Search Results (6,268)

Soil moisture (SM) plays a fundamental role in the water cycle and is an important variable for all processes occurring at the lithosphere–atmosphere interface, which are strongly affected by climate change. Among the different fields of application, accurate SM measurements are becoming more relevant for all studies related to extreme event (e.g., floods, droughts, and landslides) mitigation and assessment. In this study, data acquired by the advanced microwave sounding unit (AMSU) onboard the European Meteorological Operational Satellite Program (MetOP) satellites were used for the first time to extract information on the variability of SM by implementing the original soil wetness index (SWI). Long-term monthly SWI time series collected for the Basilicata region (southern Italy) were analyzed for drought assessment during the period 2007–2021. The accuracy of the SWI product was tested through a comparison with SM products derived by the Advanced SCATterometer (ASCAT) over the 2013–2016 period, while the Standardized Precipitation-Evapotranspiration Index (SPEI) was used to assess the relevance of the long-term achievements in terms of drought analysis. The results indicate a satisfactory accuracy of the SWI, with the mean correlation coefficient values with ASCAT higher than 0.7 and a mean normalized root mean square error less than 0.155. A negative trend in SWI during the 15-year period was found using both the original and deseasonalized series (linear and Sen’s slope ~−0.00525), confirmed by SPEI (linear and Sen’s slope ~−0.00293), suggesting the occurrence of a marginal long-term dry phase in the region. Although further investigations are needed to better assess the intensity and main causes of the phenomena, this result indicates the contribution that satellite data/products can offer in supporting drought assessment. Full article

This study was conducted to characterize and classify soils and rocks and to produce an engineering geological map that is beneficial for overall urban planning. The soils’ moisture content and specific gravity values range from 23.47% to 44.21% and 2.68 to 2.81, respectively. The activity of soils varies from 0.34 to 0.78 (inactive to normal). The shrinkage limit and shrinkage index values of soils range from 5% to 11.43% and 14.29% to 26.9%, respectively. Free swell value varies from 5 to 23% (low expansive). The unconfined compressive strength of soils ranges from 215.8 to 333.5 kPa (very stiff). According to USCS (Unified Soil Classification System), soils are classified into lean clay, lean clay with sand, fat clay with sand, and clayey silt with slight plasticity. According to BSCS (British Soil Classification SystemS), soils are classified into clay of intermediate plasticity, clay of high plasticity, and silt of intermediate plasticity. Rocks were classified into four categories based on their mass strength: very low mass strength, low mass strength, medium mass strength, and high mass strength. The RQD Rock Quality Designatione) value ranges from 47.48% to 98.25%, indicating a quality range from poor to excellent. The RMR Rock Mass Ratinge) values range from 44 to 90%, indicating that the rocks of the study area fall into three major classes: Class I (very good), Class II (good), and Class III (fair). Full article

For every tractor test carried out on a concrete road under defined conditions, the Nebraska Tractor Test Laboratory (NTTL) provides values of the specific volumetric fuel efficiency (SVFE) in unit of kWh/L). Because soil tillage is a highly energy-intensive process and the energy consumption of tillage operations is a significant component of a farm budget, there is a growing amount of attention being given to the examination of the SVFE for tillage operations. Nonetheless, the study of the tillage process and a scientific approach to the tillage process are becoming more and more dependent on scientific modeling. Therefore, in this study based on real-tillage field operation, an artificial neural network (ANN) model was built to predict SVFE. This study aimed to confirm that the ANN model could incorporate 10 inputs for prediction: initial soil moisture content, draft force, initial soil bulk density, sand, silt, and clay proportions in the soil tractor power, plow width, tillage depth, and tillage speed. The Qnet v2000, as an ANN simulation software, was employed for the simulation of the SVFE. In this regard, 20,000 runs of Qnet v2000 were completed for the training and testing stages. The anticipated results displayed that the determination coefficient (R²) was larger than 0.96; using the training dataset, R² was 0.982 and using the testing dataset, R² was 0.9741, indicating that the recognition of a full ANN model makes it likely to reply to essential enquiries that were previously unanswerable regarding the impact of working and soil conditions on the SVFE of a tractor–tillage implement system. Additionally, sensitivity analyses were completed to specify which modeled parameters were more sensitive to the factors using the obtained ANN model. According to the sensitivity analysis, SVFE was more affected by changes in the tillage speed (21.07%), silt content in the soil (15.56%), draft force (11.01%), and clay content in the soil (10.86%). Predicting SVFE can lead to more appropriate decisions on tractor–chisel plow combination management. Therefore, it is highly advisable to use the newly created ANN model to appropriately manage SVFE to reduce tractor–tillage implement energy dissipation. Additionally, suitable management of some variables, for example, tillage depth, tillage speed, and soil moisture content, can help enhance fuel consumption in the tractor–tillage implementation system. Full article

