Soil Electrical Conductivity Measurements Research Articles

Development of a soil electrical conductivity measurement system in paddy fields

This study aims to develop a data collection system for measuring soil electrical conductivity (EC) in Paddy Fields using the Wenner measurement principle, with one electrode configuration based on the Veris 3100 measuring machine, and a second electrode configuration with fixed electrodes. The result is a system that measures soil EC using the fixed electrode configuration for more stable results compared to the coulter electrode configuration on the Veris 3100. This system comprises three main components: a 7-inch industrial touchscreen monitor used for real-time monitoring and storing collected data on external memory, a controller that provides reverse direct current (DC) power supply to the electrodes and measures voltage and current parameters on the electrodes, two mechanical configurations including six electrodes in contact with the soil. The system is developed to operate best after soil preparation and before seed sowing. The collected data is processed and compared to actual measurements using an artificial neural network (ANN), resulting in an R2=0.7449, equivalent to linear regression. The system has been successfully installed and operated on a handheld tractor and tested in Paddy Fields on the outskirts of Can Tho City, Vietnam.

A multisensor approach coupled with multivariate statistics and geostatistics for assessing the status of land degradation: The case of soils contaminated in a former outdoor shooting range

Soil contamination in outdoor shooting ranges (OSRs) is a major threat for human health, particularly when, after the end of activities, the land is used for recreational areas or agricultural production. The status of land degradation of an OSR in southern Italy was assessed using a multisensor approach. It was based on: i) proximal sensors, including electromagnetic induction (EMI) for measuring soil electrical conductivity (ECa) and magnetic susceptibility (MSa), γ-ray spectrometry for K, eU and eTh analyses and ultrasonic penetrometry detecting cone index (CI) data representative of soil's strength, ii) field surveys on soil thickness (ST), and iii) laboratory analyses of potentially-toxic-elements (PTEs) by portable X-ray fluorescence spectrometry and polycyclic aromatic hydrocarbons (PAHs) by gas-chromatography. Spatial variability of measurements was modelled and mapped using geostatistical methods. The most densely measured covariate (i.e., the ECa of the topsoil) was used within kriging with external drift to improve the PTEs predictions. The PTEs maps were complemented by maps of spatial uncertainty. A robust multivariate principal component analysis (rPCA) was applied to proximal sensor and laboratory data and allowed to identify associations of PAHs, lead, CI with the topsoil ECa along the first component (PC1), highlighting the correlation between land anthropogenic effects and EMI measures; while the association between the ST (estimating the depth of underground travertine hard-layers) and the bottom soil ECa and MSa along the second component (PC2) evidenced the influence of soil stratigraphy on the EMI measures. This study demonstrates that the simultaneous use of different proximal sensors associated with laboratory analysis can allow to assess and model the spatial variability of the land degradation status of an OSR, including soil compaction, organic and inorganic contamination. The correlation between EMI data with the PTEs content highlights the potential of this technique in the field of soil contamination.

A statistical approach to map saline and fluoride (F) affected areas using optical and microwave remote sensing data simulations: A case study in Palacode Taluk, Tamil Nadu, India

This research presents an approach for the indirect assessment of soil contamination by soil salinity and Fluoride (F) in varying environmental conditions, including both dry and wet seasons. The research was conducted in Dharmapuri District, utilizing Landsat 8 and Sentinel-1 data for analysis. The region exhibits a diverse soil regime, encompassing both dry-state and irrigated agriculture. The Modified Salinity Index (MSI) and Plant Senescence Reflectance Index (PSRI) were extracted from Landsat 8 imagery to assess contamination in dry conditions. Simultaneously, the Simplified Hallikainen's model and the Inverse of Dual-Polarization Soil Vegetation Index (DPSVI−1) were obtained from Synthetic Aperture Radar (SAR) data to investigate contamination in wet conditions. The correlation coefficient (CC) analysis was performed between optical data simulations (MSI and PSRI) and synthetic aperture radar (SAR) data simulations (Simplified Hallikainen's model and DPSVI−1), yielded R2 values of 0.90 and 0.92 in the dry and wet seasons, respectively, indicating a high degree of accuracy and reliability. From the correlation coefficient (CC) analysis, approximately 23% (173 km2) of the area exhibited negative correlation was identified as F affected regions and 54% (378 km2) of the areas exhibited positive correlation was identified as saline-affected regions in dry season. Similarly, approximately 41% (288 km2) of the area was identified as F affected regions and 46% (322 km2) of the areas was identified as saline-affected regions in wet season. The findings were verified through ground measurements of soil electrical conductivity (EC) and F using a confusion matrix. The results indicate an overall accuracy of 83% and 84%, accompanied by kappa coefficient measures of 0.79 and 0.83, for salinity and F, respectively, during the dry season. Likewise, in the wet season, there is a recorded overall accuracy of 86% and 88%, with corresponding kappa coefficients of 0.81 and 0.85 for salinity and F, respectively. This implies that the proposed methodology has a higher potential for mapping saline and F-affected areas in both wet and dry conditions. Hence, the methodology outlined in this study provides crucial information for local authorities, farmers, and policymakers, enabling informed decision-making and proactive measures.

