Search Results (1,089)

Seasonal freeze–thaw cycling progressively rearranges pores and propagates microcracks in silty clay, reducing the reliability of cold-region earthworks. This study evaluated a bio–polymer stabilization strategy combining microbially induced carbonate precipitation (MICP) with anionic polyacrylamide (APAM) to improve mechanical performance and freeze–thaw durability. Six groups were prepared at identical moisture and compaction conditions: water, APAM, and four MICP–APAM groups with bacterial optical densities (OD600) of 0.8, 1.0, 1.2, and 1.4. Unconfined compressive strength, unconsolidated-undrained triaxial compression, ultrasonic pulse velocity, and SEM, TG/DTG, XRD, and FTIR analyses were conducted before and after freeze–thaw cycling. The M1.0-APAM group showed the best overall performance, with UCS values of 1.35 MPa before cycling and 0.89 MPa after nine cycles, together with high shear resistance and ultrasonic velocity. Lower bacterial concentration provided insufficient cementation, whereas higher concentrations promoted non-uniform carbonate deposition, pore heterogeneity, and local stress concentration. Microstructural evidence indicated that OD600 ≈ 1.0 produced a relatively homogeneous network of fine carbonate clusters and polymer-associated films, with calcite formation supported by TG/DTG and XRD. The results show that MICP–APAM treatment enhances silty clay primarily through coordinated mineralization uniformity, pore refinement, and polymer bridging, providing a sustainable stabilization option for seasonally frozen soils. Full article

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m³ m⁻³ at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m³ m⁻³. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m³ m⁻³. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. Full article

The Loess Hilly Region of northern Shaanxi is one of the most erosion-prone areas in the world due to its porous, erodible loess, steep slopes, and seasonal rainfall. To address this, conversion of sloping farmland to terraces has been extensively conducted across China’s loess regions, as terracing can reduce soil and water loss and enhance soil fertility. However, disturbance of soil layers during terracing can also lead to short-term decline in farmland productivity. This study investigates the effects of terracing operations at two sites of different ground substrate configurations in the Loess Hilly Region. Utilizing geochemical and molecular biological analysis methods, we examined the changes in the physicochemical and microbial properties of the ground substrate after terracing, using adjacent sloping farmlands as control sites. The results show that when the ground substrate configuration remained intact, terracing increased the average water content (from 8.44% to 14.34%) and soil organic carbon (from 2.74 g/kg to 5.76 g/kg) by 70% and 110%, respectively, and increased soil microbial α-diversity by 90%. The microbial community structure was also enhanced with an increase in relative abundance of soil- and plant-benefiting genera such as Streptomyces and Nocardioides, thereby promoting plant growth. Conversely, when the ground substrate configuration was altered, terracing led to a decrease in soil nutrient and moisture content, which was detrimental to crop growth. Therefore, maintaining the integrity of the ground substrate configuration is crucial during the terracing process to achieve optimal soil and water conservation outcomes. Full article

This study addresses the limitation of single-factor environmental assessment in autonomous sweet potato farming under open-field conditions. An IoT-based sweet potato growth environment monitoring and comprehensive evaluation system was developed by integrating an STM32-based acquisition terminal, multi-sensor data collection, Narrow Band Internet of Things (NB-IoT) transmission, and cloud-based visualization. Five key environmental variables, namely soil temperature, soil moisture, soil available nitrogen, photosynthetically active radiation (PAR), and CO₂, were continuously monitored. To improve the evaluation of heterogeneous and uncertain environmental information, a multi-factor environmental quality assessment method combining fuzzy membership functions and an improved D-S evidence theory was proposed. Field experiments were conducted in Danzhou, Hainan, China, and 600 valid synchronized samples were obtained for analysis. The results showed that most samples were classified as Suitable (63.5%), followed by Normal (30.8%) and Poor (5.7%), with a mean comprehensive environmental score of 0.802. Among the monitored variables, PAR and soil temperature showed relatively high adaptive weights, indicating their important roles in environmental quality discrimination. Furthermore, the comprehensive environmental evaluation result exhibited a significant positive correlation with sweet potato yield (r = 0.6501, p = 2.3724 × 10⁻⁷³), demonstrating good explanatory ability for yield variation. The proposed system provides an effective technical framework for real-time environmental monitoring, quantitative suitability evaluation, and precision management in autonomous sweet potato farming. Full article

