Electromagnetic Induction Data Research Articles

An agrogeophysical modelling framework for the detection of soil compaction spatial variability due to grazing using field‐scale electromagnetic induction data

AbstractSoil compaction is a regarded as a major environmental and economical hazard, degrading soils across the world. Changes in soil properties due to compaction are known to lead to decrease in biomass and increase in greenhouse gas emissions, nutrient leaching and soil erosion. Quantifying adverse impacts of soil compaction and developing strategies for amelioration relies on an understanding of soil compaction extent and temporal variability. The main indicators of soil compaction (i.e., reduction of pore space, increase in bulk density and decrease in soil transport properties) are relatively easy to quantify in laboratory conditions but such traditional point‐based methods offer little information on soil compaction extent at the field scale. Recently, geophysical methods have been proposed to provide non‐invasive information about soil compaction. In this work, we developed an agrogeophysical modelling framework to help address the challenges of characterizing soil compaction across grazing paddocks using electromagnetic induction (EMI) data. By integrative modelling of grazing, soil compaction, soil processes and EMI resistivity anomalies, we demonstrate how spatial patterns of EMI observations can be linked to management leading to soil compaction and concurrent modifications of soil functions. The model was tested in a dairy farm in the midlands of Ireland that has been grazed for decades and shows clear signatures of grazing‐induced compaction. EMI data were collected in the summer of 2021 and autumn of 2022 under dry and wet soil moisture conditions, respectively. For both years, we observed decreases of apparent electrical resistivity at locations that with visible signatures of compaction such as decreased vegetation and water ponding (e.g., near the water troughs and gates). A machine learning algorithm was used to cluster EMI data with three unique cluster signatures assumed to be representative of heavy, moderately, and non‐compacted field zones. We conducted 1D process‐based simulations corresponding to non‐compacted and compacted soils. The modelled EMI signatures agree qualitatively and quantitatively with the measured EMI data, linking decreased electrical resistivities to zones that were visibly compacted. By providing a theoretical framework based on mechanistic modelling of soil management and compaction, our work may provide a strategy for utilizing EMI data for detection of soil degradation due to compaction.

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.

Machine learning classification of metallic objects using pulse induction electromagnetic data

This paper presents the classification of metallic objects using total and scattered pulse induction electromagnetic data, with a classification accuracy greater than 90%. Machine learning classification is applied to raw electromagnetic induction (EMI) data without the use of a physics-based model. The EMI method is applied to 8 metallic objects placed at increasing distances from 10–55 mm to the EMI sensing system. The EMI sensing system consists of two RL circuits placed in close proximity. Metallic objects are classified using linear algorithms including a perceptron and multiclass logistic regression, and nonlinear algorithms including a neural network, a 1D and 2D convolutional neural network (CNN). EMI data was collected using an experiment in an electromagnetically shielded laboratory. Feature maps are presented that explain the salient components of the EMI data used by the 1D and 2D CNN.

Potential of ground-penetrating radar to calibrate electromagnetic induction for shallow soil water content estimation

Ground-penetrating radar (GPR) and electromagnetic induction (EMI) are used to determine and map soil water content (SWC) in the agricultural landscape. While GPR provides a straightforward estimation of SWC, EMI requires site-specific calibrations mainly based on point-scale measurements such as time domain reflectometry (TDR). However, there is a significant difference in the measurement volumes between EMI and point-scale TDR measurements. This study aimed to enhance the calibration of EMI for estimating SWC in the agricultural landscape by leveraging the larger sampling volumes provided by GPR. Apparent electrical conductivity (ECa) from a multi-coil EMI sensor and dielectric constant from GPR with two center frequencies (500 MHz and 250 MHz) were collected as soil proxies along with TDR measurements. Calibration models were developed under irrigated conditions and the model evaluation was conducted under natural moisture conditions. Correlations were assessed between the proxies from GPR and EMI with TDR-derived SWCs. Simple linear regression (SLR) models were developed between EMI and GPR data to predict SWC. Strong positive correlations (r ≥ 0.80) were observed between the proxies (GPR and the shorter inter-coil spacing (0.32 m) of EMI) and TDR-measured SWC. ECa data from vertical and horizontal coil orientations of the shorter inter-coil spacings were selected to develop SLR models with GPR. The SLR models with the GPR 500 MHz frequency showed a higher coefficient of determination (R2 > 0.70) for both coil orientations of EMI. Results showed the potential of using GPR to calibrate EMI for shallow SWC estimation (below 0.5 m). Nevertheless, the EMI estimated SWCs were not effectively verified with GPR-estimated SWCs, which could be attributed to specific site conditions in the study area. Further research must focus on improving the calibration and model evaluation by incorporating the variability of other soil parameters (soil porosity, pore-water conductivity, and soil salinity) that may affect the SWC–ECa relationship.

Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering

IntroductionThe mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. MethodsMulti-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.Results and discussionThe method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.

Artificial Bee Colony Algorithm with Adaptive Parameter Space Dimension: A Promising Tool for Geophysical Electromagnetic Induction Inversion

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method.

Assessing soil moisture variability in a vineyard via frequency domain electromagnetic induction data

IntroductionIn agriculture, accurate hydrological information is crucial to infer water requirements for hydrological modeling, as well as for appropriate water management.MethodsTo achieve this purpose, geophysical frequency domain electromagnetic induction (FDEM) measurements are increasingly used for integration with traditional point-scale measurements to provide effective soil moisture estimations over large areas. The conversion of electromagnetic properties to soil moisture requires specific tools that must take into account the spatial variability of the two measurements and the data and model uncertainties. In a vineyard of about 4.5 ha located in Southern Italy, we tested an innovative assessment approach that uses a freeware code licensed from USGS, MoisturEC, to integrate electromagnetic data, collected with a CMD Mini-Explorer electromagnetic sensor, and point-scale soil moisture data.ResultsAbout 30,000 data measurements of apparent electrical conductivity (sa) allowed us to build a 3D inverted electromagnetic model obtained via an inversion process. Soil properties at different depths were inferred from the FDEM model and confirmed through the ground truth sampling.DiscussionThe data analysis tool allowed a more accurate estimation of the moisture distribution of the investigated area by combining the accuracy of the point-scale soil moisture measurements and the spatial coverage of the electrical conductivity (EC) data. The results confirmed the capability of the electromagnetic data to accurately map the moisture content of agricultural soils and, at the same time, the need to employ integrated analysis tools able to update such quantitative estimations in order to optimize soil and water management.

Optimizing phosphorus fertigation management zones using electromagnetic induction, soil properties, and crop yield data under semi-arid conditions.

The impact of climate change on water resource availability and soil quality is more and more emphasized under the Mediterranean basin, mostly characterized by drought and extreme weather conditions. The present study aims to investigate how electromagnetic induction technique and soil mapping combined with crop yield data can be used to optimize phosphorus (P) use efficiency by chickpea crop under drip fertigation system. The study was carried out on a 2.5-ha agricultural plot and the agronomic experiments in two growing cycles of chickpea crop. Soil spatial variability was first assessed by the measurement of soil apparent electrical conductivity (ECa) using the CMD Mini-Explorer sensor, and then, soil physicochemical properties were evaluated based on an oriented soil sampling scheme to explore other soil spatial variabilities influencing chickpea yield and quality. Data from the first agronomic experiment were used in geostatistical, multiple linear regression (MLR), and fuzzy c-means unsupervised classification algorithms to properly identify P drip fertigation management zones (MZs). Results from the Person's correlation analysis revealed that chickpea grain yield was more influenced by soil ECa (r = - 0.56), pH (r = - 0.84), ECe (r = - 0.6), P content (r = 0.72), and calcium (Ca) content (r = - 0.83). The proposed MLR-based model to predict chickpea grain yield showed good performances with a normalized root mean square error (NRMSE) of 0.11% and a coefficient of determination (R2) equal to 0.69. The identified MZs were verified by the one-way variance analysis for the studied soil and plant attributes, revealing that the first MZ1 presents a high grain yield, high soil P content, and low ECa. The low fertility MZ2 located in the south part of the studied site presented a low chickpea grain yield due to the low P content and the high ECa. Moreover, the application of P-variable rate fertigation regimes in the second field experiment significantly improved P use efficiency, chickpea grain yield, seed quality, and farmer income by 18%, 12%, 9%, and 136 $/ha, respectively, as compared to the conventional drip fertigation practices. The approach proposed in this study can greatly contribute to optimizing agro-input use efficiency under drip fertigation system, thereby improving farmers' incomes, preserving the ecosystem, and ensuring sustainable cropping systems in the Mediterranean climate.