Measurement and Modeling of Thermo-Hydro-Mechanical Behaviors of Frozen Clays: Frost Susceptibility and Compressibility

The risk of geohazards associated with frozen subgrades is well recognized, but a comprehensive framework to evaluate frost susceptibility from microstructural characteristics to macroscopic thermo-hydro-mechanical (THM) behaviors has not been established. This study aims to propose a simple framework for quantitatively assessing frost susceptibility and compressibility in frozen soils. A systematic THM model was devised to predict heat transfer, soil freezing characteristics, and stress states in frozen soils. Constant freezing experiments and oedometer compression tests were performed on bentonite clays under varying temperatures (−5°C, −10°C, and −20°C) and stress levels to validate the proposed model. Additionally, soil electrical conductivity measurements were employed to assess the temperature- and stress-dependent volumetric and mechanical properties of frozen soils. The model used Fourier’s law to compute the transient soil temperature profile and estimated the volume change and stress states based on the soil freezing characteristic curve. Experimental results showed that frost heave of bentonite reached between 9.0% and 26.6% of axial strain, which was largely predicted by the proposed model. It also demonstrated that the frost heave was mainly attributed to the fusion of the porewater. Additionally, the preconsolidation pressure of frozen soils exhibited a rapid increasing trend with decreasing temperature, which was explained by the temperature-dependent ice morphology in the soil interpore. Furthermore, the findings also demonstrated a remarkable sensitivity in the electrical conductivity in response to the soil temperature during the frost heave process and the stress state under the loading or unloading path.

Management zone classification for variable-rate soil residual herbicide applications

AbstractThe use of soil residual herbicides, along with other practices that diversify weed management strategies, have been recommended to improve weed management and deter the progression of herbicide resistance. Although soil characteristics influence recommended application rates for these herbicides, the common practice is to apply a uniform dose of soil residual herbicides across fields with variable soil characteristics. Mapping fields for soil characteristics that dictate the optimal dose of soil residual herbicides could improve the efficiency and effectiveness of these herbicides, as well as improve environmental stewardship. The objectives of this research were to develop and quantify the accuracy of management zone classifications for variable-rate residual herbicide applications using multiple soil data sources and soil sampling intensities. The maps were created from soil data that included (i) Soil Survey Geographic database (SSURGO), (ii) soil samples (SS), (iii) soil samples regressed onto soil electrical conductivity (EC) measurements (SSEC), (iv) soil samples with organic matter (OM) data from SmartFirmer® (SF) sensors (SSSF), and (v) soil samples regressed onto EC measurements plus OM data from SmartFirmer® sensor (SSECSF). A modified Monte Carlo cross validation method was used on ten commercial Indiana fields to generate 36,000 maps across all sources of spatial soil data, sampling density, and three representative herbicides (pyroxasulfone, s-metolachlor, and metribuzin). Maps developed from SSEC data were most frequently ranked with the highest management zone classification accuracy compared to maps developed from SS data. However, SS and SSEC maps concurrently had the highest management zone classification accuracy of 34% among maps developed across all fields, herbicides, and sampling intensities. One soil sample per hectare was the most reliable sampling intensity to generate herbicide application management zones compared to one soil sample for every 2 or 4 hectares. In conclusion, soil sampling with ECa data should be used for defining the management zones for variable-rate (VR) residual herbicide applications.

