Search Results (295)

Direct seeded rice, being less water- and labor-intensive, can be an alternative approach to conventional rice planting methods. However, uneven and poor stand establishment caused by deep sowing in the field is one of the major hurdles in the adoption of direct seeding technology. Varieties with the potential to emerge from deeper layers of soil may have a positive impact on crop establishment. To evaluate the behavior of ten rice cultivars against their potential to emerge from different soil depths (0, 2.5, and 5.0 cm), a pot experiment was conducted under semi-controlled conditions at the PARC Rice Programme, Kala Shah Kaku, Lahore. Data on different seedling parameters were collected. The results showed that the highest mean seedling emergence percentage (95%) was achieved by the tested genotypes at a 2.5 cm seeding depth, while surface sowing and placement of seeds at a 5 cm depth demonstrated a similar mean emergence percentage (89%). Seeding depth, genotypes, and their interactions significantly affected mean emergence time, mesocotyl and coleoptile lengths, and root and shoot lengths. Sowing seeds at a 5 cm depth increased mean emergence time by 28%. However, increasing sowing depth increased the coleoptile length, mesocotyl length, first leaf sheath length, and shoot length of rice seedlings. Mesocotyls and coleoptile lengths showed a linear relationship with mean emergence time. Mesocotyl and coleoptile are key structures of the apical–basal axis in grasses that elongate to facilitate the emergence of germinating seeds under deep sowing. The longest coleoptiles (1.47 cm) and mesocotyls (3.27 cm) were measured from seedlings sown at a depth of 5 cm. Among genotypes, PK-1121 exhibited maximum coleoptile elongation (2.10 cm) under deep sowing (5 cm), while the longest mesocotyls were recorded from deep-sown (5 cm) seedlings of Chenab Basmati. Root length was found to be inversely proportional to sowing depth. PK-1121 aromatic, Kisan Basmati, Punjab Basmati, and Chenab Basmati produced longer shoots (22.61, 23.37, 23.32, and 21.05 cm, respectively) and took a relatively short time for emergence when sown deep. These varieties may have better potential to emerge from deeper soil layers, which may have a positive impact on even germination and better crop stand establishment. Full article

Timely access to soil moisture conditions in farmland crops is the foundation and key to achieving precise irrigation. Due to their high spatiotemporal resolution, unmanned aerial vehicle (UAV) remote sensing has become an important method for monitoring soil moisture. This study addresses soil moisture retrieval in alfalfa fields across different growth stages. Based on UAV multispectral images, a multi-source feature set was constructed by integrating spectral and texture features. The performance of three machine learning models—random forest regression (RFR), K-nearest neighbors regression (KNN), and XG-Boost—as well as two ensemble learning models, Voting and Stacking, was systematically compared. The results indicate the following: (1) The integrated learning models generally outperform individual machine learning models, with the Voting model performing best across all growth stages, achieving a maximum R² of 0.874 and an RMSE of 0.005; among the machine learning models, the optimal model varies with growth stage, with XG-Boost being the best during the branching and early flowering stages (maximum R² of 0.836), while RFR performs better during the budding stage (R² of 0.790). (2) The fusion of multi-source features significantly improved inversion accuracy. Taking the Voting model as an example, the accuracy of the fused features (R² = 0.874) increased by 0.065 compared to using single-texture features (R² = 0.809), and the RMSE decreased from 0.012 to 0.005. (3) In terms of inversion depth, the optimal inversion depth for the branching stage and budding stage is 40–60 cm, while the optimal depth for the early flowering stage is 20–40 cm. In summary, the method that integrates multi-source feature fusion and ensemble learning significantly improves the accuracy and stability of alfalfa soil moisture inversion, providing an effective technical approach for precise water management of artificial grasslands in arid regions. Full article