Frequency domain electromagnetic calibration for improved detection of sand intrusions in river embankments

Sand intrusions pose significant risks to river embankments due to potential flow pathways that can lead to instability during flood events. Visual inspection is a first step to recognize critical segments, but it does not deliver information about the subsurface. In this context, the electromagnetic induction (EMI) technique is a useful method for preliminary zoning at regional scale while the electrical resistivity tomography (ERT) method, widely used for hydrological purposes, is considered among the most reliable techniques for local subsurface imaging. A major sand intrusion within the levees of the Brenta River, located near Venice (northern Italy), resulted in water seeping during seasonal floods and posed severe threats to embankment stability. ERT and EMI techniques, along with geotechnical investigations, were the best survey choices to address the problem. Resistivity profiling successfully imaged the sand body geometry within and underneath the levee, and results correlated nicely with borehole stratigraphy. A first multiarray EMI device, which represented a faster and less expensive survey, was deployed to map further anomalies along nearby levees, but results were not satisfactory because the inverted profile failed to image the known intrusion. A second multiarray EMI device, with larger coil spacing, was also tested. Although it performed better in detecting the intrusion, results were still below expectations. A calibration procedure based on Pearson's coefficients and using ERT as a reference was then devised and implemented to correct the EMI data prior to carrying out inversion. The procedure was successful for both EMI data sets, leading to realistic subsurface resistivity in the inverted sections. EMI measurements could then be recovered and interpreted correctly to estimate subsurface textures. The possibility of calibrating EMI data and obtaining subsurface resistivity images comparable to standard ERT profiling is an important improvement for cost-effective EMI surveying of river embankments to mitigate flood hazards.

The power of integrating proximal and high-resolution remote sensing for mapping SOC stocks in agricultural peatlands

Background and aimsSoil electrical conductivity (ECa) data derived from electromagnetic induction (EMI) is valuable for estimating peat thickness and soil organic carbon stocks (SOCstocks). However, generating ECa maps locally using geostatistics limits the coverage area. This study explores the use of digital soil mapping (DSM) with random forest (RF) and universal kriging (UK) to create high-resolution ECa maps from field survey EMI data. The objective is to enhance the predictive accuracy of SOCstocks models in peatlands by incorporating these ECa maps as environmental variables.MethodsThree scenarios were evaluated, combining different environmental variables and modelling techniques for ECa mapping. Scenario 1 used spectral indices from RapidEye satellite data and RF. Scenario 2 included spectral indices and terrain derivatives from LiDAR, with RF. Scenario 3 integrated spectral indices, terrain derivatives from LiDAR, and UK. Afterwards, we evaluated the effectiveness of adding ECa maps as environmental variables for SOCstocks mapping. Finally, we incorporated ECa maps from scenario 2 and RF in three ways: (a) scenario 2 variables only, (b) ECa2 with scenario 2 variables, and (c) ECa3 with scenario 2 variables.ResultsScenarios 2 (ECa2) and 3 (ECa3) outperformed scenario 1 (ECa1). The inclusion of ECa maps significantly improved the accuracy of SOCstocks models.ConclusionOur study demonstrates that DSM, combined with RF and UK techniques, enables the generation of high-resolution ECa maps from field surveys in peatlands. Incorporating these ECa maps as environmental variables enhances the accuracy of SOCstocks mapping, providing valuable insights for peatland management and carbon stock estimation.Graphical abstract

Inversion of VLF-R data using a non-linear rank order smoothing constraint and its implementation to image surface rupture of the North Anatolian Fault in Başiskele, Kocaeli, NW Turkey