Assessing Soil Dynamics and Improving Long-Standing Irrigation Management with Treated Wastewater: A Case Study on Citrus Trees in Palestine

Irrigation with Treated Wastewater (TWW) is a well-known agricultural practice in Palestine. The long-term use of irrigation with TWW, a source of water and nutrients, can affect plant development, soil, and groundwater quality. Consequently, the frequency and the intervals of irrigation events should be adequately scheduled, especially when nutrients (TWW-N) cannot be separated from the water. Achieving good water quality implies its immediate reuse in irrigated agriculture. In contrast, long-term soil and groundwater quality conservation is marked by the complex mechanisms that correlate the soil, water, plant, and atmosphere. Therefore, monitoring and modeling (MMA) are combined to retrieve the soil water and nitrate fluxes and identify a proper irrigation management plan in a case study in Beit Dajan-Palestine, where a schedule adapted to conventional water was applied to a 6-year-old citrus orchard continuously irrigated with TWW. Soil nitrogen concentration and water content data were collected from March to August 2021 to calibrate the Hydrus-1D model under the (1) farmer demand (F) scenario, where irrigation volumes are delivered according to the farmer experience, and to define an optimal irrigation management strategy with TWW according to the (2) model demand (M) scenario, based on the irrigation frequency. The latter respects the allowable thresholds of soil solution electrical conductivity, σe, assuming an average soil salinity profile and estimated leaf nitrogen concentrations tolerance as reference; 2021 was taken as a calibration year to retrieve water and nitrate fluxes for 2019 and 2020. In 2021, the measured soil electrical conductivity, σe, showed no salinity risk with an average value of 1.07 dS m−1 (low salinity < 2 dS m−1) but with a leaf nitrogen deficit. Although an acceptable level of available soil nitrogen was observed (ranging between 10 and 35 mg kg−1, whereas the standard value is 10–40 mg kg−1), critical concentrations were observed in the leaves (below 1%) in scenario (F) compared to scenario (M) (ranging between 1.7 and 1.9%). The latter also showed a decrease in nitrate leaching by 33% compared to the former. Overall, the comparison between the simulated and measured soil variables shows that the 1D-Hydrus model could follow the temporal variation in the monitored data, with some overestimation of the measured data during the simulation period. The simulations demonstrate that by modulating the salt tolerance threshold, the M scenario achieved better results in terms of root water and N uptake despite the stress inevitably experienced by citrus with long-term TWW irrigation. Moreover, the optimum threshold values used to assess the soil quality and citrus response under conventional water irrigation were inadequate for TWW practices. Therefore, MMA could be an alternative strategy to schedule proper TWW irrigation.

A Mobile Laboratory Robot for Various and Precise Measurements of Crops and Soil in Agricultural Fields: Development and Pilot Study

Localized management based on multipoint measurements of crops and soil is expected to improve agricultural productivity. The difficulties with this process are as follows: it is time-consuming due to the many measurement points; it requires various sensors for various measurements; it can lead to unstable measurements due to sunlight and wind. To solve the above issues, the system we propose has the advantages of efficient measurements performed by a robot, various measurements performed through exchangeable sensor units, and stable measurements through protecting the crop from sunlight and wind. As a pilot study for the system, we conducted an experiment to simultaneously measure the crops and soil in a cabbage field. The robot achieved mobility in the field, >4 h of operation time, and the ability to obtain soil electrical conductivity measurements and crop imaging at 100 points. Furthermore, the stability of the measurement conditions within the sensor unit during the experiment was evaluated. Compared to the case without the covering, the illuminance became 280-fold stabler (standard deviation = 0.4 lx), and the wind-induced crop shaking became 20-fold lower (root mean square error of the image pixels = 0.5%). The potential impacts of this research include high reproducibility because of the shareable sensor unit and the expectation of new discoveries using precise indoor sensors.