Inland depression irrigation districts in the arid regions of Xinjiang, owing to the absence of natural drainage conditions, exhibit unique groundwater-salt dynamics and face prominent risks of soil salinization, thus necessitating clarification of their water-salt transport mechanisms to ensure sustainable agricultural development. This study takes the Karamay Agricultural Comprehensive Development Zone as the research subject. The study examines the distribution characteristics of soil salinity, groundwater depth, and Total Dissolved Solids (TDS) of groundwater across diverse soil textures, elucidates the correlative relationships between groundwater dynamics and soil salinity, and forecasts the evolutionary trajectory of groundwater levels within the irrigation district. The findings reveal that groundwater depth in silty soil regions (3.24–3.11 m) substantially exceeds that in silty clay regions (2.43–2.61 m), whereas TDS of groundwater demonstrates marginally elevated concentrations in silty clay areas (19.05–16.78 g L⁻¹) compared to silty soil zones (18.18–16.29 g L⁻¹). Soil salinity exhibits pronounced surface accumulation phenomena and considerable inter-annual seasonal variations: manifesting a “spring-peak, summer-trough” pattern in 2023, which inversely transitioned to a “summer-peak, spring-trough” configuration in 2024, with salinity hotspots predominantly concentrated in silty clay distribution zones. A significant sigmoid functional relationship emerges between soil salinity and groundwater depth (R² = 0.73–0.77), establishing critical depth thresholds of 2.44 m for silty soil and 2.72 m for silty clay, beneath which the risk of secondary salinization escalates dramatically. The XGBoost model demonstrates robust predictive capability for groundwater levels (R² = 0.8545, MAE = 0.4428, RMSE = 0.5174), with feature importance analysis identifying agricultural irrigation as the predominant influencing factor. Model projections indicate that mean groundwater depths across the irrigation district will decline to 2.91 m, 2.76 m, 2.62 m, and 2.36 m over the ensuing 1, 3, 5, and 10 years, respectively. Within a decade, 73.33% of silty soil regions and 92.31% of silty clay regions will experience groundwater levels below critical thresholds, subjecting the irrigation district to severe secondary salinization threats. Consequently, comprehensive mitigation strategies encompassing precision irrigation management and enhanced drainage infrastructure are imperative. Full article

Acoustic propagation in the ice cover of the Polar Ocean is of increasing interest from both scientific and engineering perspectives. The low-frequency elastic waves propagating in floating ice are primarily governed by waveguides stemming from the layered structure of the medium. For shallow water areas, experimental observation indicates that two Scholte-like waves are observed at low frequencies, i.e., the quasi-Scholte (QS) and Scholte–Stoneley (SS) waves, which are different from deep-sea cases. Due to the finite depths of ice, water, and sediment layers, both waves are dispersive. By modeling the waveguide of an ice-covered shallow-water (ICSW) system, the dispersion characteristics of both waves are derived, validated through numerical simulation, and analyzed with respect to layer structure for both soft and hard sediment. Results indicate a consistent conclusion; the QS wave exhibits a unique sensitivity to ice thickness, whereas the SS wave shows marginal sensitivity to ice thickness, and is controlled by the ratio of water depth to sediment depth, regardless of their absolute values. Based on these dispersion characteristics, a two-step inversion procedure is developed and applied to the synthetic signals from a numerical simulation. The conditional observability of the SS wave at the ice surface is also investigated and discussed. Full article

