Search Results (2,672)

Soil piping is the process by which subsurface water creates and enlarges channels, or “pipes,” within soil, enabling rapid and preferential flow beneath the surface. The collapse of these eroded pipes can lead to land degradation, gully formation, and potential damage to overlying infrastructure. While the structural consequences of pipe collapse are well recognized, there is limited understanding of the factors controlling pipe collapse and how water within the pipe influences moisture levels within a slope. This study used physical models of unsaturated slopes to examine how compaction conditions, pipe characteristics, and hydraulic conditions affect the progression of internal erosion. Models were created with different initial pipe sizes, moisture contents, densities at compaction and levels of pipe connectivity. Volumetric water content (VWC) sensors and cameras were used to monitor the slope response to subsurface flow, and measurements of pipe geometry were collected after the tests. Results showed that lower initial soil water content was more susceptible to pipe collapse, while higher water content showed improved pipe stability and sustained preferential flow. Fully connected pipes grew through erosion due to the pipe flow, while disconnected pipes grew mainly through local pipe collapse. Hydraulic equilibrium and soil erodibility affected the final pipe morphology more than the initial pipe size. These experimental results demonstrate that soil fabric and hydraulic connectivity of the pipe control the progression of piping, likelihood of collapse, and movement of water within the soil matrix. Full article

Surface soil moisture (SSM) plays a critical role in climate change, hydrological processes, and agricultural production. Decision trees and deep learning are widely applied to SSM retrieval. The former excels in interpretability while the latter outperforms in generalization, neither, however, integrates both. To address this issue, an attention decision forest (ADF) was developed, comprising feature extractor, soft decision tree, and tree-attention modules. The feature extractor projects raw inputs into a high-dimensional space to reveal nonlinear relationships. The soft decision tree preserves the advantages of tree models in nonlinear partitioning and local feature interaction. The tree-attention module integrates outputs from the soft tree’s subtrees to enhance overall fitting and generalization. Experiments on conterminous United States (CONUS) watershed dataset demonstrate that, upon sample-based validation, ADF outperforms traditional models with an R² of 0.868 and a ubRMSE of 0.041 m³/m³. Further spatiotemporal independent testing demonstrated the robust performance of this method, with R² of 0.643 and0.673, and ubRMSE of 0.062 and 0.065 m³/m³. Furthermore, an evaluation of the interpretability of the ADF using the Shapley Additive Interpretative Model (SHAP) revealed that the ADF was more stable than deep learning methods (e.g., DNN) and comparable to tree-based ensemble learning methods (e.g., RF and XGBoost). Both the ADF and ensemble learning methods demonstrated that, at large scales, spatiotemporal variation had the greatest impact on the SSM, followed by environmental conditions and soil properties. Moreover, the superior spatial SSM maps produced by ADF, compared with GSSM, SMAP L4 and ERA5-Land, further demonstrate ADF’s capability for large-scale mapping. ADF thus offers a novel architecture capable of integrating prediction accuracy, generalization, and interpretability. Full article

To improve the physical consistency and interpretability of traditional drought indices, this study proposes a drought assessment model that couples physically based constraints with data-driven approaches, leading to the development of a Multivariate Drought Index (MDI). The model employs convolutional neural networks to achieve physically consistent downscaling, thereby obtaining a high-resolution Normalized Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI). Objective weights are determined using the Criteria Importance Through Intercriteria Correlation method, while random forest and Shapley Additive Explanations are integrated for nonlinear interpretation and physics-guided calibration, forming an ensemble framework that incorporates multi-source and multi-scale factors. Validation with multi-source data from 2000 to 2024 in the major maize-growing areas of Heilongjiang Province demonstrates that MDI outperforms single indices and the Vegetation Health Index (VHI), achieving a correlation coefficient (r = 0.87), coefficient of determination (R² = 0.87), RMSE (0.08), and classification accuracy (87.4%). During representative drought events, MDI identifies signals 16–20 days earlier than the Standardized Precipitation Evapotranspiration Index (SPEI) and the Soil Moisture Condition Index (SMCI), and effectively captures localized drought patches at a 250 m scale. Feature importance analysis indicates that the NDWI and TVDI are consistently identified as dominant factors across all three methods, aligning physically interpretable analysis with statistical contribution. Long-term risk zoning reveals that the central–western region of the study area constitutes a high-risk zone, accounting for 42.6% of the total. This study overcomes the limitations of single indices by integrating physical consistency with the advantages of data-driven methods, achieving refined spatiotemporal characterization and enhanced overall performance, while also demonstrating potential for application across different crops and regions. Full article

