Search Results (46)

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

Accurate measurement and understanding of the spatiotemporal distribution of soil water content (SWC) are crucial in various environmental and agricultural sectors. The present study implements a novel precision agriculture (PA) approach under sugarbeet field conditions of two moisture-irrigation treatments with two subfactors, clay loam (CL) and clay (C) soils, for geostatistics modeling (seven models’ evaluation) of time domain reflectometry (TDR) multisensor network measurements. Two different sensor calibration methods (M1 and M2) were trialed, as well as the results of laboratory soil analysis for geospatial two-dimensional (2D) imaging for accurate GIS maps of root zone moisture profiles, granular, and hydraulic profiles in multiple soil layers (0–75 cm depth). Modeling results revealed that the best-fitted semi-variogram models for the granular attributes were circular, exponential, pentaspherical, and spherical, while for hydraulic attributes were found to be exponential, circular, and spherical models. The results showed that kriging modeling, spatial and temporal imaging for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps, the exponential model was identified as the most appropriate with TDR sensors using calibration M1, and the exponential and spherical models were the most appropriate when using calibration M2. The resulting PA profile maps depict spatiotemporal soil water variability with very high resolutions at the centimeter scale. The best validation measures of PA profile SWC ${\theta v}_{TDR}$ maps obtained were Nash-Sutcliffe model efficiency NSE = 0.6657, MPE = 0.00013, RMSE = 0.0385, MSPE = −0.0022, RMSSE = 1.6907, ASE = 0.0418, and MSDR = 0.9695. The sensor results using calibration M2 were found to be more valuable in environmental irrigation decision-making for a more accurate and timely decision on actual crop irrigation, with the lowest statistical and geostatistical errors. The best validation measures for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps obtained for clay loam over clay soils. Visualizing the SWC results and their temporal changes via root zone profile geostatistical maps assists farmers and scientists in making informed and timely environmental irrigation decisions, optimizing energy, saving water, increasing water-use efficiency and crop production, reducing costs, and managing water–soil resources sustainably. Full article

The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability of the material under different water content conditions are of concern because these stacks can reach considerable heights. The water behaviour in these structures is poorly understood, particularly the effects of the water content on the stability and potential for liquefaction of the stacks. This study aims to investigate the water retention and flow characteristics of compacted iron ore tailings in high columns to better understand their hydromechanical behaviour. The research used 5 m high columns filled with iron ore tailings from the Quadrilátero Ferrífero region in Minas Gerais, Brazil. The columns were prepared in layers, compacted, and instrumented with moisture content sensors and suction sensors to monitor the water movement during various stages of saturation, drainage, infiltration, and evaporation. The sensors provided consistent data and revealed that the tailings exhibited high drainage capacity. The moisture content and suction profiles were effectively established over time and revealed the dynamic water retention behaviour. The comparison of the data with the theoretical soil water retention curve (SWRC) demonstrated a good correlation which indicates that there was no hysteresis in the material response. The study concludes that the column setup effectively captures the water retention and flow characteristics of compacted tailings and provides valuable insights for the hydromechanical analysis of filtered tailings stacks. These findings can significantly help improve numerical models, calibrate material parameters, and contribute to the safer and more efficient management of tailings storage facilities. Full article

Thermally induced moisture flow in unsaturated soils involves complex coupled thermal–hydro processes with the moisture flow in both the vapor and liquid phases. The accurate measurement of the moisture flow in unsaturated sands remains a challenging task due to low moisture migration, the temperature effect on moisture sensors, and the gravity effect on moisture flow. This study aims to accurately measure transient moisture flow, heat transfer, and thermal conductivity in a silty sand with 35% non-plastic fines in a closed heat cell with a controlled 1-D temperature gradient. The heat cell consists of two temperature-controlled heat exchanger plates, heat flux sensors, moisture sensors, thermocouples, and thermal conductivity sensors. The soil moisture sensors were calibrated in the test soil at room temperature and then at elevated incremental temperatures. Soil samples compacted at various initial moisture contents were tested under a constant 1-D temperature gradient of 4 °C/cm. Soil moisture redistribution, temperature, and thermal conductivity profiles were determined from the test results. Transient temperature responses indicated that a lower initial moisture content led to a higher temperature drop after reaching the peak, or a more concaved temperature profile in a steady state due to enhanced moisture migration driven by the temperature gradients. Dry soils exhibited uniform thermal properties, while moist soils showed varying thermal conductivity profiles. A critical moisture content was identified when the maximum moisture migration occurred. Thermal conductivity in soils increased with the distance from the heat source due to thermally induced moisture migration. These findings provide valuable insights into coupled moisture–heat flow dynamics in unsaturated sands. Full article