Understanding how spatial drought variability influences streamflow is critical for sustainable water management under changing climate conditions. This study developed a novel Combined Drought Index (CDI) and a method to assess spatial drought impacts on different flow components by integrating remote sensing and hydrological modelling frameworks with generic applicability. The CDI was constructed using Principal Component Analysis to merge multiple standardized indicators: the Standardized Precipitation Evapotranspiration Index, Temperature Condition Index, Vegetation Condition Index, and Soil Moisture Condition Index. The developed framework was applied to the Giriulla sub-basin of the Maha Oya River Basin, Sri Lanka. The CDI strongly correlated with standardized streamflow with a Pearson correlation coefficient of 0.74 and successfully captured major drought and flood events between 2015 and 2023. A semi-distributed hydrological model was used to simulate streamflow variations across sub-catchments under varying drought conditions. Results show upstream sub-catchments were more sensitive to droughts, with sharper declines in specific discharge. Spatial drought variability had different impacts under high- and low-flow conditions: wetter sub-catchments contributed more during high flows, while resilience during low flows varied with catchment characteristics. This integrated approach provides a valuable framework that can be generically applicable for enhanced drought impact assessments. Full article

In this study, we investigate the impact of soil moisture at varying depths on the stability of Chinese ecosystems, with ecosystem stability assessed using the Enhanced Vegetation Index (EVI) and Gross Primary Productivity (GPP). A multi-perspective analysis is conducted across different climatic zones and land cover types. Sen’s Slope Estimation and the Mann–Kendall trend test, combined with linear regression and correlation analyses, are employed to analyze the long-term trends of EVI and GPP in different climatic zones and land cover types and to assess the effects of soil moisture changes on ecosystem stability. The research reveals the following findings: (1) On a national scale, both EVI and GPP exhibit positive growth trends, with more significant increases in humid areas and relatively slower growth in arid areas. In addition, EVI and GPP of different land cover types exhibit positive inter-annual variation trends, reflecting a gradual enhancement in ecosystem productivity. (2) Cluster analysis shows that EVI has strong spatial correlation, with a distribution pattern of low–low (L-L) clusters in the north and high–high (H-H) clusters in the south. L-H clusters are concentrated in the Huaihai, Southwest Rivers, and Pearl River basins, while H-L clusters are scattered along the eastern coast. The spatial correlation of GPP is mainly concentrated in the south and the northeast, with a distribution pattern of L-L in the northeast, L-H in the Yangtze River basin, and H-H in the south. H-L clusters are dispersed in the downstream area of the Yangtze River. Both EVI and GPP show a tendency for high-value aggregation in space, with high-value areas of EVI located in the south and low-value areas in the central and western regions. High-value areas of GPP are in the south, while low-value areas are in the northeast, particularly in the Yangtze River Delta. (3) The correlation between EVI, GPP, and soil moisture varies significantly across different climatic regions. Arid and semi-humid regions show significant correlations between specific soil moisture depths and EVI and GPP, while such correlations are not significant in humid regions. The EVI and GPP values of croplands and grasslands are significantly and negatively correlated with soil moisture at depths of 150–200 cm (SM4). Conversely, wetland GPP values increase significantly with increasing soil moisture. Other vegetation types do not show significant correlations with soil moisture. The results of this study provide an important basis for understanding the impact of climate change on ecosystem stability and offer scientific guidance for ecological protection and water resource management. Full article