Temporal and operation-induced instability of apparent soil electrical conductivity measurements

Measuring apparent soil electrical conductivity (ECa), using galvanic contact resistivity (GCR) and electromagnetic induction (EMI) techniques, is frequently conducted to reveal spatial soil heterogeneity. Various studies have demonstrated the possibilities for significant changes in the measured quantities over time with relatively stable spatial structure representations. The objective of this study was to quantify the effects of temporal drift and operational noise for three popular ECa mapping instruments. They were placed in stationary positions approximately 8 m apart in an area with relatively low ECa. Temporal drift was assessed using a series of 4.5-h data logs recorded under different weather conditions (from extremely hot to near freezing temperatures). The two EMI instruments were also used to quantify the effect of minor changes in the height, pitch and roll of the sensor with respect to the ground. These operational noise tests were replicated over several days. Our results reveal the GCR measurements of ECa, along with perpendicular coplanar EMI measurements, have shown relatively strong stability over time. Each operational effect introduced measurement uncertainties comparable to the impact of a change in temperature and soil water content.

Research on vehicle-mounted soil electrical conductivity and moisture content detection system based on current–voltage six-terminal method and spectroscopy

To provide real-time, online, and accurate measurements of soil electrical conductivity (EC) and moisture content (SM) in large-scale agricultural fields and guide field management, vehicle-mounted online EC and SM measurement system based on the fusion of current–voltage six-terminal method and near-infrared spectral information was developed and the predictive accuracy of the system was tested in field and laboratory experiments. The traditional current–voltage six-terminal method is used to fuse the spectral information at the moisture-sensitive wavelength of 1450 nm to predict SM and then a correction factor Q was proposed to eliminate the influence of SM on EC. In addition, the linear relationship between soil apparent electrical conductivity (or bulk soil electrical conductivity, ECa) and SM, the exponential relationship between SM and spectral reflectance (R) were used to indirectly obtain the exponential relationship between ECa and R. And then, the binary model of EC is established using the electrical conductivity values of the univariate model (ECeq) obtained from the current–voltage six-terminal method and the univariate model (EClq) obtained from the relationship between the conductivity of the ECa and the R index as an input variable. The experimental data of the univariate model EClq and ECeq and the binary model were analyzed. The field electrical conductivity detection accuracy R2 of the univariate model EClq and ECeq is 0.790 and 0.829 respectively, and the RMSE is 10.113 and 8.078 respectively. While the field electrical conductivity detection accuracy R2 and RMSE of the binary model are 0.899 and 6.535 respectively. The laboratory electrical conductivity detection accuracy of R2 for the univariate models EClq and ECeq were 0.874 and 0.893, and the RMSE were 7.129 and 6.663 respectively. The laboratory electrical conductivity detection accuracy of R2 and RMSE for the binary model were 0.942 and 5.346 respectively. From the above result, it can be seen that R2 for the binary model has improved and the RMSE of the model has decreased. Analysis result of the SM prediction model shows that the accuracy of the SM prediction model R2 in the field and laboratory is 0.888 and 0.938 respectively, and the RMSE is 0.339 and 0.239 respectively, which indicates that the accuracy of the SM prediction model can meet the system requirements. The overall experimental results show that the electrical conductivity detection accuracy of the binary model of the detection system fused with spectral data is significantly improved, and the SM prediction accuracy can meet the needs of farmland detection, which can guide scientific and precise management of farmland.

Ensemble HYDRUS-2D modeling to improve apparent electrical conductivity sensing of soil salinity under drip irrigation

Monitoring and mapping soil salinity are valuable for irrigation management and reclamation of salt-affected agricultural soils in arid and semi-arid regions. Proximal measurements of apparent soil electrical conductivity (ECa) can help characterize soil salinity spatial distributions. However, ECa is not solely a function of salinity. ECa is strongly influenced by soil salinity, water content, and edaphic properties such as texture and bulk density. Consequently, monitoring and mapping salinity based on geospatial ECa measurements is challenging in fields with dynamic and spatially complex patterns of salinity and water content, such as occurs under drip irrigation. We conducted a numerical modeling study to evaluate protocols for using proximal ECa sensing in drip irrigated systems, focusing specifically on the measurement distance from the drip-line that consistently identifies areas of high salinity in the rootzone. The measurement distance was evaluated as a function of six irrigation management parameters: soil hydraulic conductivity, irrigation discharge, irrigation interval, solute concentration, root-zone volume, and leaching fraction. HYDRUS-2D was used to run a 729 member ensemble of drip irrigation simulations of water and solute dynamics under different irrigation management scenarios. Two case studies were simulated for clay loam soil: (1) low salinity soil irrigated with high salinity irrigation water and (2) high salinity soil irrigated with low salinity water. Depth-averaged ECa measurements down to the 75 and 150 cm depths, such as can be obtained using an electromagnetic induction (EMI) sensor, were evaluated in the simulations. According to the ensemble results, a reliable EMI measurement distance from the drip-line was about 100 cm for the case of low salinity irrigation in saline soil and adjacent to the drip-line for the high salinity irrigation. The ensemble ECa and EC of saturated paste extract (ECe) distributions were twice as sensitive to the irrigation water salinity level as compared to the other irrigation management parameters. The probabilistic ensemble approach can be extended to a variety of case studies to aid soil scientists and agricultural consultants monitoring and mapping soil salinity with ECa-directed soil sampling for micro-irrigation systems.

