Search Results (641)

Geogrids are frequently utilized in engineering for reinforcement; yet, they are vulnerable to construction damage when employed on coarse-grained soil subgrades. In contrast, waste tire grids are more appropriate for subgrade reinforcement owing to their rough surfaces, integrated steel meshes, robust transverse ribs, extended degradation cycles, and superior durability. Based on the limit equilibrium theory, this study developed formulae for calculating the internal and external frictional resistance, as well as the end resistance of waste tires, to ascertain the interface bearing properties and calculation techniques of waste tire grids. Based on this, a mechanical model for the ultimate pull-out resistance of waste-tire-reinforced soil was developed, and its validity was confirmed through a series of pull-out tests on single-sided strips, double-sided strips, and tire grids. The results indicated that the tensile strength of one side of the strip was approximately 43% of that of both sides, and the rough outer surface of the tire significantly enhanced the tensile performance of the strip; under identical normal stress, the tensile strength of the single-sided tire grid was roughly nine times and four times greater than that of the single-sided and double-sided strips, respectively, and the grid structure exhibited superior anti-deformation capabilities compared to the strip structure. The average discrepancy between the calculated values of the established model and the theoretical values was merely 2.38% (maximum error < 5%). Overall, this research offers technical assistance for ensuring the safety of subgrade design and promoting environmental sustainability in engineering, enabling the effective utilization of waste tire grids in sustainable reinforcement applications. Full article

Global interest in nematode communities and their ecological relationships as unique and complex soil ecosystems has remarkably increased in recent years. As they have a representative role in the soil biota, nematodes present great potential to help understand soil health through analyzing their food chains in different environments. The objective of this study was to analyze the spatial and dynamic distributions of nematode communities and soil properties in two riparian areas of the Caatinga biome: one with native vegetation and the other with a history of agricultural use (modified). The study was carried out in a semi-arid region of Brazil in Parnamirim, PE. In both areas, sampling grids of 60 m × 40 m were established to obtain data on soil moisture, organic matter, particle size, electrical conductivity, and pH, as well as metabolic activity and ecological indices of nematode communities. There was a greater abundance and diversity of nematodes in riparian soils with native vegetation compared to in the modified area due to agricultural use and the dominance of exotic and invasive species. In both areas, bacterivores and plant-parasitic nematodes were dominant, with the genus Acrobeles and Tylenchorhynchus as the main contributors to the community. In the modified area, soil variables (fine sand, clay, and pH) positively influenced Fu4 and PP4 guilds, while in the area with native vegetation, moisture and organic matter exerted a greater influence on Om4, PP5, and Ba3 guilds. Kriging maps showed the soil variables were more concentrated in the center in the areas with native vegetation, in contrast to the area with modified vegetation, where they concentrated more on the margins. The functional guilds in the native vegetation did not exhibit a gradual increase towards the regions close to the riverbank, unlike in the modified area. The presence of plant-parasitic nematodes, especially of the genus Tylenchorhynchus, indicates the need for greater attention in the management of these ecosystems. The study contributes to understanding the interactions between nematode communities and soil in riparian areas of the Caatinga biome, emphasizing the importance of preserving native vegetation to maintain the diversity and balance of this ecosystem, in addition to highlighting the need for appropriate management practices in areas with a history of agricultural use, aiming to conserve soil biodiversity. Full article

The agricultural sector faces significant sustainability, productivity, and environmental impact challenges. In this context, geographic information systems (GISs) have become a key tool to optimize resource management and make informed decisions based on spatial data. These data support planning the best cotton planting and harvest dates based on agroclimatic conditions, such as temperature, precipitation, and soil type, as well as identifying areas with a lower risk of water or thermal stress. As a result, cotton productivity is optimized, and costs associated with supplementary irrigation or losses due to adverse conditions are reduced. However, data from automatic weather stations in Mexico are scarce and incomplete. Instead, grid meteorological databases (DMM, in Spanish) were used with daily temperature and precipitation data from 1983 to 2020 to determine the heat units (HUs) for each cotton crop development stage; daily and accumulated HU; minimum, mean, and maximum temperatures; and mean annual precipitation. This information was used to determine areas that comply with environmental, geographic, and regulatory conditions (NOM-059-SEMARNAT-2010, NOM-026-SAG/FITO-2014) to delimit areas with agricultural potential for planting genetically modified (GM) cotton. The methodology made it possible to produce thirty-four maps at a 1:250,000 scale and a digital GIS with 95% accuracy. These maps indicate whether a given agricultural parcel is optimal for cultivating GM cotton. Full article

