Electromagnetic Conductivity Imaging Research Articles

Time-Lapse Electromagnetic Conductivity Imaging for Soil Salinity Monitoring in Salt-Affected Agricultural Regions

In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using electromagnetic induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated soil apparent electrical conductivity (ECa) measurements were taken using an electromagnetic conductivity meter (CMD2) and inverted (using a time-lapse inversion algorithm) to generate electromagnetic conductivity images (EMCIs), representing soil electrical conductivity (σ) distribution. This process involved converting EMCI data into salinity cross-sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated accurate prediction ability of soil salinity with an R2 value of 0.81. The soil salinity cross-sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmers’ income.

Field-scale assessment of soil water dynamics using distributed modeling and electromagnetic conductivity imaging

Field-scale assessment of soil water dynamics using distributed modeling and electromagnetic conductivity imaging

Time‐lapse imaging of soil moisture using electromagnetic conductivity imaging: Wetting phase

AbstractThe Vertosols of the lower Namoi valley in New South Wales, Australia, have been extensively developed for irrigated cotton (Gossypium hirsutum L.) production. However, there are competing demands for water from mining, environmental needs, and agriculture, with climate change forecasts suggesting reduced rainfall and hotter climates. This means irrigators need to improve water efficiency, which requires methods to monitor the soil volumetric water content (θ, m3 m–3). The aim of this study is to assess if a multicoil electromagnetic instrument (DUALEM‐421), which measures soil apparent electrical conductivity (ECa, mS m–1), can be used to value add to limited measurements of θ and allow extrapolation along a uniformly heavy clay irrigation furrow. Specifically, we seek to invert ECa using EM4Soil software to generate electromagnetic conductivity images, whereby the estimates of soil true electrical conductivity (σ, mS m–1) in the topsoil, subsurface, and subsoil are correlated with θ measured by Decagon GS3 sensors installed at the same depths. We also compared the usefulness of inversion of different combinations of DUALEM ECa: DUALEM‐1, DUALEM‐2, and DUALEM‐21. We found estimated σ from inversion of DUALEM‐421 ECa had good agreement (i.e., Lin's concordance = .84) with measured soil bulk electrical conductivity (σb, mS m–1). We explored the relationship between DUALEM‐421 estimated σ and θ using an artificial neural network. The predictions showed the model had good coefficient of determination (R2 = .74) with good accuracy (RMSE = 0.04 m3 m–3) and demonstrated good agreement between measured and predicted θ (Lin's = .84).

Comparing electromagnetic induction instruments to map soil salinity in two-dimensional cross-sections along the Kham-rean Canal using EM inversion software

The introduction of irrigated agriculture through a network of canals has seen enormous growth in agricultural output in northeast Thailand. However, the canals are leaking causing recharge water to interact with an underlying sequence of geological units rich in salts, which has caused secondary soil salinization. Herein, we measure apparent electrical conductivity (ECa) from EM instruments (e.g. Geonics EM38, EM34 and DUALEM-421) along a section of the Kham-rean Canal of the Muang Pea project in Thailand. We inverted the ECa to generate electromagnetic conductivity images (EMCI) using EM4Soil and evaluate these 2-dimensional models of estimates of the electrical conductivity (σ) distribution with depth, against soil properties, including, measured electrical conductivity of saturated soil paste extract (ECe – dS/m). Using a linear regression (LR) calibration we determine the suitability of each instrument to predict ECe from σ. We found a moderate coefficient of determination between σ and ECe by inverting ECa using either a EM38 on the ground (R2 = 0.66) or a height of 0.5 m (0.65). This was also the case inverting DUALEM-1 (0.63) and DUALEM-2 (0.64) ECa. A strong R2 was achieved by inverting DUALEM-421 (0.72) ECa, with generally weak R2 achieved with DUALEM-4 (0.34) and EM34 (0.51). Using a leave-one-out cross validation, good agreement in ECe predictions were achieved by inverting DUALEM-421 (Lin’s = 0.83), followed by moderate predictions when EM38 on the ground or a height of 0.5 m (0.79) and EM34 (0.66) were used. Our interpretation of predicted ECe suggests the approach can identify non-saline and highly saline areas. Our interpretation of the predicted ECe shows non-saline and highly saline areas, where the former represents a likely recharge area. In the centre of the study, the recharge area was characterised by large silt content (>70%), moderately sodic (6 < ESP < 12%) and slightly saline (ECe < 4 dS/m) conditions. The results have implications for Muang Pea project where 400 km of canals remain unsurveyed. We recommend using an EM38, DUALEM-421 or other shallow sensing EM can similarly potentially identify areas where further hydrological investigations can be undertaken and if recharge is confirmed, engineering solutions can be recommended to mitigate recharge and prevent soil salinisation.