Twenty-four years of contrasting cropping systems on a brown chernozem in Southern Alberta: crop yields, soil carbon, and subsoil salinity

Cropping systems with perennial forages and reduced fallow frequency generally increase soil organic carbon and thus subsequent soil health and crop yield. We evaluated the impact of prior cropping systems on subsequent yields and soil properties in a semiarid region by using crop yields as a bioassay of soil health following the termination of a 24-year crop rotation study in the Brown soil zone in Alberta. During 24 growing seasons from 1992 to 2015, the study included three fallow-containing rotations, two annual crop rotations that were cropped continuously, and perennial grass hay, each with two to six fertilizer treatments. During the bioassay period from 2016 through 2020, all plots in the study were uniformly cropped. Compared to unfertilized fallow wheat, soil organic C in the fall of 2015 was 54% higher after 24 years of fertilized grass and up to 14% higher following annual crops in rotations without fallow. The most notable impact of the previous cropping system on yield during the bioassay years was low yield following perennial grass in 2016 and 2018. Soil electrical conductivity measurements showed that subsoil salinity was elevated following perennial grass, demonstrating the importance of subsoil characteristics for healthy soils. Crop yields in the fifth year of the crop bioassay were 10%–20% greater due to reduced fallow frequency or increased crop diversity. The long-term impact of the cropping system on crop yield in this study depended on drought intensity due to counteracting changes in soil organic matter and subsoil salinity.

Effectiveness of Electrostatic Shielding in High-Frequency Electromagnetic Induction Soil Sensing.

High-frequency electromagnetic induction (HFEMI) sensors, operating in the frequency range from 300 kHz to 30 MHz, have been proposed for the measurement of soil electrical conductivity and dielectric permittivity that are related to the physical and chemical properties of soil. Because of the high-frequency operation, the capacitive coupling between the sensor transmitter and receiver coils is comparable to inductive coupling, creating the need for electrostatic shielding. The remnant capacitive coupling after the implementation of shielding can lead to significant difficulties in the sensor signal interpretation, because both coupling mechanisms are highly dependent on the geometry of the HFEMI sensor and applied shield. In this paper, we introduce the discussion on the relationship between the sensor geometry, shielding and the coupling mechanisms for HFEMI soil sensing. We theoretically and experimentally evaluate a typical HFEMI sensor and its shielding in the frequency range of up to 20 MHz and propose a method for evaluating the effectiveness of a shield configuration. In the case study, we experimentally analyze the HFEMI sensor above a saline solution for two shield configurations. The results agree well with the results of a finite element method analysis.

Digital soil assessment in support of a soil information system for monitoring salinization and sodification in agricultural areas

AbstractSalinization and sodification are types of degradation associated with the accumulation of salts and alkaline materials in the soil. They gradually advance unnoticed till severe degradation appears if not properly controlled. Relevant soil information is not routinely kept by many countries thus contributing to their advancement particularly in agricultural areas where their impacts have huge consequences on food security. We developed a 3‐step protocol to detect salinization and sodification advancement at different spatial scales and support soil information system development. The first step establishes an indicator database focusing on measured soil Electrical Conductivity (EC), pH, Exchangeable Sodium Percent (ESP), and a suit of environmental covariates. The second step outlines data harmonization strategies and classification of salinization and sodification problems. It also incorporates digital soil mapping for spatial information on salinization or sodification. The third step has time‐series modelling of salinization and sodification using mixed‐effects approach to improve hotspot detection in agricultural areas. The protocol was tested in Afghanistan, Sudan, and Lesotho using time‐series data between 2001 and 2018. It successfully identified salt and soda accumulation at different soil depths with more than 80% accuracy on holdout samples. In Sudan and Afghanistan, it identified topsoils as more prone to salinization than the subsoils and consequently depicted more salt accumulation in topsoils than subsoils in agricultural areas. In Lesotho, it identified emerging subsoil sodification. The protocol has potential for supporting the development of soil information on salinization and sodification which is currently lacking at many national and regional levels.