In the seasonally frozen area, slopes are exposed to freeze–thaw cycles; thus, water and heat are moved, and the foundation for the transportation infrastructure in cold regions may be weakened. Based on the relatively strong water-recharge effect and considerable fluctuations in shallow soil moisture during the spring thaw along the Naba section of the G218 Highway in Xinjiang, China, a coupled hydro-thermal model for frozen soil that considers snowmelt infiltration and rainfall recharge was developed, and it was numerically implemented in COMSOL. A one-dimensional unidirectional freezing test of a soil column was used to validate the model, and the relative errors of the simulated temperature and moisture fields were 3.8% and 4.3%, respectively; both are within the accuracy requirements for engineering-scale analysis. Then, a model was used to determine how the temperature, volumetric ice content and volumetric water content of a representative slope in the Naba section changed during a freeze–thaw cycle. Based on the above results, the annual temperature range at the surface of the topsoil on the slope is 37.61 °C, and this thermal effect extends to a depth of 0–3 m. In the spring thaw, the volumetric water content of the surface layer increased from 8.45% in February to 19.34% in May, and further to 20.65% in July; therefore, it can be inferred that the shallow soil is still being replenished by snowmelt and rain. Freezing-thaw phase change, freezing-front migration and external water infiltration work together to control hydro-thermal transport in the slope; thus, a redistribution and local accumulation of liquid water occur below the residual frozen layer and under the shallow surface. The above results can serve as a reference for drainage design and as a means to prevent or control freeze–thaw damage to the slope of a highway in Xinjiang’s seasonally frozen area during the spring thaw. 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

Vegetation change in cryosphere-affected mountain basins reflects interacting climate and human pressures but their relative influence remains uncertain in the Upper Indus Basin. The novelty of this study is the integration of satellite vegetation, climate variables, human pressure indicators, residual attribution and diagnostic validation in a data-scarce high-mountain basin. We evaluated growing-season Normalized Difference Vegetation Index dynamics and associated drivers from 2001 to 2023 using trend analysis, correlation, Random Forest diagnostics, Sentinel 2 validation, and residual trend analysis. The results showed widespread greening across 96.59% of the basin, with stronger improvement in the lower and central areas. Significant greening covered 69.94% of the basin, while only 1.55% showed significant browning. Precipitation and temperature were predominantly positive drivers of vegetation change, whereas potential evapotranspiration and solar radiation were mostly negative. Soil moisture played a strong regulatory role along elevation gradients. Residual trend analysis provided approximate and method-dependent estimates of the possible anthropogenic influence on vegetation change at 73.09% and climatic drivers at 26.91% rather than direct causal decomposition. These values are approximate and method-dependent estimates, not direct causal decomposition. The findings highlight human-related greening in lower valleys and climate-controlled vegetation responses in high-mountain areas. Full article

This study investigates the synergistic regulation mechanism of water–heat–salt transport in the black soil of cold regions in Northeast China by combining field monitoring with HYDRUS-2D simulations. Four tillage treatments were evaluated: control group (CK), no-tillage with flat straw mulching (NM), ridge tillage with flat straw mulching (RM), and straw return with rotary tillage (RR). Monitoring data indicated that all straw incorporation treatments significantly improved soil moisture retention capacity. Compared with CK, soil water content under RM increased by 63.93% correspondingly; soil salinity in CK was 5.75–13.68% higher than that in straw-amended treatments. In addition, RM exerted a more prominent regulatory effect on soil temperature fluctuations relative to CK. Simulation results reveal that straw incorporation effectively reduces surface runoff, thereby substantially weakening the driving force for upward salt migration. Structural equation modeling (SEM) quantified path coefficients, revealing that straw incorporation optimizes the soil microenvironment. This integrated approach provides a mechanistic basis for black soil conservation in seasonally frozen regions, identifying RM as the optimal management practice to balance water retention and salt inhibition. Full article