Identifying Potential Leakage Zones in an Irrigation Supply Channel by Mapping Soil Properties Using Electromagnetic Induction, Inversion Modelling and a Support Vector Machine

The clay alluvial plains of Namoi Valley have been intensively developed for irrigation. A condition of a license is water needs to be stored on the farm. However, the clay plain was developed from prior stream channels characterised by sandy clay loam textures that are permeable. Cheap methods of soil physical and chemical characterisations are required to map the supply channels used to move water on farms. Herein, we collect apparent electrical conductivity (ECa) from a DUALEM-421 along a 4-km section of a supply channel. We invert ECa to generate electromagnetic conductivity images (EMCI) using EM4Soil software and evaluate two-dimensional models of estimates of true electrical conductivity (σ—mS m−1) against physical (i.e., clay and sand—%) and chemical properties (i.e., electrical conductivity of saturated soil paste extract (ECe—dS m−1) and the cation exchange capacity (CEC, cmol(+) kg−1). Using a support vector machine (SVM), we predict these properties from the σ and depth. Leave-one-site-out cross-validation shows strong 1:1 agreement (Lin’s) between the σ and clay (0.85), sand (0.81), ECe (0.86) and CEC (0.83). Our interpretation of predicted properties suggests the approach can identify leakage areas (i.e., prior stream channels). We suggest that, with this calibration, the approach can be used to predict soil physical and chemical properties beneath supply channels across the rest of the valley. Future research should also explore whether similar calibrations can be developed to enable characterisations in other cotton-growing areas of Australia.

One-dimensional deep learning inversion of electromagnetic induction data using convolutional neural network

SUMMARY Conventional geophysical inversion techniques suffer from several limitations including computational cost, nonlinearity, non-uniqueness and dimensionality of the inverse problem. Successful inversion of geophysical data has been a major challenge for decades. Here, a novel approach based on deep learning (DL) inversion via convolutional neural network (CNN) is proposed to instantaneously estimate subsurface electrical conductivity (σ) layering from electromagnetic induction (EMI) data. In this respect, a fully convolutional network was trained on a large synthetic data set generated based on 1-D EMI forward model. The accuracy of the proposed approach was examined using several synthetic scenarios. Moreover, the trained network was used to find subsurface electromagnetic conductivity images (EMCIs) from EMI data measured along two transects from Chicken Creek catchment (Brandenburg, Germany). Dipole–dipole electrical resistivity tomography data were measured as well to obtain reference subsurface σ distributions down to a 6 m depth. The inversely estimated models were juxtaposed and compared with their counterparts obtained from a spatially constrained deterministic algorithm as a standard code. Theoretical simulations demonstrated a well performance of the algorithm even in the presence of noise in data. Moreover, application of the DL inversion for subsurface imaging from Chicken Creek catchment manifested the accuracy and robustness of the proposed approach for EMI inversion. This approach returns subsurface σ distribution directly from EMI data in a single step without any iterations. The proposed strategy simplifies considerably EMI inversion and allows for rapid and accurate estimation of subsurface EMCI from multiconfiguration EMI data.