Inversion of electromagnetic induction data is often realized using smoothing operators for regularizing the process. These operators are applied using derivative matrices, penalizing differences between neighboring mesh cells. The resulting smoothing effect is similar to that of low-pass Gaussian filters and may cause difficulties due to the smoothed boundaries between structures. To prevent such ambiguities, non-linear rank filters are applied widely in image processing applications to filter low frequency variations while keeping boundaries. Correspondingly, we have defined a non-linear rank order smoothing operator for regularization. The effects of the defined parameters are demonstrated on synthetic models and the operator is observed to be able to provide better defined boundaries. The implementation of the operator on field data is realized using single-frequency VLF-R data collected on profiles traversing the surface rupture of the North Anatolian Fault in Kocaeli, Turkey. In the study area, ERT data are also collected on a single profile, and its smooth inversions are realized individually and jointly with VLF-R data for comparisons. Inversions show that the recovered subsurface structure using the rank order smoothing is closer to that indicated by the ERT data and previous studies. According to the recovered models, the clayey fillings in the fault rupture are acting as a barrier affecting the horizontal fluid flow in the near-surface and the resulting water accumulations around the fault rupture may cause susceptibility for earthquake induced damages.

Constraints on MTZ Water Content From Joint Inversion of Diurnal Variations and Magnetospheric Signals

AbstractElectrical conductivity of mantle minerals is highly sensitive to water content, so estimates of this physical parameter based on long‐period electromagnetic induction data can help to constrain mantle hydration. Reliable constraints require conductivity model with sufficient resolution at relevant depths, as well as a comprehensive uncertainty analysis. In this global induction study, we combine data from the primarily ionospheric diurnal variation and longer‐period magnetospheric ring current bands. Joint inversion of these two data sources allows significantly improved vertical resolution of radial conductivity variations, especially in the mantle transition zone (MTZ). We then evaluate how uncertainties in the resulting conductivity model, in thermal models, and in laboratory results affect water content estimates, focusing on the MTZ. Our analysis suggests that the conductivity model is best explained by a dry MTZ with pyrolitic composition. Considering the uncertainties, an upper bound on average water content in the MTZ is 0.3 wt.% of water.

Calibration of near‐surface multi‐frequency electromagnetic induction data

AbstractThe frequency‐domain electromagnetic induction method is a very efficient and convenient method to investigate a site of interest and for a large range of applications including hydrogeology and archaeology. Besides an easy infield acquisition, the data can be used to estimate the electrical, magnetic and, in certain cases, dielectric properties of the ground. It does, however, require calibration to get reliable quantitative values of these properties. We propose here an original and reliable self‐calibration method, specific to multi‐frequency measurements in the range of (5–100 kHz)‐based only on the consistency between the data at different acquisition frequencies. Contrary to other existing cross‐calibration procedures, no additional measurements with other methods are necessary. This self‐calibration (quadrature and in‐phase components of the electromagnetic induction signal) procedure is tested at a test site located at the centre for studies on risks, the environment, mobility and urban planning Normandie‐Centre facilities (Rouen, France). The developed quadrature self‐calibration has been successfully compared with the usual electrical resistivity tomography – electromagnetic induction cross‐calibration procedure. The main results reveal the potential of systematic assessment of all electromagnetic parameters from multi‐frequency and multi‐configuration electromagnetic induction data: The electrical resistivity and the magnetic susceptibility are estimated from the low‐frequency range, whereas the highest part of the frequency range is used for the dielectric permittivity.

Forward Electromagnetic Induction Modelling in a Multilayered Half-Space: An Open-Source Software Tool

Electromagnetic induction (EMI) techniques are widely used in geophysical surveying. Their success is mainly due to their easy and fast data acquisition, but the effectiveness of data inversion is strongly influenced by the quality of sensed data, resulting from suiting the device configuration to the physical features of the survey site. Forward modelling is an essential tool to optimize this aspect and design a successful surveying campaign. In this paper, a new software tool for forward EMI modelling is introduced. It extends and complements an existing open-source package for EMI data inversion, and includes an interactive graphical user interface. Its use is explained by a theoretical introduction and demonstrated through a simulated case study. The nonlinear data inversion issue is briefly discussed and the inversion module of the package is extended by a new regularized minimal-norm algorithm.