Polymer-assisted hot water flooding (PAHWF) is an important enhanced oil recovery technique involving strongly coupled thermal, chemical, and multiphase flow processes. Accurate prediction of water saturation, polymer concentration, and temperature evolution in PAHWF is challenging due to the highly nonlinear and multiscale governing equations. In this study, a physics-informed neural network (PINN) framework is developed for one-dimensional PAHWF simulation as a controlled benchmark system to systematically investigate PINN behavior in multiphysics-coupled problems. The PAHWF governing equations incorporating temperature- and concentration-dependent viscosity are embedded into the PINN loss function. Three progressively designed numerical examples are conducted to examine the effects of temperature normalization, network architecture (PINN-1 versus PINN-2), and network depth on training stability and solution accuracy. The results demonstrate that temperature normalization effectively mitigates gradient-scale imbalance, significantly improving convergence stability and prediction accuracy. Furthermore, the PINN-2 architecture, which employs a dedicated network for temperature, exhibits enhanced robustness and accuracy compared with the unified PINN-1 structure. Variations in network depth show limited influence on solution quality, indicating the inherent robustness of PINNs under the proposed framework. Although conventional numerical methods remain more efficient for one-dimensional forward problems, this study establishes a methodological foundation for extending PINNs to higher-dimensional, strongly coupled PAHWF simulations and inverse reservoir problems. The proposed framework provides insights into improving PINN trainability and reliability for complex enhanced oil recovery processes. Full article

Massive phytoplankton blooms threaten lake ecosystems, causing significant ecological and socio-economic damage. While remote sensing is vital for monitoring, the vertical stratification of algae influences light propagation and distorts remote sensing reflectance signals. This effect is particularly understudied in high-latitude lakes, leaving a gap in understanding phytoplankton biomass patterns. To address this, our study investigated three high-latitude water bodies: Lake Hulun, Fengman Reservoir, and Lake Khanka. We collected water samples from three depths based on total and euphotic zone depth and developed layer-specific inversion models for chlorophyll-a (Chal) and phycocyanin (PC) using a random forest algorithm. These models demonstrated strong performance and were applied to Sentinel-3 OLCI imagery from 2016–2024. Our results show that Chla generally decreases exponentially with depth, whereas PC exhibits a Gaussian-like vertical distribution with a pronounced subsurface maximum at approximately 1 m. In addition, a significant positive correlation between Chla and PC was observed in surface waters. In Lake Khanka, the northern basin exhibited a significant interannual increase in phytoplankton biomass. At 3 m, PC correlated negatively with turbidity and responded strongly to cyanobacterial blooms, while organic suspended matter correlated positively with Chla. This work establishes a robust framework for multilayer water quality monitoring in high-latitude lakes, providing critical insights for eutrophication management and cyanobacterial bloom early warning. Full article

Ground settlement is readily induced by shield–tail gaps formed during tunneling, where soil loss must be compensated through backfill grouting. However, improper grouting control may trigger tunnel uplift, segment misalignment, and, after solidification, problems such as voids, cracking, and water ingress. Ensuring construction safety and long-term serviceability requires both reliable detection of grouting effectiveness and a mechanistic understanding of grout diffusion. This review systematically synthesizes sensing technologies, diffusion modeling, and intelligent data interpretation. It highlights their interdependence and identifies emerging trends toward multimodal joint inversion and real-time grouting control. Non-destructive testing techniques can be broadly categorized into geophysical approaches and sensor-based methods. For synchronous detection, vehicle-mounted GPR systems and IoT-based monitoring platforms have been explored, although studies remain sparse. Theoretically, grout diffusion has been investigated via numerical simulation and field measurement, including the spherical diffusion theory, columnar diffusion theory, and sleeve-pipe permeation grouting theory. These theories decompose the diffusion process of the slurry into independent movements. Nevertheless, oversimplified models and sparse monitoring data hinder the development of universally applicable frameworks capable of capturing diverse engineering conditions. Existing techniques are further constrained by limited imaging resolution, insufficient detection depth, and poor adaptability to complex strata. Looking ahead, future research should integrate complementary non-destructive methods with numerical simulation and intelligent data analytics to achieve accurate inversion and dynamic monitoring of the entire process, ranging from grout diffusion and consolidation to defect evolution. Such efforts are expected to advance both synchronous grouting detection theory and intelligent and digital-twin tunnel construction. Full article