Reliable gridded precipitation products (GPPs) are essential for effective hydrological simulations, particularly in mountainous regions with limited ground-based observations. This study evaluates the performance of two widely used GPPs, CHIRPS and ERA5, in estimating precipitation and supporting runoff generation using the Soil and Water Assessment Tool (SWAT) across three headwater catchments (Sill, Drava and Isel) in the Austrian Alps from 1991 to 2018. The region’s complex topography and climatic variability present a rigorous test for GPP application. The evaluation methods combined point-to-point comparisons with gauge observations and assessments of generated runoff and runoff trends at annual, seasonal and monthly scales. CHIRPS showed a lower precipitation error (RMAE = 25%) and generated more consistent runoff results (RMAE = 12%), particularly in smaller catchments, whereas ERA5 showed higher spatial consistency but higher overall precipitation bias (RMAE = 37%). Although both datasets successfully reproduced the seasonal runoff regime, CHIRPS outperformed ERA5 in trend detection and monthly runoff estimates. Both GPPs systematically overestimate annual and seasonal precipitation amounts, especially at lower elevations and during the cold season. The results highlight the critical influence of GPP spatial resolution and its alignment with catchment morphology on model performance. While both products are viable alternatives to observed precipitation, CHIRPS is recommended for hydrological modelling in smaller, topographically complex alpine catchments due to its higher spatial resolution. Despite its higher precipitation bias, ERA5’s superior correlation with observations suggests strong potential for improved model performance if bias correction techniques are applied. The findings emphasize the importance of selecting GPPs based on the scale and geomorphological and climatic conditions of the study area. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

Geological surveys have vital importance at the planning stage of dammed reservoir construction projects. The results of these surveys determine the majority of the technical solutions adopted in the construction design to ensure the proper safety and stability parameters of the structure during water damming. Where the ground type is found to be different from what is expected, the construction project may be delayed or even cancelled. This study analyses issues and design modifications caused by the identification of different soil conditions during the construction of four new flood control reservoirs in the Nysa Kłodzka River basin in south-western Poland. The key findings are as follows: (1) a higher density of exploratory boreholes in areas with potentially fractured rock mass is essential for selecting the appropriate anti-filtration protection; (2) when deciding to apply deep piles, it is reasonable to verify, at the planning stage, whether they can be installed using the given technology directly at the planned site; (3) inaccurate identification of foundation soils under the dam body can lead to significant design modifications—in contrast, a denser borehole grid helps to determine the precise elevation of the base layer, which is essential for reliably estimating the volume of material required for the embankment; (4) in order to correctly assess the soil deposits located, for instance, in the reservoir basin area, it is more effective to use test excavations rather than relying solely on borehole-based investigations—as a last resort, test excavations can be used to supplement the latter. Full article

Multispectral images obtained from Unmanned Aerial Vehicles (UAVs) have become strategic tools in precision agriculture, particularly for analyzing spatial variability in soil attributes. This study aimed to evaluate the spatial distribution of soil electrical (EC) and total organic carbon (TOC) in irrigated forage cactus areas in the Brazilian semiarid region, using field measurements and UAV-based multispectral imagery. The study was conducted in a communal agricultural settlement located in the Mimoso Alluvial Valley (MAV), where EC and TOC were measured at 96 points, and seven biophysical indices were derived from UAV multispectral imagery. Geostatistical models, including cokriging with spectral indices (NDVI, EVI, GDVI, SAVI, and NDSI), were applied to map soil attributes at different spatial scales. Cokriging improved the spatial prediction of EC and TOC by reducing uncertainty and increasing mapping accuracy. The standard deviation of EC decreased from 1.39 (kriging) to 0.67 (cokriging with EVI), and for TOC from 15.55 to 8.78 (cokriging with NDVI and NDSI), reflecting a 43.5% reduction in uncertainty. The indices, EVI, NDVI, and NDSI, showed strong potential in representing and enhancing the spatial variability in soil attributes. NDVI and NDSI were particularly effective at finer grid resolutions, supporting more efficient irrigation strategies and sustainable agricultural practices. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