Soil electrical conductivity imaging using a neural network-based forward solver: Applied to large-scale Bayesian electromagnetic inversion

In recent years, probabilistic solution to the inversion of electromagnetic induction (EMI) data has been progressively developed for non-invasive subsurface characterization. However, Bayesian inversion of EMI data using forward solvers based on full solution of Maxwell's equation is associated with computationally expensive modelings, particularly for large-scale surveys. Here, we incorporated artificial neural network (ANN) with Bayesian inference to obtain subsurface electromagnetic conductivity image (EMCI) from EMI data down to 10 m depth. In this respect, a complex EMI forward model was replaced by a trained neural network (ANN proxy forward function) that can be evaluated comparably rapidly. The accuracy of the ANN-based forward solver was examined using different synthetic subsurface models. The proposed methodology was applied on EMI data measured with a DUALEM-421 s sensor from 10 ha study site in the Alken Enge area of Denmark. We compared the inversely estimated EMCI with the counterpart obtained from a quasi-three-dimensional (quasi-3D) spatially-constrained deterministic algorithm as a standard code. The network training procedure was performed within few minutes, and once it was trained, the ANN-based forward solver returned roughly 150,000 model responses per second. This value for the EMI forward solver was around 400, demonstrating the computational efficiency of the ANN proxy forward function. The theoretical simulations demonstrated that the ANN-based forward solver accurately mimics the EMI response within the training range. Moreover, the proposed inversion strategy successfully delineated the subsurface EMCI from Alken Enge area. This approach thus facilitates rapid and accurate subsurface conductivity imaging using Bayesian inversion of multi-configuration EMI data, which is particularly pertinent for large-scale measurements.

Prediction of soil salinity and sodicity using electromagnetic conductivity imaging

Salt related problems in soils can refer to an excess of soluble salts (saline soils), a dominance of exchangeable sodium in the soil exchange complex (sodic soils), or a mixture of both situations (saline-sodic soils). These categories are important because the impacts and management vary accordingly. Electromagnetic induction (EMI) methods and inversion techniques have been used to obtain electromagnetic conductivity images of the soil true soil electrical conductivity (σ) which can be used to estimate soil salinity and other soil properties in-depth and over large areas. However the potential to predict both soil salinity and sodicity with these methods has not been fully investigated.In this study, data collected with an EMI instrument (EM38) at two modes and heights and an inversion algorithm were used to obtain σ. Soil samples were collected at five layers to a depth of 1.35 m, at sampling sites along the EMI transects, and used for laboratory determination of the soil physico-chemical properties – electrical conductivity of the soil saturation paste extract (ECe), sodium adsorption ratio (SAR), pH, cation exchange capacity (CEC), exchangeable sodium percentage (ESP), volumetric water content (θ), and particle size distribution. A principal component analysis (PCA) was performed to analyze the correlation between σ and the soil physico-chemical properties. Correlations between σ and ECe, ESP and SAR could be established and prediction results were evaluated using the leave-one-out cross validation method and calculating the root mean square error of prediction (RMSEP). It was possible to predict ECe (RMSEP = 2.03 dS·m−1), SAR (RMSEP = 4.68 (mmolc·L−1)0.5), and ESP (RMSEP = 3.83%) from σ and to classify the soil according to salinity and sodicity. The results show that it is possible to use EMI to monitor soil salinity and sodicity in risk areas rapid and efficiently, which is required to conserve and improve the soil functions.

Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network

Loop-loop electromagnetic induction (EMI) has proven to be efficient for fast and real-time soil apparent electrical conductivity (ECa) measurements. It is important to develop robust and accurate inversion strategies to obtain soil electromagnetic conductivity image (EMCI) from ECa data. Moreover, obtaining an accurate non-linear relationship between subsurface electrical conductivity (σ) and water content (θ) plays a key role for soil moisture monitoring using EMI. Here, we incorporated probabilistic inversion of multi-configuration ECa data with dimensionality reduction technique through the discrete cosine transform (DCT) using training image (TI)-based parametrization to retrieve soil EMCI. The ECa data were measured repeatedly along a 10 m transect using a CMD mini-Explorer sensor. Time-lapse reference data were collected as well to benchmark the inversion results and to find the in-situ relationship between σ and θ. To convert the inversely estimated time-lapse EMCI to the soil moisture, we examined two approaches, namely, Rhoades et al. (1976) model and artificial neural network (ANN). The proposed inversion strategy estimated the soil EMCI with an excellent agreement with the reference counterpart. Moreover, the ANN approach demonstrated superiorities than the commonly used petrophysical model of Rhoades et al. (1976) to obtain spatiotemporal images of θ from time-lapse EMCI. The results demonstrated that incorporation of the DCT-based probabilistic inversion of ECa data with the ANN approach offers a great promise for accurate characterization of the temporal wetting front and root zone soil moisture.