Accumulation of salts in irrigated soils can be detrimental not only to growing crops but also to groundwater quality. Soil salinity should be regularly monitored, and appropriate irrigation at the required leaching rate should be applied to prevent excessive salt accumulation in the root zone, thereby improving soil fertility and crop production. We combined two frequency domain electromagnetic induction (FD-EMI) mono-channel sensors (EM31 and EM38) and operated them at different heights and with different coil orientations to monitor the vertical distribution of soil salinity in a salt-affected irrigated area in Kairouan (central Tunisia). Multiple measurement heights and coil orientations were used to enhance depth sensitivity and thereby improve salinity predictions from this type of proximal sensor. The resulting multi-configuration FD-EMI datasets were used to derive soil salinity information via inverse modeling with a recently developed in-house laterally constrained inversion (LCI) approach. The collected apparent electrical conductivity (ECa) data were inverted to predict the spatial and temporal distribution of soil salinity. The results highlight several findings about the distribution of salinity in relation to different irrigation systems using brackish water, both in the short and long term. The expected transfer of salinity from the surface to deeper layers was systematically observed by our FD-EMI surveys. However, the intensity and spatial distribution of soil salinity varied between different crops, depending on the frequency and amount of drip or sprinkler irrigation. Furthermore, our results show that vertical salinity transfer is also influenced by the wet or dry season. The study provides insights into the effectiveness of combining two different FD-EMI sensors, EM31 and EM38, for monitoring soil salinity in agricultural areas, thereby contributing to the sustainability of irrigated agricultural production. The inversion approach provides a more detailed representation of soil salinity distribution across spatial and temporal scales at different depths, and across irrigation systems, compared to the classical method based on soil samples and laboratory analysis, which is a point-scale measurement. It provides a more extensive assessment of soil conditions at depths up to 4 m with different irrigation systems. For example, the influence of local drip irrigation was imaged, and the history of a non-irrigated plot was evaluated, confirming the potential of this method. Full article

The source region of the Yellow River on the Tibetan Plateau constitutes a critical ecological security barrier and a key water-conservation region, where permafrost dynamics exercise primary control over ecosystem stability and hydrological processes. Although observations document intensifying freeze–thaw processes under climate warming, the historical and future evolution of maximum freezing depth, abbreviated as MFD, in the source region of the Yellow River remains poorly constrained. Using ground-temperature and meteorological records from 15 stations for 1981–2014, we estimated MFD with a Stefan-type formulation, assessed trend significance using the Mann–Kendall test and Sen’s slope, and characterized changes through 2100 using CMIP6 projections under four shared socioeconomic pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. We found a strong inverse association between MFD and annual mean ground temperature, such that a 1 °C increase corresponds to an average decrease of approximately 13.2 cm. Historically, MFD has progressively shallowed and exhibits a clear meridional gradient—deeper in the north and shallower in the south; low-value zones declined from 0.75 to 0.50 m, whereas high-value zones decreased from 2.92 to 2.83 m. Across future scenarios, MFD continues to shallow; the strongest signal occurs under SSP5-8.5, yielding an additional decline of approximately 42 percent relative to the historical baseline, with degradation most pronounced at lower elevations. These findings provide actionable guidance for understanding ecohydrological processes and for water resource management in the source region of the Yellow River under climate warming. Full article

Substrate classification based on remote sensing images can provide fundamental data for offshore engineering construction and coastal ecological protection without in situ data. To address the inadequate consideration of features in existing substrate classification methods and to mitigate the low classification accuracy caused by noise in single-temporal images, this paper improves the existing remote sensing classification methods and proposes a shallow-water substrate classification method based on the fusion of multitemporal remote sensing images using a random forest model. The proposed method proposes and applies adaptive weighted fusion to bathymetric inversion results from single-temporal images for optimal water depth and terrain features. It derives and constructs a depth-independent bottom reflectance equation, uses median fusion to generate optimal images for accurate bottom reflectance and spectral features, then feeds selected features into the random forest model for training to produce final classification results. Three different types of shallow-water areas were selected for the experiments. The results show that the proposed method effectively leverages multiple features and capitalizes on multitemporal advantages to circumvent the limitations of single-temporal classification, the classification accuracy reaches over 90.34% on different substrates, the maximum misclassification rate is below 8.13% and the number of misclassified pixels is significantly less than that of single-temporal images. Compared with the four mainstream classification algorithms, the proposed method not only achieves overall accuracy (OA) and F1-score exceeding 0.94 and a kappa coefficient greater than 0.90, but demonstrates excellent detail preservation capability and strong adaptability to complex shallow-water environments, and delivers stable classification results. Full article