Recently, there has been significant progress in the area of semantic classification of water bodies at global scales with deep learning. For the key purposes of water inventory and change detection, advanced deep learning classifiers such as UNets and Vision Transformers have been shown to be both accurate and flexible when applied to large-scale, or even global, satellite image datasets from optical (e.g., Sentinel-2) and radar sensors (e.g., Sentinel-1). Most of this work is conducted with optical sensors, which usually have better image quality, but their obvious limitation is cloud cover, which is why radar imagery is an important complementary dataset. However, radar imagery is generally more sensitive to soil moisture than optical data. Furthermore, topography and wind-ripple effects can alter the reflected intensity of radar waves, which can induce errors in water classification models that fundamentally rely on the fact that water is darker than the surrounding landscape. In this paper, we develop a solution to the use of Sentinel-1 radar images for the semantic classification of water bodies that uses style transfer with multi-modal and multi-temporal image fusion. Instead of developing new semantic classification models that work directly on Sentinel-1 images, we develop a global style transfer model that produces synthetic Sentinel-2 images from Sentinel-1 input. The resulting synthetic Sentinel-2 imagery can then be classified with existing models. This has the advantage of obviating the need for large volumes of manually labeled Sentinel-1 water masks. Next, we show that fusing an 8-year cloud-free composite of the near-infrared band 8 of Sentinel-2 to the input Sentinel-1 image improves the classification performance. Style transfer models were trained and validated with global scale data covering the years 2017 to 2024, and include every month of the year. When tested against a global independent benchmark, S1S2-Water, the semantic classifications produced from our synthetic imagery show a marked improvement with the use of image fusion. When we use only Sentinel-1 data, we find an overall IoU (Intersection over Union) score of 0.70, but when we add image fusion, the overall IoU score rises to 0.93. Full article

To investigate differences in water and salt transport during irrigation, freezing, and thawing periods in typical saline-affected paddy fields and saline-affected upland fields, field-based automated in situ monitoring was conducted in both types of saline-affected farmland (May 2023 to May 2024). Correlation analysis identified seasonal drivers of water–salt migration, while the HYDRUS-3D model simulated transport and equilibrium processes. The HYDRUS-3D model, equipped with a freeze–thaw module, accurately simulated complex water–salt transport in cold arid regions. Key findings include: (1) During freeze–thaw periods, soil moisture content and electrical conductivity (Ec) increased with the retreating frost front in both upland and paddy soils. During the irrigation period, maximum soil moisture content and Ec values occurred at 80 cm depth in dryland soils and 60 cm depth in paddy soils, primarily influenced by irrigation and capillary rise. (2) Groundwater salt ions significantly affected soil salinization in both farmland types. During the freeze–thaw period, Ec positively correlated with soil temperature. During the irrigation period, Ec positively correlated with evapotranspiration and negatively correlated with precipitation. (3) Salt changes during the irrigation, freezing, and thawing periods were −565.4, 326.85, and 376.55 kg/ha for upland fields, respectively; corresponding changes for paddy fields were −1217.0, 280.07, and 299.35 kg/ha. (4) Both land types exhibited reduced salinity during the irrigation period, with paddy fields showing a reduction 3.36 times greater than dryland fields. During the freezing and thawing periods, both land types experienced salinity accumulation, with dryland fields accumulating higher salinity levels than paddy fields. These results indicate that paddy field irrigation and drainage systems help mitigate salinization, while dryland fields are more prone to springtime salt accumulation. These findings provide a basis for developing targeted management strategies for saline–alkali soils. Full article

Accurate profiling of deep (20–300 cm) soil moisture is crucial for precision irrigation but remains technically challenging and costly at operational scales. We systematically benchmark eight regression algorithms—including linear regression, Lasso, Ridge, elastic net, support vector regression, multi-layer perceptron (MLP), random forest (RF), and gradient boosting trees (GBDT)—that use easily accessible inputs of 0–20 cm surface soil moisture (SSM) and ten meteorological variables to non-invasively infer soil moisture at fourteen 20 cm layers. Data from a typical agricultural site in Wenxi, Shanxi (2020–2022), were divided into training and testing datasets based on temporal order (2020–2021 for training, 2022 for testing) and standardized prior to modeling. Across depths, non-linear ensemble models significantly outperform linear baselines. Ridge Regression achieves the highest accuracy at 0–20 cm, SVR performs best at 20–40 cm, and MLP yields consistently optimal performance across deep layers from 60 cm to 300 cm (R² = 0.895–0.978, KGE = 0.826–0.985). Although ensemble models like RF and GBDT exhibit strong fitting ability, their generalization performance under temporal validation is relatively limited. Model interpretability combining SHAP, PDP, and ALE shows that surface soil moisture is the dominant predictor across all depths, with a clear attenuation trend and a critical transition zone between 160 and 200 cm. Precipitation and humidity primarily drive shallow to mid-layers (20–140 cm), whereas temperature variables gain relative importance in deeper profiles (200–300 cm). ALE analysis eliminates feature correlation biases while maintaining high predictive accuracy, confirming surface-to-deep information transmission mechanisms. We propose a depth-adaptive modeling strategy by assigning the best-performing model at each soil layer, enabling practical non-invasive deep soil moisture prediction for precision irrigation and water resource management. Full article