The design of tailings dams has improved significantly in recent decades due to experience and advances in applied research. However, there are still several environmental and geomechanical uncertainties associated with the response of these structures. Failures on the wall of tailings dams are well documented, where the most common causes are related to the action of water overtopping, slope instability, seepage, and foundation failure. Measuring the humidity or the saturation level at tailings dam walls has become a must do in the recent years. Resistivity monitoring using electrical resistivity tomography (ERT) techniques has proven to be one of the tools that provide good subsurface characterization for internal erosion detection and seepage assessment to evaluate potential environmental risks and the physical stability of tailings dams. Also, the integrated techniques of geotechnical, geophysical, and geochemical data have been used to correlate, coordinate, and improve the characterization. In this research, a procedure to guide us to a new methodology of acquiring and monitoring humidity content is presented, in which 2D electrical resistivity tomography (ERT) profiles are linked to the degree of soil saturation, using moisture sensors installed in a nearby well. The ERT profiles provide a 2D resistivity profile, and the moisture sensors can measure resistivity and volumetric water content (VWC) at a given installation depth. This second measure (VWC), with a defined total porosity, can be combined with Archie’s empirical law to obtain the degree of saturation, allowing the possibility to create remote monitoring suitable for mining operations without excessive laboratory testing. Full article

Measuring the soil water content (SWC) is a fundamental component of the sustainable management of water resources, soil preservation, and high irrigation efficiency. Non-destructive SWC measurements using soil moisture sensors (SMSs) enables timely irrigation and reduces overirrigation and water stress. Within this context, the performance of four commercial single-point soil moisture sensors (Watermark and tensiometer (Irrometer Company, Inc., Riverside, CA, USA), SM150 (Delta-T Devices, Cambridge, UK)), FieldScout TDR300 (Spectrum Technologies, Aurora, IL, USA) and one soil profile PR2 probe (Delta-T Devices, Cambridge, UK) were tested under anthropogenic eutric cambisol with a silty clay loamy texture (20, 30, and 40 cm) to evaluate accuracy and sensitivity to changes in the SWC in an irrigated apple orchard. The Watermark and tensiometer were additionally tested in the laboratory to convert soil water tension (kPa) to the volumetric soil water content (%vol.). In general, all tested SMSs responded to changes in the SWC, with sensor-to-sensor differences. The Watermark and tensiometer underestimated the SWC, while the TDR overestimated the SWC. The SM150 and PR2 showed high accuracy, i.e., SM150—RMSE-2.24 (20 cm), 2.18 (30 cm) and 2.34 (40 cm), MSE—5.02 (20 cm), 2.93 (30 cm) and 1.89 (40 cm), and PR2—RMSE-1.8 (20 cm), 1.3 (30 cm) and 1.55 (40 cm), MSE-3.23 (20 cm), 1.7 (30 cm) and 2.39 (40 cm) at all observed soil depths. Full article