Rapid Object Depth Estimation From Position-Referenced EMI Data Using Machine Learning

State-of-the-art methods for localization and detection of small metallic objects using electromagnetic induction (EMI) sensing usually struggle due to the strong correlation between the intrinsic parameters of the object and the object’s depth. In this article, we present a machine-learning-based approach for rapid estimation of metallic object depth from line-scan EMI data. The 1-D-convolutional neural network (1D-CNN), trained on a simulated dataset, takes advantage of metal detector (MD) spatial response to extract features from which depth is inferred. Experimental evaluation using a mono-coil pulse induction MD and an electromagnetic (EM) tracking system was performed under laboratory conditions on a large dataset containing arbitrarily oriented objects of different sizes, shapes, and materials. The nonlinear least-squares (NLSs) inversion was employed as the benchmark method for comparison. The proposed solution shows superior performance over NLS at depths >10 cm. From two passes of MD over the object at depths within the range of 2.5–15 cm, our method yields a median absolute error (MedAE) on the order of millimeters.

Structurally constrained inversion by means of a Minimum Gradient Support regularizer: examples of FD-EMI data inversion constrained by GPR reflection data

SUMMARYMany geophysical inverse problems are known to be ill-posed and, thus, requiring some kind of regularization in order to provide a unique and stable solution. A possible approach to overcome the inversion ill-posedness consists in constraining the position of the model interfaces. For a grid-based parameterization, such a structurally constrained inversion can be implemented by adopting the usual smooth regularization scheme in which the local weight of the regularization is reduced where an interface is expected. By doing so, sharp contrasts are promoted at interface locations while standard smoothness constraints keep affecting the other regions of the model. In this work, we present a structurally constrained approach and test it on the inversion of frequency-domain electromagnetic induction (FD-EMI) data using a regularization approach based on the Minimum Gradient Support stabilizer, which is capable to promote sharp transitions everywhere in the model, i.e., also in areas where no structural a prioriinformation is available. Using 1D and 2D synthetic data examples, we compare the proposed approach to a structurally constrained smooth inversion as well as to more standard (i.e., not structurally constrained) smooth and sharp inversions. Our results demonstrate that the proposed approach helps in finding a better and more reliable reconstruction of the subsurface electrical conductivity distribution, including its structural characteristics. Furthermore, we demonstrate that it allows to promote sharp parameter variations in areas where no structural information are available. Lastly, we apply our structurally constrained scheme to FD-EMI field data collected at a field site in Eastern Germany to image the thickness of peat deposits along two selected profiles. In this field example, we use collocated constant offset ground-penetrating radar (GPR) data to derive structural a priori information to constrain the inversion of the FD-EMI data. The results of this case study demonstrate the effectiveness and flexibility of the proposed approach.

Feasibility assessment on use of proximal geophysical sensors to support precision management

AbstractA study was conducted at three sites in North Dakota to strengthen understanding of the usefulness of different proximal geophysical data types in agricultural contexts of varying pedology. This study hypothesizes that electromagnetic induction (EMI), gamma‐ray sensor (GRS), cosmic‐ray neutron sensor (CRNS), and elevation data layers are all useful in multiple linear regression (MLR) predictions of soil properties that meet expert criteria at three agricultural sites. In addition to geophysical data collection with vehicle‐mounted sensors, 15 soil samples were collected at each site and analyzed for nine soil properties of interest. A set of model training data was compiled by pairing the sampled soil property measurements with the nearest geophysical data. Eleven models passed expert‐defined uncertainty criteria at Site 1, 16 passed at Site 2, and 14 passed at Site 3. Electrical conductivity (EC), organic matter (OM), available water holding capacity, silt, and clay were predicted at Site 1 with an R‐squared of prediction > .50 and acceptable root mean square error of prediction (RMSEP). Bulk density (BD), OM, available water capacity, silt, and clay were predicted with > .50 and acceptable RMSEP at Site 2. At Site 3, no soil properties were predicted with acceptable RMSEP and an > .50. These results confirm feasibility of our method, and the authors recommend the prioritization of EMI data collection if geophysical data collection is limited to a single mapping effort and calibration soil samples are few.