Effective water management in plug trays is critical for high-quality young plant production. While soil moisture sensor-based irrigation can improve irrigation efficiency, its application to young plants is constrained by the small size and high density of plug tray cells, which hinder reliable moisture sensing. We developed a load cell-based automated irrigation system that estimates substrate volumetric water content (VWC) from plug tray weight dynamics and evaluated its applicability for plug production. The system continuously monitored tray weight and estimated VWC relative to a saturated reference tray weight, which was updated after each irrigation event to account for plant growth. Estimated VWC closely matched oven-dry measurements (R² = 0.9888, RMSE = 0.029 m³·m⁻³). In a trial with torenia, irrigation was regulated using three VWC thresholds (0.30, 0.45, and 0.60 m³·m⁻³) and compared with a daily irrigation regime. The system regulated irrigation according to the target thresholds, with the 0.45 m³·m⁻³ threshold achieving the best balance between plant growth and irrigation efficiency, indicating it as the optimal irrigation threshold for torenia cutting propagation. This approach provides a practical decision-support tool for precision irrigation and for determining crop-specific VWC thresholds, supporting growers in improving water-use efficiency while ensuring high propagation performance in plug production. Full article

The tomato leafminer, Tuta absoluta (Meyrick), is a globally invasive lepidopteran pest whose pupal stage commonly occurs within or at the soil surface. Soil moisture is a primary abiotic driver shaping soil microhabitats and therefore likely to influence pupation behavior, pupal survival and emergence. We quantified how six controlled soil moisture levels (0%, 20%, 40%, 60%, 80% and 100% saturation, wet-weight basis) affect pupation site selection, pupation depth, pupal survival and emergence under laboratory conditions, and performed a four-choice moisture preference assay. Larvae preferentially selected shallow soil layers (0–1 cm) and showed a marked behavioral preference for relatively low moisture (20–40%); maximum emergence (83%) occurred at 20% moisture. Both extreme drought (0%) and high saturation (≥80%) strongly reduced survival and emergence. These unimodal responses indicate that soil moisture constrains T. absoluta recruitment and may be exploited as a target for ecologically based control. The results provide a quantitative basis for soil-moisture-oriented IPM tactics and identify field validations and formulation/timing optimizations required to translate laboratory findings into practice. Full article

The purpose of this work is to investigate the use of high-resolution ($~100$ m) surface soil moisture (SSM) maps derived from Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify irrigation events occurring in the Riaza irrigation district (Castilla y León region, Spain) from 2017 to 2021. The proposed method is based on the application of the Constant False Alarm Rate (CFAR) algorithm, which is an adaptive and unsupervised thresholding algorithm traditionally used for target detection in SAR images. This algorithm uses a sliding window approach that allows an adaptive threshold estimate for each pixel of the image, depending on the distribution of the surrounding pixels. The analysis was carried out on fields cultivated with maize, sugar beet and sunflower. Results show that the Overall Accuracy (OA) of the detection mainly depends on the time span (TS) between the S-1 passage and the irrigation event, the acquisition timing and the development stage of the vegetation. Indeed, the OA reaches a mean of $78\%$ and $70\%$, respectively, for the $6$ a.m. and $6$ p.m. acquisitions, when the irrigation events occur within $36$ h before the S-1 passage, and it follows a downward trend as the TS increases. On the other hand, when the vegetation reaches the mature stage, the mean OA decreases respectively to $56\%$ and $52\%$. Stemming from the event detection, the study explored the estimation of the total irrigated area in the early growing season, showing promising agreement with in situ data, as evidenced by the low Relative Error ($E_r\approx 5.6\%$). Additionally, the analysis revealed a significant correlation between field-scale mean SSM and irrigation depths ($R=0.89$). Full article