Geochemically Defined Space-for-Time Transects Successfully Capture Microbial Dynamics Along Lacustrine Chronosequences in a Polar Desert.

The space-for-time substitution approach provides a valuable empirical assessment to infer temporal effects of disturbance from spatial gradients. Applied to predict the response of different ecosystems under current climate change scenarios, it remains poorly tested in microbial ecology studies, partly due to the trophic complexity of the ecosystems typically studied. The McMurdo Dry Valleys (MDV) of Antarctica represent a trophically simple polar desert projected to experience drastic changes in water availability under current climate change scenarios. We used this ideal model system to develop and validate a microbial space-for-time sampling approach, using the variation of geochemical profiles that follow alterations in water availability and reflect past changes in the system. Our framework measured soil electrical conductivity, pH, and water activity in situ to geochemically define 17 space-for-time transects from the shores of four dynamic and two static Dry Valley lakes. We identified microbial taxa that are consistently responsive to changes in wetness in the soils and reliably associated with long-term dry or wet edaphic conditions. Comparisons between transects defined at static (open-basin) and dynamic (closed-basin) lakes highlighted the capacity for geochemically defined space-for-time gradients to identify lasting deterministic impacts of historical changes in water presence on the structure and diversity of extant microbial communities. We highlight the potential for geochemically defined space-for-time transects to resolve legacy impacts of environmental change when used in conjunction with static and dynamic scenarios, and to inform future environmental scenarios through changes in the microbial community structure, composition, and diversity.

Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.

Predictive Mapping of Solute‐rich Wetlands in the Canadian Prairie Pothole Region Through High‐resolution Digital Elevation Model Analyses

Prairie Pothole Region wetlands are integral in reducing nutrient mobility in prairie watersheds. Wetland soil CaCO3 content likely plays an important role in wetland phosphorus retention capacity. Accurate predictions of the spatial distributions of wetlands with CaCO3-enriched soils would allow for further study on these relationships and prioritized wetland conservation efforts to encourage this ecosystem service. Solute accumulations (including CaCO3) within wetlands are largely determined by the wetland’s topographic position and relationship with groundwater. These characteristics were estimated by predicting spill channel connections between wetlands using LiDAR-derived digital elevation models. Spatial distributions of solute-rich wetlands were predicted with a simple decision tree model that predicts wetlands as either fresh or solute-rich based on approximated hydrologic characteristics (Strahler order and terminal status) determined from the predicted active spill channel networks. The model was trained and tested using measurements of wetland pond water and soil electrical conductivity (i.e., solute-richness) in three study areas within the Saskatchewan Prairie Pothole Region. The model achieved acceptable predictive accuracies based on training and independent validation tests. The proposed methodologies could be incorporated into more complex multivariate models for the purposes of predictive soil mapping or hydrologic studies in the Prairie Pothole Region.

The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones.

Assessing the degree of soil salinity in the Indian Sundarban Biosphere Reserve using measured soil electrical conductivity and remote sensing data–derived salinity indices

The Sundarban Biosphere Reserve (SBR) in India is vulnerable to soil salinity issues arising out of the occurrence of regular floods and storm surges. While much research has investigated various geo-hazards in this Reserve, the soil salinity aspect has remained underemphasized. This study assesses the degree of soil salinity and its spatial distribution in the SBR, using remote sensing and field measured datasets. Eight soil salinity indices extracted from satellite images were statistically correlated with measured electrical conductivity values using the Pearson correlation coefficient. Through this, the Salinity Index (SI–3) and the Normalized Difference Soil Index (NDSI) were determined as the most suitable indices to map the SBR’s soil salinity. The spatial analysis of these indices revealed that the administrative Blocks of Patharpratima, Basanti, Kultali, Sagar, Sandeshkhali-I, Gosaba, and Haroa experienced extremely high level of soil salinity and salt intrusion. The high drainage density and close proximity of these blocks to rivers fosters such high soil salinity. The study calls for efficacious policy measures to lessen the effects of soil salinity on the subsistence prospects of the coastal communities. The methodology adopted in this study can be utilized for similar analysis in other saline-affected areas at various spatial scales.