Over the past four decades, Wadi Fatimah in western Saudi Arabia has undergone significant environmental changes that have contributed to desertification. High-resolution spatial and temporal analyses are essential for monitoring the extent of desertification and understanding its driving factors. This study aimed to assess the spatial distribution of desertification in Wadi Fatimah using satellite and climate data. Landsat imagery from 1984 to 2022 was employed to derive land surface temperature (LST) and assess vegetation trends using the Normalized Difference Vegetation Index (NDVI). Climate variables, including precipitation and evapotranspiration (ET), were sourced from the gridded TerraClimate dataset (1980–2022). LST estimates were validated using MOD11A2 products (2001–2022), while TerraClimate precipitation data were evaluated against observations from four local rain gauge stations: Wadi Muharam, Al-Seal Al-Kabeer, Makkah, and Baharah Al-Jadeedah. A Desertification Index (DI) was developed based on four variables: NDVI, LST, precipitation, and ET. Five regression models—ridge, lasso, elastic net, polynomial regression (degree 2), and random forest regression—were applied to evaluate the predictive capacity of these variables in explaining desertification dynamics. Among these, Random Forest and Polynomial Regression demonstrated superior predictive performance. The classification accuracy of the desertification map showed high overall accuracy and a strong Kappa coefficient. Results revealed extensive land degradation in the central and lower sub-basins of Wadi Fatimah, driven by both climatic stressors and anthropogenic pressures. LST exhibited a clear upward trend between 1984 and 2022, especially in the lower sub-basin. Precipitation and ET analysis confirmed the region’s arid climate, characterized by limited rainfall and high ET, which exacerbate vegetation stress and soil moisture deficits. Validation of LST with MOD11A2 data showed reasonable agreement, with RMSE values ranging from 2 °C to 6 °C and strong correlation coefficients across most years. Precipitation validation revealed low correlation at Al-Seal Al-Kabeer, moderate at Baharah Al-Jadeedah, and high correlations at Wadi Muharam and Makkah stations. These results highlight the importance of developing robust validation methods for gridded climate datasets, especially in data-sparse regions. Promoting sustainable land management and implementing targeted interventions are vital to mitigating desertification and preserving the environmental integrity of Wadi Fatimah. Full article

In this work, an innovative process chain is set up for the regular provision of fertilization consultation services to farmers for a variety of crops, within a precision agriculture framework. The central hub of this mechanism is a geographic information system (GIS), while a 5 × 5 m point grid is the information carrier. Potential data sources include soil samples, satellite imagery, meteorological parameters, yield maps, and agronomic information. Whenever big data are available per crop, decision-making is supported by machine learning systems (MLSs). All the map data are uploaded to a farm management information system (FMIS) for visualization and storage. The recipe maps are transmitted wirelessly to variable rate technologies (VRTs) for applications in the field. To a large degree, the process chain has been automated with programming at many levels. Currently, four different service modules based on the new process chain are available in the market. Full article

To solve the problems of strong professionalism and cumbersome operation required for crop soil moisture prediction, a soil moisture prediction platform has been developed for real-time irrigation decision-making based on the Django framework. This platform supports users in quickly selecting prediction models, setting model hyperparameters, and training models based on historical data to achieve the real-time predictions of soil moisture. Users can simply run the model according to the prompts, greatly reducing the threshold for non-professionals to use the prediction model and improving the convenience and scalability of practical applications. In addition, to improve the accuracy and stability of the model, the platform has integrated automatic parameter tuning methods including grid search and random search to further optimize model performance. The experimental results show that the platform’s models can continuously and accurately predict soil moisture during crop reproductive stages, achieving an R² of 0.95 compared to observed values. This study not only effectively avoids the problem of duplicate development of soil moisture models and significantly improves the efficiency of model development and application, but also provides important support for the practical application and promotion of smart irrigation decision-making systems. Full article