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches. Full article

The ground surface and subsurface of green parks in arid and desert areas may be subjected to desiccation as a result of weather and hot temperatures. It is not wise to wait until plants are turning pale and yellow before watering is resumed. Given the scarcity of water in typical desert zones, we recommend full control of irrigation water. This study presents a method of recycling irrigation water using 5TE sensors, employing time-domain reflectometry (TDR) technology. A trial test section was constructed along the coast of the eastern province of Saudi Arabia. Water recycling involves using clay–sand liners placed below the top agricultural soils to intercept excess water and direct it towards a collection tank, and then it is pumped out to a major water supply tank. The main properties of soils and clay–sand liners normally taken into account include moisture content, density, and hydraulic conductivity. An assessment of geotechnical properties of clay–sand mixtures containing 20% clay content was conducted. The profiles of moisture and temperature changes were monitored using 5TE sensors and data loggers. The 5TE sensors provided continuous measurements at varying temperatures and watering cycles. Twenty-nine watering cycles were conducted over a six-month period. An additional section was considered with a liner consisting of the same clay but enhanced with bentonite as one-third of the clay content. The volumetric water content was found to vary from 0.150 to 0.565 following changing weather and direct watering cycles. The results indicated that the use of a TDR instrumentation is a cost-effective and time-saving technique to construct a system for saving irrigation water. Full article

This article presents a wireless in situ sensor designed to continuously monitor profiles of parameters in porous media, such as soil moisture, salinity, and temperature. A review of existing in situ soil sensors reveals that it is the only device capable of measuring the complex permittivity of the medium, allowing for conversions into moisture and salinity that are independent of the instrument. Flow perturbation and invasiveness have also been minimized to maintain good representativeness. Plans include autonomous networks of such sensors, facilitated by the use of the recent radio mode LoRaWAN and cost optimizations for series production. Costs were reduced through electronic simplification and integration, and the use of low-cost modular sensing parts in soil, while still maintaining high measurement quality. A complete set of sensor data recorded during a three-month trial is also presented and interpreted. Full article

In hyper-arid regions, soil moisture’s role in ecohydrological processes can differ significantly from that in arid or semi-arid ecosystems. We investigated the spatial–temporal dynamics of soil moisture and its relationship with groundwater depths in a 200 m × 300 m phreatophytic tamarisk stand in the lower basin of the Tarim River, a hyper-arid zone in China. Soil moisture profiles, from the surface to the water table, were derived using drilling and oven-drying techniques. Over a three-year period, the soil moisture at multiple depths was continuously monitored in a specific plot using nine frequency domain reflectometry (FDR) sensors. Our results indicate a correlation between horizontal variations in soil moisture and groundwater depths (GWDs). Nevertheless, anomalies in this correlation were observed. Variations in horizontal soil moisture were strongly influenced by the clay content in the soil, with finer soils retaining more moisture. Despite varying GWDs, soil moisture profiles remained consistent, with no distinct correlation between them. Soil moisture exhibited stability across layers, with noticeable changes only adjacent to the water table. These results imply that in hyper-arid environments, soil texture primarily governs soil moisture distribution. However, the limited spatial and temporal scopes in our dataset, constrained by the region’s inhospitable conditions, necessitate further investigation. Future work should prioritize amalgamating diverse data sources to devise a region-specific soil moisture model for in-depth analysis of hyper-arid regions. Full article

The Chinese Loess Plateau has undertaken a large-scale “Grain for Green” project since 1999. Understanding how reforestation affects soil moisture is crucial for ecological construction and the region’s revegetation. In this study, soil sensors were installed to monitor the soil moisture content (SMC) and soil desiccation intensity in a 0–200 cm soil profile online during the growing season, with farmland as a control and Robinia (R.) pseudoacacia L., Pinus (P.) tabulaeformis Carr., Populus (P.) alba L., and Ulmus (U.) pumila L. were selected. The results showed that the SMC increased with soil depth, and the soil moisture storage (SMS) in the 0–200 cm soil profile was ranked as R. pseudoacacia L. (424.3 mm) < farmland (479.8 mm) < U. pumila L. (569.8 mm) < P. alba L. (583.9 mm) < P. tabulaeformis Carr. (589.8 mm). Secondly, the percentages of inefficient water and gravimetric water in soil moisture were ranked as R. pseudoacacia L. (63%) > farmland (49%) > U. pumila L. (43%) > P. alba L. (17%) > P. tabulaeformis Carr. (11%). The soil desiccation intensity of artificial forests was heavy in June, light in April and July, and no desiccation in the other months. Moderate desiccation was discovered in the 0–40 cm soil layer and mild desiccation occurred in the 40–60 cm soil layer. Additionally, the representative soil layer for SMS in farmland for P. tabulaeformis Carr., U. pumila L., and R. pseudoacacia L. was the 90 cm soil layer, and the SMS representative soil layer for P. alba L. was the 70 cm soil layer. In brief, an SMS deficit occurred after the conversion of the farmland to R. pseudoacacia L., but there was an SMS surplus after the conversion of the farmland to P. alba L., U. pumila L., and P. tabulaeformis Carr. This suggests that the artificial forest species could be optimized by introducing P. tabulaeformis Carr. instead of R. pseudoacacia L., and the degradation of R. pseudoacacia L. could be suppressed by the application of a nitrogen fertilizer. Our research demonstrated that soil moisture depletion patterns were closely related to artificial forest species, and attention should be paid to the vegetation restoration and maintenance of afforestation achievements in water-constrained arid regions in the future. Full article

Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m³ m⁻³ per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m³ m⁻³) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future. Full article

The soil water storage capacity is critical for soil management as it drives crop production, soil carbon sequestration, and soil quality and health. It depends on soil textural class, depth, land-use and soil management practices; therefore, the complexity strongly limits its estimation on a large scale with conventional-process-based approaches. In this paper, a machine learning approach is proposed to build the profile of the soil water storage capacity. A neural network is designed to estimate the soil moisture from the meteorology data input. By taking the soil moisture as a proxy in the modelling, the training captures those impact factors of soil water storage capacity and their nonlinear interaction implicitly without knowing the underlying soil hydrologic processes. An internal vector of the proposed neural network assimilates the soil moisture response to meteorological conditions and is regulated as the profile of the soil water storage capacity. The proposed approach is data-driven. Since the low-cost soil moisture sensors have made soil moisture monitoring simple and the meteorology data are easy to obtain, the proposed approach enables a convenient way of estimating soil water storage capacity in a high sampling resolution and at a large scale. Moreover, an average root mean squared deviation at $0.0307{\mathrm{m}}^3/{\mathrm{m}}^3$ can be achieved in the soil moisture estimation; hence, the trained model can be deployed as an alternative to the expensive sensor networks for continuous soil moisture monitoring. The proposed approach innovatively represents the soil water storage capacity as a vector profile rather than a single value indicator. Compared with the single value indicator, which is common in hydrology, a multidimensional vector can encode more information and thus has a more powerful representation. This can be seen in the anomaly detection demonstrated in the paper, where subtle differences in soil water storage capacity among the sensor sites can be captured even though these sensors are installed on the same grassland. Another merit of vector representation is that advanced numeric methods can be applied to soil analysis. This paper demonstrates such an advantage by clustering sensor sites into groups with the unsupervised K-means clustering on the profile vectors which encapsulate soil characteristics and land properties of each sensor site implicitly. Full article

Soil profile moisture is a crucial parameter of agricultural irrigation. To meet the demand of soil profile moisture, simple fast-sensing, and low-cost in situ detection, a portable pull-out soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of a moisture-sensing probe and a data processing unit. The probe converts soil moisture into a frequency signal using an electromagnetic field. The data processing unit was designed for signal detection and transmitting moisture content data to a smartphone app. The data processing unit and the probe are connected by a tie rod with adjustable length, which can be moved up and down to measure the moisture content of different soil layers. According to indoor tests, the maximum detection height for the sensor was 130 mm, the maximum detection radius was 96 mm, and the degree of fitting (R²) of the constructed moisture measurement model was 0.972. In the verification tests, the root mean square error (RMSE) of the measured value of the sensor was 0.02 m³/m³, the mean bias error (MBE) was ±0.009 m³/m³, and the maximum error was ±0.039 m³/m³. According to the results, the sensor, which features a wide detection range and good accuracy, is well suited for the portable measurement of soil profile moisture. Full article

Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers. Full article