Mapping soil salinity using electromagnetic conductivity imaging—A comparison of regional and location‐specific calibrations

AbstractSoil salinization limits agricultural productivity and can ultimately cause desertification and land abandonment. One approach to assess soil salinity over large areas efficiently is to use electromagnetic instruments to measure the soil apparent electrical conductivity (ECa, mS m−1). ECa data can be then inverted to generate electromagnetic conductivity images (EMCIs), which provide the vertical distribution of the soil electrical conductivity (σ, mS m−1). In this study, we collected ECa data using an EM38 instrument across four locations with different levels of salinity in an important agricultural area of alluvial origin in Portugal. Using an inversion algorithm, we generated EMCIs and evaluated the potential for prediction of the electrical conductivity of the saturated soil paste extract (ECe). The main aim of our study is to compare regional and location‐specific calibrations in terms of ability to predict ECe from EMCIs. The results showed that the regional calibration predicted ECe unbiased, precisely (root mean square error [RMSE] = 2.54 dS m−1), and with strong concordance (0.93). The location‐specific calibration also predicted ECe unbiased, precisely (RMSE = 1.67 dS m−1), and with strong concordance (0.97). We conclude that the location‐specific calibration has slightly better prediction results, but the regional calibration is more practical for mapping soil salinity in the study area because it can be used at any new location without the need for new calibration. The prediction results at locations 3 and 4 show high to severely high soil salinity, which can compromise agricultural productivity. Therefore, monitoring soil salinity is required to conserve and improve agricultural productivity in the south of the study area.

Probabilistic Inversion of Multiconfiguration Electromagnetic Induction Data Using Dimensionality Reduction Technique: A Numerical Study

Core Ideas DCT‐based inversion of EMI data successfully recovers subsurface conductivity images. Training image‐based parameterization ensures accurate subsurface characterization. Using ECa data pseudosection as a training image provides an adequate prior. Low‐frequency loop–loop electromagnetic induction (EMI) offers several key advantages over many other geophysical techniques for proximal soil sensing. Yet, because of problems with the inversion of measured apparent electrical conductivity (ECa) data, application of EMI for geophysical imaging and interpretation is limited. In this study, a Bayesian inference was used to obtain electromagnetic conductivity images (EMCIs) from multiconfiguration ECa data. This approach allows analysis of highly nonlinear problems and renders an ensemble of models obtained from the posterior distribution that can be used to explore parameter uncertainty. In this respect, generalized formal likelihood function was used to more accurately describe the sensitivity of the posterior distribution to residual assumptions. Discrete cosine transform (DCT) was employed as a model compression technique to reduce the number of unknown parameters in the inversion. The DCT parameterization was performed using training image (TI)‐based geostatistical simulations considering the ECa data pseudosection as a TI. The potential of the proposed approach was examined through different theoretical scenarios. The estimated subsurface EMCI shows excellent agreement with the original synthetic models subject to the appropriate choice of prior information. Moreover, DCT parameterization reduces the number of unknown parameters, increasing accuracy of the inversion with the Bayesian procedure. The proposed approach ensures accurate and high‐resolution characterization of subsurface conductivity layering from measured ECa values.

Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

AbstractIn the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (ECp – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R2 = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (ECe – dS/m). Our interpretation based on estimates of ECe suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.