Heat storage in compacted soil embankments is a promising technology in energy geotechnics, but its impact on the thermo-hydraulic behavior of unsaturated soils remains insufficiently understood. This paper investigates coupled heat and moisture transfer in unsaturated soil under different thermal conditions using a new bottom-heating method. The thermo-hydraulic response is monitored along the soil column and compared to an isothermal drying test. Variations in suction and water content were analyzed to determine water retention curve and to derive unsaturated hydraulic conductivity using the instantaneous profile method. The water retention curve exhibited deviations under thermal conditions, with reduced water contents observed only at intermediate suctions. Unsaturated hydraulic conductivity decreased significantly at moderate suctions but increased by up to one order of magnitude at high suctions. Heat-driven moisture redistribution was examined through flux calculations, highlighting that vapor-phase transport contributed significantly, up to 88%, to the upward water migration. These findings contribute to a better understanding of thermo-hydraulic interactions in unsaturated soils, which is essential for optimizing thermal storage applications in compacted embankments. Full article

The potato (Solanum tuberosum L.) is highly dependent on water availability, with physiological sensitivity varying throughout its phenological cycle. In the context of increasing water scarcity and greater climate variability, identifying critical periods where water stress negatively impacts productivity and tuber quality is essential. This study evaluated the physiological response of potatoes under different deficit irrigation strategies in field conditions, and aimed to determine the irrigation reduction thresholds that optimize water use efficiency without significantly compromising yield. Five irrigation regimes were applied: well-watered (T1; irrigation was applied when the volumetric soil moisture content was close to 35% of total water available), 130% of T1 (T2, 30% more than T1), 75% of T1 (T3), 50% of T1 (T4), and 30% of T1 (T5). Key physiological parameters were monitored, including gas exchange (net photosynthesis, stomatal conductance, and transpiration), chlorophyll fluorescence (Fv’/Fm’, ΦPSII, electron transport rate), and photosynthetic pigment content, at three critical phenological phases: tuberization, flowering, and fruit set. The results indicate that water stress during tuberization and flowering significantly reduced photosynthetic efficiency, with decreases in stomatal conductance (gs), effective quantum efficiency of PSII (ΦPSII), and electron transport rate (ETR). In contrast, moderate irrigation reduction (75%) lowered the seasonal application of water by ~25% (≈80 mm ha⁻¹) while maintaining commercial yield and tuber quality comparable to the fully irrigated control. Intrinsic water use efficiency increased by 18 ± 4% under this regime. These findings highlight the importance of irrigation management based on crop phenology, prioritizing water supply during the stages of higher physiological sensitivity and allowing irrigation reductions in less critical phases. In a scenario of increasing water limitations, this strategy enhances water use efficiency while ensuring the production of tubers with optimal commercial quality, promoting more sustainable agricultural management practices. Full article

Forest fires critically disrupt soil ecosystems by altering physicochemical properties and microbial structure-function dynamics. This study assessed short-term impacts of fire intensities (light/moderate/heavy) on microbial communities in Larix gmelinii forests one year post-fire. Using phospholipid fatty acid (PLFA) and Biolog EcoPlate analyses, we found the following: (1) fire reduced soil organic carbon (SOC), dissolved organic carbon (DOC), total nitrogen (TN), and available nitrogen/potassium (AN/AK) via pyrolytic carbon release, while heavy-intensity fires enriched available phosphorus (AP), AN, and AK through ash deposition. (2) Thermal mortality and nutrient-pH-moisture stress persistently suppressed microbial biomass and metabolic activity. Moderate fires increased taxonomic richness but reduced functional diversity, confirming “functional redundancy.” (3) Neither soil microbial biomass nor metabolic activity at the fire site reached pre-fire levels after one year of recovery. Our findings advance post-fire soil restoration frameworks and advocate multi-omics integration to decode fire-adapted functional gene networks, guiding climate-resilient forest management. Full article