Geothermal energy, recognized as a sustainable and clean resource, is playing an increasingly critical role in the global shift toward low-carbon energy systems. Nevertheless, the exploitation of fractured geothermal reservoirs is often impeded by severe lost circulation during drilling, where conventional plugging materials fail under high-temperature, high-salinity, and high-pressure conditions due to inadequate mechanical strength, poor thermal resistance, and lack of self-adaptive sealing behavior. In response, self-healing materials have emerged as an innovative strategy for developing intelligent lost circulation control technologies. Herein, we report a novel self-healing gel (XFFD) synthesized via inverse emulsion polymerization using acrylamide (AM), acrylic acid (AA), p-nitroblue tetrazolium (PNBT), and modified silica nanoparticles (PAS). The resulting material exhibits exceptional thermal stability, with decomposition onset above 356 °C, as determined by thermogravimetric analysis. Rheological and mechanical assessments reveal outstanding viscoelasticity, moderate swelling capacity (4.17-fold in deionized water), and a high self-recovery efficiency of 91.15%, accompanied by a bearing strength of 3.65 MPa. Mechanistic investigations indicate that the autonomous repair capability stems from dynamic non-covalent interactions—primarily hydrogen bonding and ionic associations—enabled by amide and carboxyl groups within the polymer network. Sand bed filtration tests under simulated geothermal conditions (150 °C, 8% salinity) demonstrate that XFFD forms a robust sealing barrier with significantly shallower invasion depth compared to conventional materials such as sulfonated asphalt and calcium carbonate. This work presents an effective self-healing gel system that ensures reliable wellbore strengthening and fluid loss control in challenging high-temperature, high-salinity geothermal drilling operations. Full article

Wet-snow avalanches constitute a major geomorphic hazard in southeastern Tibet, where warm, humid climatic conditions and a steep, high-relief terrain generate failure mechanisms that are distinct from those in cold, dry snow environments. This study investigates the snowpack conditions underlying avalanche initiation in this region by integrating UAV-based multi-sensor surveys with field validation. Ground-penetrating radar (GPR), infrared thermography, and optical imaging were employed to characterize snow depth, stratigraphy, liquid water content (LWC), snow water equivalent (SWE), and surface temperature across an inaccessible avalanche channel. Calibration at representative wet-snow sites established an appropriate LWC inversion model and clarified the dielectric properties of avalanche-prone snow. Results revealed SWE up to 1092.98 mm and LWC exceeding 13.8%, well above the critical thresholds for wet-snow instability, alongside near-isothermal profiles and weak bonding at the snow–ground interface. Stratigraphic and UAV-based observations consistently showed poorly bonded, water-saturated snow layers with ice lenses. These findings provide new insights into the hydro-thermal controls of wet-snow avalanche release under monsoonal influence and demonstrate the value of UAV-based surveys for advancing the monitoring and early warning of snow-related hazards in high-relief mountain systems. Full article