In this work, we explore the optimization of 3D Electrical Resistivity Tomography (ERT) measurement protocols for a 3D borehole grid configuration. Currently, there is no widely accepted standard measurement scheme for such setups. The use of numerous electrodes and the possibility of cross-borehole configurations lead to an extremely large number of potential electrode combinations. However, not all these combinations contribute significantly to the final resistivity model, and a complete measurement cycle requires substantial time to perform. This becomes particularly problematic in dynamic subsurface conditions, where changes may occur during data acquisition. In such cases, the measurements collected within a single cycle may reflect different subsurface states. Conversely, attempting to shorten acquisition time can result in too few measurements to resolve the subsurface structure at high resolution. Furthermore, most existing approaches assume a uniform half-space model and treat all measurements equally, failing to prioritize those that are most sensitive to actual subsurface changes. To address these challenges, we propose a 3D measurement optimization approach that yields an efficient acquisition scheme. This method produces inversion results comparable to those obtained from much larger datasets while reducing both measurement and processing requirements. Our optimization is based on a sensitivity-driven selection algorithm that accounts for the real subsurface structure rather than assuming a generic half-space. The proposed methodology is validated using synthetic data and tested with experimental data obtained from a laboratory tank setup. These experimental measurements were used to monitor permeation grouting; a technique applied to reduce permeability and/or increase the strength of granular soils through targeted injection. Full article

To evaluate the ecological remediation effect of Salix matsudana × alba on saline coastal soils, we established a five-year field experiment in Rudong County, Jiangsu Province, China. The experiment was designed with three salinity gradients (low, medium, and high) and five plant spacing treatments (2 × 2 m, 2 × 3 m, 3 × 3 m, 3 × 4 m, and 4 × 4 m). Soil samples were collected annually at a depth of 0–20 cm using grid and random sampling methods. Indicators of soil physicochemical properties and heavy metal content were measured, including soil organic matter (SOM), pH, total nitrogen (TN), total phosphorus (TP), total potassium (TK), electrical conductivity (EC), total salinity (TS), and bulk density (BD). Additionally, eight heavy metals were analyzed: zinc (Zn), chromium (Cr), nickel (Ni), copper (Cu), cadmium (Cd), lead (Pb), arsenic (As), and mercury (Hg). Results showed that the hybrid willow significantly improved SOM content by up to 90% and reduced EC and TS by 52% and 60% over five years, especially under low and medium salinity conditions with dense planting (2 × 2 m, 2 × 3 m). The content of most heavy metals exhibited a decreasing trend or remained stable, indicating the plant’s phytostabilization potential (i.e., stabilization of heavy metals via plant-soil interaction). Principal component analysis (PCA) and random forest (RF) modeling identified SOM, EC, TS, and BD as the dominant factors influencing soil quality improvement. A soil quality index (SQI) was constructed based on PCA-derived weights, which further confirmed the positive ecological effect of this hybrid species on coastal saline soils. This study provides scientific evidence supporting the use of Salix matsudana × alba as a promising species for large-scale ecological restoration in coastal saline-alkaline lands. Full article

In low-relief agricultural areas, crop cover makes it challenging to obtain remotely sensed bare soil spectral data for predicting soil texture. Therefore, this study proposed a method for predicting soil texture using crop growth information with spatiotemporal stability. Spatiotemporal Stable Peak (SSP) maps were generated using the Ratio Vegetation Index (RVI) time-series data of rice and wheat, and they were used to represent crop growth information with spatiotemporal stability. Eighty-three soil sampling sites were arranged on the SSP maps with a regular grid. Ridge Regression, Ordinary Kriging, and Co-Kriging were adopted to map soil texture. The results showed that the SSP was closely related to clay and sand contents, with Pearson’s |r| ranging from 0.57 to 0.67. SSP-based Ridge Regression yielded better prediction accuracy (MAE = 3.95 and RMSE = 4.57) than Ordinary Kriging (MAE = 4.45 and RMSE = 5.19) in predicting clay content. The comparison between Ordinary Kriging and SSP-based Co-Kriging further demonstrated the effectiveness of SSP in improving clay content prediction accuracy, with an increase in R² of 70% and a reduction in RMSE of 3.85%. Similar results were obtained for sand content prediction. These results suggest that SSP can serve as an effective environmental variable for predicting soil texture spatial variation in low-relief agricultural areas. Full article