This study focuses on the alpine meadow ecosystem of the Qinghai–Tibet Plateau, which plays a vital role in carbon sequestration and water resource protection. However, mining activities have severely damaged the ecosystem, posing challenges for ecological restoration. The study selected the Jiangcang mining area and analyzed the physical, chemical, and carbon characteristics and heavy metal content of soil samples from the slag platforms and slopes (0–20 cm), which were restored in 2015 and 2020 to explore the effects of different soil reconstruction methods on soil function and ecological resilience. The results show that the minimum data set (MDS) can effectively replace the total data set (TDS) in assessing soil quality. The assessment indicates good restoration effects in 2020, with some areas rated high in soil quality. Although issues such as high bulk density, high electrical conductivity, low moisture content, nitrogen deficiency, and low organic matter limit ecological restoration, the carbon sequestration capacity of the restored soil is strong. This study provides scientific evidence for ecological restoration in cold mining areas, indicating that capping measures can enhance soil resistance to erosion, nutrient retention, and carbon sink functions. Full article

Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R² = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R² value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions. Full article

The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. However, the prediction accuracy for moisture conditions using spectral data alone is low. In this study, relationships between water deficits and phenotypic characteristics in oats were evaluated and used to develop a UAV multispectral-based water prediction model. The vegetation indices NDRE (Normalized Difference Red Edge), CIG (Chlorophyll Index), and MCARI (Modified Chlorophyll Absorption in Reflectance Index) were highly correlated with oat yield. Based on a multipath analysis in the structural equation modeling framework, irrigation (p < 0.01), leaf area index (LAI) (p < 0.001), and SPAD (p < 0.001) had direct positive effects on NDRE. Three distinct machine learning approaches—linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to establish predictive models between the Normalized Difference Red Edge Index (NDRE) and soil water content (SWC). The linear regression model showed moderate correlation (R² = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: R² = 0.828; ANN: R² = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices. Full article

Global warming is driven by the increasing atmospheric emissions of greenhouse gases. Soils are highly sensitive to climate change and can shift from being carbon reservoirs to carbon sources under warmer and wetter conditions. This study is the first to simultaneously measure trace gas fluxes in Euterpe oleracea (açaí) plantations in upland areas, contrasting them with floodplain areas managed for açaí production in the eastern Amazon. Flux measurements were conducted during both the rainy and dry seasons using the closed dynamic chamber technique. In upland areas, CO₂ fluxes exhibited spatial (plateau vs. lowland) and temporal (hourly, daily, and seasonal) variations. During both the rainy and dry months, CH₄ uptake in upland soils was higher in lowland areas compared to the plateau. When comparing the two ecosystems, upland areas emitted more CO₂ during the rainy season, while floodplain areas released more CH₄ into the atmosphere. Unexpectedly, during the dry season, floodplain soils produced more CO₂ and captured more CH₄ from the atmosphere compared to upland soils. In upland areas, CO₂-equivalent production reached 59.1 Mg CO₂-eq ha⁻¹ yr⁻¹, while in floodplain areas, it reached 49.3 Mg CO₂-eq ha⁻¹ yr⁻¹. Soil organic matter plays a vital role in preserving water and microorganisms, enhancing ecosystem productivity in uniform açaí plantations and intensifying the transfer of CH₄ from the atmosphere to the soil. However, excessive soil moisture can create anoxic conditions, block gas diffusion, reduce soil respiration, and potentially turn the soil from a sink into a source of CH₄. Full article