Accurate numerical simulations of soil-tire interactions are essential for optimizing agricultural machinery to minimize soil compaction and enhance crop yield. This study developed and compared two approaches for identifying and validating parameters of a LS-Dyna soil model. The laboratory-based approach derives parameters from triaxial, consolidation, and cone penetrometer tests (CPT), while the optimization-based method refines them using in-situ CPT data via LS-OPT to better capture field variability. Simulations employing Multi-Material Arbitrary Lagrangian–Eulerian (MM-ALE), Smoothed Particle Hydrodynamics (SPH), and Hybrid-SPH methods demonstrate that Hybrid-SPH achieves the optimal balance of accuracy (2% error post-optimization) and efficiency (14-h runtime vs. 22 h for SPH). Optimized parameters improve soil–tire interaction predictions, including net traction and tire sinkage across slip ratios from −10% to 30% (e.g., sinkage of 12.5 mm vs. 11.1 mm experimental at 30% slip, with overall mean-absolute percentage error (MAPE) reduced to 3.5% for sinkage and 4.2% for traction) and rut profiles, outperforming lab-derived values. This framework highlights the value of field-calibrated optimization for sustainable agriculture, offering a cost-effective alternative to field trials for designing low-compaction equipment and reducing yield losses from soil degradation. While sandy loam soil at 0.4% moisture content was used in this study, future extensions to different soil types with varied moisture are recommended. Full article

Photosynthesis drives terrestrial carbon uptake, yet its diurnal dynamics remain poorly resolved due to the sparse availability of flux towers and the coarse spatial resolution of current satellite observations. Solar-induced chlorophyll fluorescence (SIF) provides a direct proxy of carbon uptake, but the existing global monthly mean diurnal total canopy SIF product is limited to 0.5° resolution. We developed a random forest-based downscaling framework to generate a global monthly mean hourly SIF dataset (SIF_{total_01}) at 0.1° resolution for 2000–2022. When validated against eddy-covariance-based gross primary productivity (GPP) data, SIF_{total_01} showed a strong correlation (R² = 0.81) and reduced root mean square error when compared with SIF_total (2.89→2.8 mW m⁻² nm⁻¹), providing notable gains in broadleaved forests (R²: 0.80→0.88 with a root mean square error of 2.32→1.81 mW m⁻² nm⁻¹). The SIF_{total_01} dataset revealed a distinct double-peak in the SIF_{total_01}–GPP slope, reflecting widespread afternoon depression of photosynthesis, with normalized slopes declining from 1.03 in the morning to 0.98 in the afternoon. Soil moisture modulated this depression pattern, as the afternoon–morning SIF_{total_01} difference increased from 0.02 to 0.10 mW m⁻² nm⁻¹ across dry to wet years. Under water stress, SIF yield was more sensitive than absorbed photosynthetic active radiation (APAR), with a doubling of the afternoon–morning SIF yield difference (0.5→1.1 10⁻³ nm⁻¹), while the afternoon–morning APAR difference showed a smaller change (−300→−180 kJ m⁻²). This study improves the potential for bridging observational gaps and constraining models offer valuable insights for fundamental and applied research in the analysis of ecosystem productivity, climate-carbon feedbacks, and vegetation stress. Full article