Accurate nearshore bathymetry is an essential dataset for coastal modelling and coastal hazard management, but traditional surveys are expensive and dangerous to conduct in energetic surf zones. Remotely piloted aircraft (RPA) offer a flexible way to collect high spatial and temporal resolution bathymetric data. This study applies deliberately simple workflows with accessible instrumentation to compare video-based and spectral inversion techniques at two contrasting coastal settings: an exposed open beach with relative higher wave energy and turbidity, and a sheltered embayed beach with lower energy conditions. The video-based (UBathy) approach achieved lower errors (0.22–0.41 m RMSE) than the spectral approach (Stumpf) (0.30–0.71 m RMSE), confirming its strength in semi-turbid, low- to moderate-energy settings. Stumpf’s accuracy matched prior findings (~0.5 m errors in clear water) but declined with depth. Areas with sun glint areas and breaking waves are challenging but UBathy performed better in mixed wave conditions. While these errors are higher than traditional hydrographic surveys, they fall within expected RPA-derived ranges presenting opportunities for use in specific coastal management applications. Future improvements may come from reducing reliance on ground control and advancing deep learning-based hybrid methods to filter outliers and improve prediction accuracy on sub-optimal imagery caused by environmental conditions. Full article

This study, based on global AERONET observation data from 2023, employs a synergistic inversion algorithm that integrates aerosol optical, microphysical, and chemical properties to retrieve the global distribution of aerosol parameters. We find that the global annual mean aerosol optical depth (AOD), fine-mode AOD (AOD_f), coarse-mode AOD (AOD_c), absorbing aerosol optical depth (AAOD), single scattering albedo (SSA) are 0.20, 0.15, 0.04, 0.024, and 0.87, respectively. From the perspective of spatial distribution, in densely populated urban areas, AOD is mainly determined by AOD_f, while in the areas dominated by natural sources, AOD_c contributes more. Combined with the optical and microphysical properties, fine-mode aerosols dominate optical contributions, whereas coarse-mode aerosols dominate volume contributions. In terms of chemical components, fine-mode aerosols at most global sites are primarily carbonaceous. The mass concentrations of black carbon (BC) exceed 10 mg m⁻² in parts of South Asia, Southeast Asia, and the Arabian Peninsula, while the mass fraction of brown carbon (BrC) accounts for more than 16% in regions such as the Sahara, Western Africa, and the North Atlantic Ocean reference areas. The dust (DU) dominates in coarse mode, with the annual mean DU fraction reaching 86.07% in the Sahara. In coastal and humid regions, the sea salt (SS) and water content (AW_c) contribute significantly to the aerosol mass, with fractions reaching 13.13% and 34.39%. The comparison of aerosol properties in the hemispheres reveals that the aerosol loading in the Northern Hemisphere caused by human activities is higher than in the Southern Hemisphere, and the absorption properties are also stronger. We also find that the uneven distribution of global observation sites leads to a significant underestimation of aerosol absorption and coarse-mode features in global mean values, highlighting the adverse impact of observational imbalance on the assessment of global aerosol properties. By combining analyses of aerosol optical, microphysical, and chemical properties, our study offers a quantitative foundation for understanding the spatiotemporal distribution of global aerosols and their emission contributions, providing valuable insights for climate change assessment and air quality research. Full article

Water scarcity in semi-arid regions necessitates accurate soil water modeling to optimize irrigation management. This study compares three HYDRUS-1D parameterization approaches—based on the drying-branch soil water retention curve (SWRC), wetting-branch SWRC (using Shani’s drip method), and inverse modeling—to simulating soil water content at 15 cm and 45 cm depths under center-pivot irrigation in a semi-arid region. Field experiments in three maize fields provided daily soil water, soil hydraulic, and meteorological data. Inverse modeling achieved the highest accuracy (NRMSE: 2.29–7.40%; RMSE: 0.006–0.023 cm³ cm⁻³), particularly at 15 cm depth, by calibrating van Genuchten parameters against observed water content. The wetting-branch approach outperformed the drying branch at the same depth, capturing irrigation-induced wetting processes more effectively. Statistical validation confirmed the robustness of inverse modeling in reproducing temporal patterns, while wetting-branch data improved deep-layer accuracy. The results demonstrate that inverse modeling is a reliable approach for soil water simulation and irrigation management, whereas the wetting-branch parameterization offers a practical, field-adaptable alternative. This study provides one of the first side-by-side evaluations of these three modeling approaches under real-world semi-arid conditions. Full article