Characterization of field‐scale dryland salinity with depth by quasi‐3d inversion of DUALEM‐1 data

AbstractTo understand the limitations of saline soil and determine best management practices, simple methods need to be developed to determine the salinity distribution in a soil profile and map this variation across the landscape. Using a field study in southwestern Australia, we describe a method to map this distribution in three dimensions using a DUALEM‐1 instrument and the EM4Soil inversion software. We identified suitable parameters to invert the apparent electrical conductivity (ECa – mS/m) data acquired with a DUALEM‐1, by comparing the estimates of true electrical conductivity (σ – mS/m) derived from electromagnetic conductivity images (EMCI) to values of soil electrical conductivity of a soil‐paste extract (ECe) which exhibited large ranges at 0–0.25 (32.4 dS/m), 0.25–0.50 (18.6 dS/m) and 0.50–0.75 m (17.6 dS/m). We developed EMCI using EM4Soil and the quasi‐3d (q‐3d), cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor (λ) of 0.07. Using a cross‐validation approach, where we removed one in 15 of the calibration locations and predicted ECe, the prediction was shown to have high accuracy (RMSE = 2.24 dS/m), small bias (ME = −0.03 dS/m) and large Lin's concordance (0.94). The results were similar to those from linear regression models between ECa and ECe for each depth of interest but were slightly less accurate (2.26 dS/m). We conclude that the q‐3d inversion was more efficient and allowed for estimates of ECe to be made at any depth. The method can be applied elsewhere to map soil salinity in three dimensions.

3D soil water nowcasting using electromagnetic conductivity imaging and the ensemble Kalman filter

Mapping and immediate forecasting of soil water content (θ) and its movement can be challenging. Although inversion of apparent electrical conductivity (ECa) measured by electromagnetic induction to calculate depth-specific electrical conductivity (σ) has been used, it is difficult to apply it across a field. In this paper we use a calibration established along a transect, across a 3.94-ha field with varying soil texture, using an ensemble Kalman filter (EnKF) to monitor and nowcast the 3-dimensional θ dynamics on 16 separate days over a period of 38days. The EnKF combined a physical model fitted with θ measured by soil moisture sensors and an Artificial Neural Network model comprising σ generated by quasi-3d inversions of DUALEM-421S ECa data. Results showed that the distribution of θ was controlled by soil texture, topography, and vegetation. Soil water dried fastest at the beginning after the initial irrigation event and decreased with time and soil depth, which was consistent with classical soil drying theory and experiments. It was also found that the soil dried fastest in the loamy and duplex soils present in the field, which was attributable to deep drainage and preferential flow. It was concluded that the EnKF approach can be used to improve the irrigation efficiency by applying variable irrigation rates across the field. In addition, soil water status can be nowcasted across large spatial extents using this method with weather forecast information, which will provide guidance to farmers for real-time irrigation management.

Potential to map depth-specific soil organic matter content across an olive grove using quasi-2d and quasi-3d inversion of DUALEM-21 data

Owing to conducive economic conditions and a suitable climate Andalucía has become the largest olive oil producing area in the world. However, extensive rill and severe gully erosion are threatening sustainability. To conserve the soil, land management strategies have to be implemented that minimize erosion and enhance at the same time rain water conservation to safeguard crop yields. An indicator of soil quality and the success of soil management will be organic matter content (OM—%). There is therefore a need to measure, map, manage and monitor its content. Proximal sensors such as electromagnetic induction instruments may be useful in mapping this because the apparent soil electrical conductivity (ECa—mS/m) is related to clay, salinity and mineralogy, which influence organic matter content. In this research we collect data from a single frequency and multiple-coil DUALEM-21 along a transect and across an olive grove in the “La Manga” catchment in Setenil de las Bodegas in the southwest of Spain. We inverted the data using EM4Soil software and developed calibrations between estimates of true electrical conductivity (σ—mS/m) with depth against measured OM % using the quasi-2d algorithm along a single transect. We did this by using a fitted linear regression model and by varying the forward modelling algorithm (cumulative function and full solution), inversion algorithm (S1 and S2) and damping factor (λ) to determine a suitable electromagnetic conductivity image for 2-d and 3-d mapping. Our results along a detailed transect showed promise and suggest differences in OM content could be mapped down a topographic sequence. We applied this calibration to a quasi-3d model across the entire olive grove and to predict OM at various depths. The results across the olive grove were compromised in some locations and within geomorphological complex locations in the landscape, such as near the erosion gully where frequent erosion and deposition occurs. We conclude that better results may have been achieved if more detailed ECa data collection was undertaken in and around the gully and also across a larger extent.