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

The electromagnetic induction (EMI) and frequency domain reflectometry (FDR) sensors, which measure the soil apparent electrical conductivity (ECa) in situ, have emerged as efficient and rapid tools for the indirect assessment of soil salinity, conventionally determined by the electrical conductivity of the saturated soil paste extract (ECe). However, the limitations of applying a single soil sensor and the ECa dependence on multiple soil properties, such as soil moisture and texture, can hinder the interpretation of ECe, whereas selecting the most appropriate set of sensors is challenging. To address these issues, this study explored the prediction ability of a noninvasive EM38-MK2 (EMI) and a capacitance dielectric WET-2 probe (FDR) in assessing topsoil salinity and texture within 0–30 cm depth across diverse soil and land-use conditions in Laconia, Greece. To this aim, multiple linear regression models of laboratory-estimated ECe and soil texture were constructed by the in situ measurements of EM38-MK2 and WET-2, and their performances were individually evaluated using statistical metrics. As was shown, in heterogeneous soils with sufficient wetness and high salinity levels, both sensors produced models with high adjusted coefficients of determination (adj. R² > 0.82) and low root mean square error (RMSE) and mean absolute error (MAE), indicating strong model fit and reliable estimations of topsoil salinity. For the EM38-MK2, model accuracy improved when clay was included in the regression, while for the WET-2, the soil pore water electrical conductivity (ECp) was the most accurate predictor. The drying soil surface was the greatest constraint to both sensors’ predictive performances, whereas in non-saline soils, the silt and sand were moderately assessed by the EM38-MK2 readings (0.49 < adj. R² < 0.51). The results revealed that a complementary use of the contemporary EM38-MK2 and the low-cost WET-2 could provide an enhanced interpretation of the soil properties in the topsoil without the need for additional data acquisition, although more dense soil measurements are recommended. Full article

Soils in arid regions are characterized by elevated salinity levels. During the process of soil moisture evaporation, salts are transported with water to the surface, resulting in the formation of salt crusts. Although these crusts significantly impact soil moisture evaporation, there is a paucity of systematic quantitative research concerning their formation mechanisms, dynamic evolution patterns, and effects on evaporation. To elucidate the mechanisms by which salt crusts influence soil moisture evaporation, this study conducted evaporation experiments utilizing brine soil columns. Various thicknesses of sand mulching (1 cm, 2 cm, 3 cm, 4 cm, 5 cm, and 6 cm) were applied to the top of the soil columns to generate different forms of NaCl salt crusts. Observations of soil column water evaporation rates, salt crust coverage (SCC), and salt crust morphology were conducted to analyze the effects of salt crust formation on soil water evaporation. The results indicate that the morphology and coverage of NaCl salt crusts significantly influence soil moisture evaporation. A crusty salt crust with high coverage impedes soil moisture evaporation; a patchy salt crust with moderate coverage may promote evaporation; the absence of a crust on the surface has a relatively weak effect on soil moisture evaporation. Nevertheless, the development of ‘salt trees’ within the soil profile can increase soil evaporation. These findings challenge the conventional understanding that “salt inhibits evaporation,” providing essential mechanistic parameters for accurately quantifying evaporation fluxes in saline soils and enhancing regional water cycle models, particularly the module related to atmosphere–soil water vapor exchange. Full article

Laboratory incubation experiments were conducted to investigate the effects of coal fly ash (FA) amendment (0%, 2.5%, 7.5%, and 15%) and moisture regimes (40%, 70%, and 100% water holding capacity (WHC)) on the mineralization of carbon (C) in an acidic agricultural soil. The results showed that the soil C mineralization intensity initially increased and subsequently decreased throughout the incubation period, with the mineralization dynamics well described by the first-order kinetic model (0.9633 ≤ R² ≤ 0.9972). Carbon mineralization increased with the application rate of FA, while moisture effect followed the order 70% WHC > 100% WHC > 40% WHC. Indicators showing highly significant correlations with total C mineralization amount included FA application rate, pH, water-soluble organic carbon, (WSOC) and cellulase (CEL) activity. Specific bacterial (Acidobacteriota, Gemmatimonadota, Pseudomonadota, and Actinobacteriota) and fungal phyla (Chytridiomycota, Glomeromycota, and Olpidiomycota) exhibited stronger correlations with C mineralization. The microbial taxa exhibiting significant responses to FA and moisture conditions were not consistent. Although the addition of high proportions of FA, especially with adequate moisture conditions, can enhance soil microbial activity and C mineralization, the potential risks of soil C loss and the accumulation of toxic elements necessitate the prudent implementation of elevated FA application rates in practical scenarios. Full article