Natural water content affects many geotechnical parameters and geological properties of soils, which can reduce cohesion and friction, leading to potential failures in structures such as foundations, retaining walls, and slopes. Identification of the water content helps in designing effective drainage and water management systems to prevent flooding and erosion. In tunnel engineering, soil water content plays an important role as the stability of the tunnel face depends on it. This research solves the problem of classifying soil images depending on the natural water content by computer vision technology. First, laboratory soil tests were carried out, and the relationship between the amount of torque on the screw conveyor and the moisture content of the soil was established; photographs of the soil at different conditions were taken at each step of the experiment. Second, the resulting dataset after preprocessing was processed by convolutional neural network algorithms during deep learning; the transfer learning technique was used to obtain better results. As a result, seven algorithms were obtained that allow classifying the soil images, which can later be used to optimize the tunnel construction process. The best classification ability is demonstrated by the algorithm based on the DenseNet architecture (accuracy 0.9302 and loss 0.1980). The proposed model surpasses traditional approaches due to its increased automation and processing speed. Laboratory tests can be carried out only once for one type of soil in order to determine the boundaries of water content for classes labeling, after which only a cheap camera is required from the equipment to transmit new images for processing by the algorithm. Full article

Agricultural waste poses significant environmental, economic, and social challenges globally, with estimates indicating that 10–50% of agricultural products are discarded annually as waste. This review explores strategies for managing agricultural waste to mitigate its adverse impacts and promote sustainable development. Agricultural residues, such as those from sugarcane, rice, and wheat, contribute to pollution when improperly disposed of through burning or burying, contaminating soil, water, and air. However, these residues also represent untapped resources for bioenergy production, composting, mulching, and the creation of value-added products like biochar, bioplastics, single-cell protein and biobased building blocks. The paper highlights various solutions, including integrating agricultural waste into livestock feed formulations to reduce competition for human food crops, producing biofuels like ethanol and biodiesel from lignocellulosic materials, and adopting circular economy practices to upcycle waste into high-value products. Technologies such as anaerobic digestion for biogas production and gasification for synthesis gas offer renewable energy alternatives and ample feedstocks for gas fermentation while addressing waste management issues. Composting and vermicomposting enhance soil fertility, while mulching improves moisture retention and reduces erosion. Moreover, the review emphasizes the importance of policy frameworks, public-private partnerships, and farmer education in promoting effective waste management practices. By implementing these strategies, agricultural waste can be transformed into a resource, contributing to food security, environmental conservation, and economic growth. Full article

This study investigates urban warming in Rajshahi City, Bangladesh, by examining changes in land surface temperature (LST) from 1990 to 2023 and exploring its relationship with key biophysical factors. LST was derived from Landsat thermal imagery, and both spatial and temporal variations were analyzed using Geographic Information Systems (GIS). Key biophysical indices, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Bareness Soil Index (NDBSI), were calculated using corresponding Landsat satellite sensors, and they evaluated the impact of LULC types (vegetation, water, soil, and built-up areas) on thermal variations. LULC was derived following the Support Vector Machine classification technique. The Urban Thermal Field Variance Index (UTFVI) was employed to assess surface urban heat island (SUHI) effects, warming conditions, ecological stress, and thermal comfort zones. Spatial trend and hotspot analyses of LST change were performed using spatial trend analysis and the Getis-Ord Gi* statistic, respectively. Linear regression analysis examined the relationship between LST and biophysical indices. Results show that winter mean LST increased by 2.66 °C during the 33-year period, with maximum LST rising by 4.29 °C. The most significant warming occurred in central-northern, central-western, and south-eastern zones. The rise in LST and the growing intensity of SUHI effects are largely due to urban growth, especially where green spaces and water bodies have been replaced by impervious surfaces. Hotspot analysis identified clusters of high-temperature zones, while UTFVI analysis confirmed a marked expansion of strong heat island conditions, especially in central urban areas. Linear regression results showed notable links between LST and key biophysical variables, where higher LST values were commonly linked to greater built-up density and declines in vegetation cover and surface water. Overall, the results highlight the need for better urban planning approaches such as increasing green cover, using permeable materials, and adopting strategies that can adapt to climate impacts. This study presents a framework for analyzing urban climate dynamics that can be adapted to other rapidly growing cities, aiding efforts to promote sustainable development and build urban resilience. Full article