Mapping soil salinity and a fresh-water intrusion in three-dimensions using a quasi-3d joint-inversion of DUALEM-421S and EM34 data

In order to understand the drivers of topsoil salinization, the distribution and movement of salt in accordance with groundwater need mapping. In this study, we described a method to map the distribution of soil salinity, as measured by the electrical conductivity of a saturated soil-paste extract (ECe), and in 3-dimensions around a water storage reservoir in an irrigated field near Bourke, New South Wales, Australia. A quasi-3d electromagnetic conductivity image (EMCI) or model of the true electrical conductivity (σ) was developed using 133 apparent electrical conductivity (ECa) measurements collected on a 50m grid and using various coil arrays of DUALEM-421S and EM34 instruments. For the DUALEM-421S we considered ECa in horizontal coplanar (i.e., 1mPcon, 2mPcon and 4mPcon) and vertical coplanar (i.e., 1mHcon, 2mHcon and 4mHcon) arrays. For the EM34, three measurements in the horizontal mode (i.e., EM34-10H, EM34-20H and EM34-40H) were considered. We estimated σ using a quasi-3d joint-inversion algorithm (EM4Soil). The best correlation (R2=0.92) between σ and measured soil ECe was identified when a forward modelling (FS), inversion algorithm (S2) and damping factor (λ=0.2) were used and using both DUALEM-421 and EM34 data; but not including the 4m coil arrays of the DUALEM-421S. A linear regression calibration model was used to predict ECe in 3-dimensions beneath the study field. The predicted ECe was consistent with previous studies and revealed the distribution of ECe and helped to infer a freshwater intrusion from a water storage reservoir at depth and as a function of its proximity to near-surface prior stream channels and buried paleochannels. It was concluded that this method can be applied elsewhere to map the soil salinity and water movement and provide guidance for improved land management.

Monitoring and modelling soil water dynamics using electromagnetic conductivity imaging and the ensemble Kalman filter

Understanding the soil water (θ) dynamics is important in irrigated agriculture. Due to the labour-intensive nature of determining θ, non-invasive electromagnetic (EM) induction techniques have been used. However, predicting depth-specific θ is a challenge because EM instruments measure the integral depth of the soil's apparent electrical conductivity (ECa). Recently, “true” electrical conductivity with depth produced by inverse modelling of ECa has been employed to establish empirical models of θ. However, the potential to combine empirical models with soil physical models using data assimilation such as ensemble Kalman filter (EnKF) has not been explored. Along a 480-m transect with varying soil texture profiles, repeated ECa data were collected using a DUALEM-421S on 20days over a 40-day period. A quasi-2d inversion algorithm was used to convert the temperature corrected ECa data to σ. An empirical model (artificial neural network) that predicted θ was calibrated using σ values, elevation, and topographic wetness index, as well as the mean and range of σ for each depth and each site over the whole study period. A physical soil-water tipping bucket model was also constructed using θ measured by soil moisture sensors. Afterwards, the EnKF approach was applied to 8 profiles along the transect separately combining both the empirical and the physical models. The results with the EnFK modelling (Lin's concordance=0.89) were superior to the physical model and superior or equivalent to the empirical model for loam, clay and duplex soil profiles. In order to improve the prediction of θ dynamics, a more robust physical model could be used. In addition, correction of the diurnal effects of ECa data and redefining the model and measurement errors of the EnFK to account for the temporal dependence should be considered.

Time‐lapse monitoring of soil water content using electromagnetic conductivity imaging