Soil respiration (Rs), the second largest carbon flux in terrestrial ecosystems, critically regulates the turnover of soil carbon pools. However, its seasonal and annual responses to extreme events in monsoon forests remain unclear. This study used a continuous multichannel automated chamber system to monitor Rs over three years of drought (2019–2021) in an Asian monsoon forest in Taiwan. We assessed seasonal and annual Rs patterns and examined how drought influenced autotrophic (Rr) and heterotrophic (Rh) respiration through changes in soil temperature and moisture. Results showed Rs declined from 5.20 ± 2.08 to 3.86 ± 1.20 μmol CO₂ m⁻² s⁻¹, and Rh from 3.36 ± 1.21 to 3.15 ± 0.98 μmol CO₂ m⁻² s⁻¹ over the study period. Spring Rr values dropped significantly—by 29.3% in 2020 and 62.2% in 2021 compared to 2019 (p < 0.05), while Rh remained unchanged (p > 0.05). These results suggest that spring drought strongly suppresses autotrophic respiration but has minimal effect on Rh. Incorporating these dynamics into carbon models could improve predictions of carbon cycling under climate change. Our findings demonstrate that spring drought exerts a strong influence on soil carbon fluxes in Asian monsoon forests. Full article

Land Surface Temperature (LST) is a critical variable for understanding land–atmosphere interactions and is widely applied in urban heat monitoring, evapotranspiration estimation, near-surface air temperature modeling, soil moisture assessment, and climate studies. MODIS LST products, with their global coverage, long-term consistency, and radiometric calibration, are a major source of LST data. However, frequent data gaps caused by cloud contamination and atmospheric interference severely limit their applicability in analyses requiring high spatiotemporal continuity. This study presents a seamless MODIS LST reconstruction framework that integrates multi-source data fusion and a multi-stage optimization strategy. The method consists of three key components: (1) topography- and land cover-constrained spatial interpolation, which preliminarily fills orbit-induced gaps using elevation and land cover similarity criteria; (2) pixel-level LST reconstruction via random forest (RF) modeling with multi-source predictors (e.g., NDVI, NDWI, surface reflectance, DEM, land cover), coupled with HANTS-based temporal smoothing to enhance temporal consistency and seasonal fidelity; and (3) Poisson-based image fusion, which ensures spatial continuity and smooth transitions without compromising temperature gradients. Experiments conducted over two representative regions—Huainan and Jining—demonstrate the superior performance of the proposed method under both daytime and nighttime scenarios. The integrated approach (Step 3) achieves high accuracy, with correlation coefficients (CCs) exceeding 0.95 and root mean square errors (RMSEs) below 2K, outperforming conventional HANTS and standalone interpolation methods. Cross-validation with high-resolution Landsat LST further confirms the method’s ability to retain spatial detail and cross-scale consistency. Overall, this study offers a robust and generalizable solution for reconstructing MODIS LST with high spatial and temporal fidelity. The framework holds strong potential for broad applications in land surface process modeling, regional climate studies, and urban thermal environment analysis. Full article

The sustainability of Arctic ecosystems that are extremely vulnerable is contingent upon the state of cryosoils. Understanding the principles of ecosystem stability in permafrost conditions, particularly under external natural or human-induced influences, necessitates an examination of the thermal and moisture regimes of the seasonally thawed soil layer. The study concentrated on the variability in the soil’s thermophysical properties in Central Siberia’s permafrost zone (the northern part of Krasnoyarsk Region, Taimyr, Russia). In the industrially affected area of interest, we evaluated and contrasted the differences in the thermophysical properties of soils between two opposing types of landscapes. On the one hand, these are soils that are characteristic of the natural landscape of flat shrub tundra, with a well-developed moss–lichen cover. An alternative is the soils in the landscape, which have exhibited significant degradation in the vegetation cover due to both natural and human-induced factors. The heat-insulating properties of background areas are controlled by the layer of moss and shrubs, while its disturbance determines the excessive heating of the soil at depth. In comparison to the background soil characteristics, degradation of on-ground vegetation causes the active layer depth of the soils to double and the temperature gradient to decrease. With respect to depth, we examine the changes in soil temperature and heat flow dynamics (q, W/m²). The ranges of thermal conductivity (λ, W/(m∙K)) were assessed using field-measured temperature profiles and heat flux values in the soil layers. The background soil was discovered to have lower thermal conductivity values, which are typical of organic matter, in comparison to the soil of the transformed landscape. Thermal diffusivity coefficients for soil layers were calculated using long-term temperature monitoring data. It is shown that it is possible to use an adjusted model of the thermal conductivity coefficient to reconstruct the dynamics of moisture content from temperature dynamics data. A satisfactory agreement is shown when the estimated ($W_{calc}$, %) and observed ($W_{exp}$, %) moisture content values in the soil layer are compared. The findings will be employed to regulate the effects on landscapes in order to implement sustainable nature management in the region, thereby preventing the significant degradation of ecosystems and the concomitant risks to human well-being. Full article