Mulch films play a vital role in modern agriculture by enhancing soil hydrothermal conditions, suppressing weed growth, and improving crop performance. Across 13 major crops, mulching increased yields by an average of 26%, with particularly strong responses in soybean (44%), millet (42%), wheat (29%), and maize (25%), and improved water-use efficiency by up to 33%. However, conventional polyethylene (PE) mulch films accumulate persistently in soils, reaching 7183–10,586 microplastic particles/kg in topsoil after long-term use and contributing up to 56% of total microplastics across the 0–100 cm soil profile. These residues impair enzymatic activity, disrupt nutrient cycling, and alter microbial community structure, making biodegradable alternatives essential for mitigating these issues. Lignin-based biodegradable mulch films (BDMs) have gained increasing attention owing to lignin’s intrinsic UV-shielding capacity, mechanical reinforcement, hydrophobicity, and biodegradability. Lignin-containing films may block UV radiation below 300 nm, reduce visible-light transmittance by ~80%, exhibit thermal stability up to 150 °C, and demonstrate low water vapour permeability (3.41 × 10⁻⁸ g/m·h·Pa) depending on formulation and lignin loading. Incorporation of lignin may enhance biodegradability, increasing soil-burial degradation by 25.47% relative to pure PVA, with composite systems achieving ~55% degradation within 50 days. This review provides a comprehensive assessment of lignin structure, sources, chemistry, extraction methods. It examines lignin as a renewable and value-added feedstock for mulch applications, and critically evaluates the optical, mechanical, thermal, hydrophobic, and biodegradation properties of lignin-based BDMs. The review also discusses their agronomic applications, including weed suppression, soil moisture retention, nutrient management, and soil microclimate regulation, while analysing the economic considerations affecting large-scale implementation and commercial feasibility. Finally, it outlines key research priorities to enable scalable, field-reliable, and environmentally sustainable mulch film technologies. Full article

Intensive mining over recent decades has caused severe ground subsidence in mining regions, threatening safety and long-term sustainability. High-precision, continuous monitoring and prediction of subsidence are therefore urgently needed. Traditional methods—terrestrial surveying and GPS—offer limited coverage, sparse measurement points, high costs, and poor scalability, making them unsuitable for large-scale, long-term surface deformation monitoring. InSAR is widely used for ground deformation monitoring due to its wide-area coverage, long-term sampling, high spatial resolution, and millimeter-scale precision. However, conventional InSAR often fails in vegetated areas and under steep deformation gradients—common in mining zones. To overcome these limitations, this study applied SBAS-InSAR, a method better suited for large-magnitude, continuous subsidence monitoring in mining areas. This study proposed an enhanced hierarchical spatiotemporal dependency graph neural network (HSDGNN) integrated with a Long Short-Term Memory (LSTM) module to improve temporal feature representation. Using this model, this study predicted surface subsidence at the Dexing Copper Mine under environmental drivers. Key findings are as follows: (1) Surface subsidence exhibited pronounced spatial heterogeneity and strong temporal nonlinearity; major subsidence zones were localized in open-pit excavation areas and waste rock dumps, with peak subsidence rates reaching −126.121 mm/yr. (2) Precipitation and soil moisture emerged as the dominant environmental controls on subsidence, displaying distinct seasonal modulation and quantifiable lagged responses—up to several months—relative to subsidence onset. (3) The HSDGNN model achieved high predictive accuracy for both Mine 1 and Mine 2, attaining R² values of up to 0.9950. This work establishes a robust, scalable, and operationally viable framework for high-precision subsidence monitoring and forecasting in geologically and anthropogenically complex mining environments. Full article