Source identification and management of perennial contaminated groundwater seepage in the highly industrial watershed, south India.

Perennial contaminated groundwater seepage is threatening the downstream ecosystem of the Kazipally Pharmaceutical industrial area located in South India. The sources of seepage are unknown for the last three decades that challenging the regulatory authorities and industries. In general, water quality monitoring and geophysical techniques are applied to identify the sources. However, these techniques may lead to ambiguous results and fail to identify the seepage sources, especially when the area is urbanized/paved, and groundwater is already contaminated with other leakage sources that have similar chemical compounds. In the present study, a novel and multidisciplinary approach were adopted that includes satellite-based Land Surface Temperature (LST) observations, field-based Electrical Resistivity Tomography (ERT), continuous Soil Electrical Conductivity (SEC) and Volumetric Soil Moisture (VSM%) measurements along with groundwater levels monitoring to identify the sources and to control the seepage. The integrated results identified that the locations with the Standard Thermal Anomaly (STA) in the range of-0.5 to -1°C, VSM% >50%, SEC>1.5mS/cm, bulk resistivity<12Ωm with shallow groundwater levels<3m below ground level (bgl) are potentially contaminated perennial seepage sources. Impermeable sheet piles have been installed across the groundwater flow direction to control the seepage up to 1.5m bgl, where groundwater frequently intercepts land surface. The quantity of dry season groundwater seepage has been declined by 79.2% after these interventions, which in turn minimized the treatment cost of 1,96,283 USD/year and improved the downstream ecosystem.

Prediction of topsoil properties at field-scale by using C-band SAR data

Designing and validating digital soil mapping (DSM) techniques can facilitate precision agriculture implementation. This study generates and validates a technique for the spatial prediction of soil properties based on C-band radar data. To this end, (i) we focused on working at farm-field scale and conditions, a fact scarcely reported; (ii) we validated the usefulness of Random Forest regression (RF) to predict soil properties based on C-band radar data; (iii) we validated the prediction accuracy of C-band radar data according to the coverage condition (for example: crop or fallow); and (iv) we aimed to find spatial relationship between soil apparent electrical conductivity and C-band radar. The experiment was conducted on two agricultural fields in the southern Argentine Pampas. Fifty one Sentinel 1 Level-1 GRD (Grid) products of C-band frequency (5.36 GHz) were processed. VH and VV polarizations and the dual polarization SAR vegetation index (DPSVI) were estimated. Soil information was obtained through regular-grid sample scheme and apparent soil electrical conductivity (ECa) measurements. Soil properties predicted were: texture, effective soil depth, ECa at 0-0.3m depth and ECa at 0-0.9m depth. The effect of water, vegetation and soil on the depolarization from SAR backscattering was analyzed. Complementary, spatial predictions of all soil properties from ordinary cokriging and Conditioned Latin hypercube sampling (cLHS) were evaluated using six different soil sample sizes: 20, 40, 60, 80, 100 and the total of the grid sampling scheme. The results demonstrate that the prediction accuracy of C-band SAR data for most of the soil properties evaluated varies considerably and is closely dependent on the coverage type and weather dynamics. The polarizations with high prediction accuracy of all soil properties showed low values of σVVo and σVHo, while those with low prediction accuracy showed high values of σVVo and low values of σVHo. The spatial patterns among maps of all soil properties using all samples and all sample sizes were similar. In conditions when summer crops demand large amount of water and there is soil water deficit backscattering showed higher prediction accuracy for most soil properties. During the fallow season, the prediction accuracy decreased and the spatial prediction accuracy was closely dependent on the number of validation samples. The findings of this study corroborates that DSM techniques at field scale can be achieved by using C-band SAR data. Extrapolation y applicability of this study to other areas remain to be tested.