Estimating grapevine-relevant physicochemical soil zones using apparent electrical conductivity and in-phase data from EMI methods

This study explores the ability of the frequency domain electromagnetic induction (EMI) method to produce physicochemical soil maps with relevance to grapevine vigor. The study was conducted in the Médoc area of the Bordeaux wine region in France. A flexible clustering approach was used with both the apparent electrical conductivity and the in-phase components of EMI measurements. Soil zones with distinct physicochemical signatures were successfully identified, primarily due to the inclusion of in-phase data in the clustering method. Importantly, this approach does not use prior information to guide zone classification and does not require calibration of EMI measurements. Extensive soil texture and physicochemical measurements were used to compare the different zones identified by the clustering approach. Grapevine vigor was assessed using normalized vegetation index (NDVI) data to help conceptualize the critical physicochemical variables of the soil. It was evident that NDVI and EMI data were correlated to organic matter, nitrogen, CEC, pH, CaO, and copper concentration in one of the investigated sites. Unlike previous work in other Bordeaux soil viticulture areas, soil texture at these sites was relatively homogenous and consequently did not influence plant vigor substantially. This work demonstrates the value of the in-phase component of EMI measurements to estimate soil zonation. More generally, this work demonstrates the ability of coupled EMI and NDVI surveys to inform viticulture at scales relevant to management decisions.

Development of a crop water use monitoring system using electromagnetic induction survey

Monitoring soil water use at the plot level is essential for studying the plant’s response to drought or irrigation. However, the current soil monitoring system using moisture sensors is impractical. Soil electromagnetic induction (EMI) surveys can be a rapid, convenient, and non-invasive solution for monitoring soil moisture changes at the plot level. This study developed a crop water use monitoring system for the plot level using an EMI instrument that collects measurements at 0, 20, 40, 60, and 80 cm above the ground surface. The EMI data were converted to bulk soil electrical conductivity (σ) to the depth 1.5 m via an inversion technique and correlated with measured volumetric moisture contents. The EMI system was first tested for variation in ambient temperature. Within the temperature range of 25.5–34 °C, the standard deviation varied between 0.84 and 3.45 mS m−1 with coefficient of variations 1.54–9.26%. The system was then tested on a field trial at Narrabri, New South Wales, Australia, to measure the water use of chickpea genotypes grown over a season. The total water use of chickpeas obtained from EMI surveys correlated highly with values obtained from the Neutron Moisture Meter (NMM) (R2 = 0.73). Time-series of water content, as measured by the EMI survey, could differentiate the water use of different genotypes. In conclusion, the EMI survey is an efficient technique for monitoring crop water use.

Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems.

Data measured using electromagnetic induction (EMI) systems are known to be susceptible to measurement influences associated with time-varying external ambient factors. Temperature variation is one of the most prominent factors causing drift in EMI data, leading to non-reproducible measurement results. Typical approaches to mitigate drift effects in EMI instruments rely on a temperature drift calibration, where the instrument is heated up to specific temperatures in a controlled environment and the observed drift is determined to derive a static thermal apparent electrical conductivity (ECa) drift correction. In this study, a novel correction method is presented that models the dynamic characteristics of drift using a low-pass filter (LPF) and uses it for correction. The method is developed and tested using a customized EMI device with an intercoil spacing of 1.2 m, optimized for low drift and equipped with ten temperature sensors that simultaneously measure the internal ambient temperature across the device. The device is used to perform outdoor calibration measurements over a period of 16 days for a wide range of temperatures. The measured temperature-dependent ECa drift of the system without corrections is approximately 2.27 mSm−1K−1, with a standard deviation (std) of only 30 μSm−1K−1 for a temperature variation of around 30 K. The use of the novel correction method reduces the overall root mean square error (RMSE) for all datasets from 15.7 mSm−1 to a value of only 0.48 mSm−1. In comparison, a method using a purely static characterization of drift could only reduce the error to an RMSE of 1.97 mSm−1. The results show that modeling the dynamic thermal characteristics of the drift helps to improve the accuracy by a factor of four compared to a purely static characterization. It is concluded that the modeling of the dynamic thermal characteristics of EMI systems is relevant for improved drift correction.