AbstractThe volumetric soil water content (θ) is fundamental to agriculture because its spatiotemporal variation in soil affects the growth of plants. Unfortunately, the universally accepted thermogravimetric method for estimating volumetric soil water content is very labour intensive and time‐consuming for use in field‐scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatiotemporal variation of θ. However, depth‐specific variation in θ, which is important for irrigation management, has been little explored. The objective of this study was to develop a relationship between θ and estimates of true electrical conductivity (σ) and to use this relationship to develop time‐lapse images of soil θ beneath a centre‐pivot irrigated alfalfa (Medicago sativa L.) crop in San Jacinto, California, USA. We first measured the bulk apparent electrical conductivity (ECa – mS/m) using a DUALEM‐421 over a period of 12 days after an irrigation event (i.e. days 1, 2, 3, 4, 6, 8 and 12). We used EM4Soil to generate EM conductivity images (EMCIs). We used a physical model to estimate θ from σ, accounting for soil tortuosity and pore water salinity, with a cross‐validation RMSE of 0.04 cm3/cm3. Testing the scenario where no soil information is available, we used a three‐parameter exponential model to relate θ to σ and then to map θ along the transect on different days. The results allowed us to monitor the spatiotemporal variations of θ across the surveyed area, over the 12‐day period. In this regard, we were able to map the soil close to field capacity (0.27 cm3/cm3) and approaching permanent wilting point (0.03 cm3/cm3). The time‐lapse θ monitoring approach, developed using EMCI, has implications for soil and water use and management and will potentially allow farmers and consultants to identify inefficiencies in water application rates and use. It can also be used as a research tool to potentially assist precision irrigation practices and to test the efficacy of different methods of irrigation in terms of water delivery and efficiency in water use in near real time.

Monitoring scale-specific and temporal variation in electromagnetic conductivity images

In semiarid and arid landscapes, irrigation sustains agricultural activity but because of increasing demands on water resources there is a need to make gains in efficiency. As such spatial variation of soil properties such as clay and salinity needs to be understood because they strongly influence soil moisture availability. One way is to use electromagnetic induction because apparent soil electrical conductivity (ECa) is related to volumetric soil moisture (θ), clay and salinity (ECe). However, depth-specific variation has not been explored. Our aim is to generate electromagnetic conductivity images (EMCIs) by inverting DUALEM-421 ECa and show how true electrical conductivity (σ) can be correlated with θ, clay, ECe and bulk density (ρ) on different days post-irrigation (i.e., 1, 4 and 12 days). Two-dimensional multi-resolution analysis (MRA) is used to show how spatio-temporal variation in σ is scale-specific and how soil properties influence σ at different scales. We study this beneath a pivot irrigated alfalfa crop. We found that σ on days 1 and 4 was correlated with θ (Pearson’s r = 0.79 and 0.61) and clay (0.86 and 0.80) and the dominant scale of variation occurred at 9.3–18.7 m (50.21 % of total variation), >74.7 m (23.18 %) and 4.7–9.3 m (16.29 %). Between 9.3–18.7 and 4.7–9.3 m the variation may be a function of the cutter width (8 m), while >74.7 m may be change in clay and ECe and gantry spacing (~48 m). The sprinkler spacing (1.2 and 1.6 m) explains short-scale variation at 1.2–2.3 m.

Mapping Salinity in Three Dimensions using a DUALEM-421 and Electromagnetic Inversion Software

To implement management plans, the salt content across affected fields and with depth needs mapping. In this study, we developed a method to map the distribution of normal, uniformly saline, and inverted salinity profiles. We did this by establishing a linear regression (LR) between calculated true electrical conductivity (σ) and electrical conductivity of the saturated soil-paste extract (ECe). We estimated σ by inverting the apparent electrical conductivity (ECa) collected from a DUALEM-421. The ECa values were collected along 13 parallel transects spaced 50 m apart. The inversion was performed using a quasi-three-dimensional model available in the EM4Soil software, where we chose the full solution and S1 inversion algorithm with a damping factor (λ) of 0.3. Using cross-validation, the quasi-three-dimensional model yielded a high accuracy (RMSE = 5.28 dS m−1), small bias (mean error [ME] = −0.03 dS m−1), and large R2 (0.88) and Lin's concordance (0.93). While slightly better results were achieved using individual LRs established at each depth increment overall (i.e., RMSE = 4.35 dS m−1, ME = −0.17 dS m−1, R2 = 0.92, and Lin's concordance = 0.96) and with the DUALEM-421 ECa, the inversion approach requires the development of a single LR (i.e., ECe = 4.1253 + 0.0167σ), which enables efficiencies in estimating salinity and allows ECe to be estimated at any depth where σ was estimated within a three-dimensional electromagnetic conductivity image. This can improve understanding of the cause and best management of salinity. Improvements in accuracy and bias can be achieved by collection of ECa on more closely spaced transects.