porosity, and moisture content) and straw characteristics (length and density) were evaluated to determine their influence on decomposition efficiency and nutrient release. Results showed that shallow incorporation (0–5 cm) accelerated straw breakdown and microbial activity, while deeper incorporation (15–20 cm) enhanced long-term organic matter accumulation. Temporal control using mulching films and decomposer agents further improved moisture retention, aeration, and nutrient availability. For the rice–wheat system study area, four typical straw return modes were selected based on spatial distribution and soil physical parameters: straw even spreading, rotary plowing, conventional tillage with mulching, and straw plowing with burying. This study added to the growing body of literature on straw return by providing a systematic analysis of the parameters influencing straw decomposition and the incorporation. The results have significant implications for sustainable agricultural practices, offering practical recommendations for optimizing straw return strategies to improve soil health. Full article