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

The 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 (EC_a – 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.     

Measurement of soil water and nutrients by its electrical properties

Abstract

An instrument for measurement of soil dielectric constant ε _r , electrical conductivity EC _a, and soil temperature was tested on soils under potato crop to investigate contents of soil volumetric water θ and nutrients for eventual use in a field crop model.

To approximate the dependence of θ on ε _r , a logarithmic equation was chosen. Satisfactory results were obtained on stone-free areas, with the mean relative variance between θ-values determined by dielectric constant and converted from a gravimetric method remaining within the limits of measuring error. However, variances were higher for stony soils, with ε _r -values at the same θ being considerably higher. To reconcile data from stony and stone-free soils, a formula was composed.

Salinity, calculated by a semi-empirical model based on Hilhorst's theory using measured values of EC _a, ε _r , and soil temperature, correlated well with contents of K and Mg in the soil. A lower correlation resulted for P, and was practically absent for Ca. Inequality of these regression equations at different measuring sites demonstrates the necessity of considering soil pH when assessing plant nutrients in the soil.     

Evaluating soil salinity status from bulk electrical conductivity and permittivity

In the range of volumetric water content, θ, from about 0.12 cm³ cm^–3 to saturation the relation between bulk electrical conductivity, C_b, and bulk electrical permittivity, ε, of mineral soils was observed to be linear. The partial derivative ?C_b/?ε appeared independent of the moisture content and directly proportional to soil salinity. We found that the variable X_s = ?C_b/?ε determined from in situ measurements of C_b(θ > 0.2) and ε(θ > 0.2) can be considered as an index of soil salinity, and we call it the ‘salinity index’. Knowing the index and sand content for a given soil we could calculate the electrical conductivity of the soil water, C_w, which is a widely accepted measure of soil salinity. The two variables from which the salinity index can be calculated, i.e. C_b and ε, can be read simultaneously from the same sensor by time-domain reflectometry. Quantities and symbols a constant /dS m^–1 b constant c constant /dS m^–1 C _b electrical conductivity of bulk soil /dS m^–1 C _b′ constant equal to 0.08 dS m^–1 C _s electrical conductivity of a solution used to moisten soil samples /dS m^–1 C _w electrical conductivity of soil water defined as the soil salinity /dS m^–1 C _wref reference salinity (that truly existing) resulting from the procedure of moistening samples, expressed as C_s + C_r/dS m^–1 C _r baseline value of C_s due to residual soluble salts present in the soil /dS m^–1 d constant D dry soil bulk density /g cm^–3 l slope r ratio S sand content /% by weight t time /s X _s salinity index /dS m^–1 X _si initial salinity index when distilled water is used to moisten soil samples /dS m^–1 Y a moisture-independent salinity-dependent variable /dS m^–1 z coordinate along direction of flow of the soil solution ε′ constant equal to 6.2 ε relative bulk electrical permittivity (dielectric constant) of the soil θ volumetric water content determined thermogravimetrically using oven-drying /cm³ cm^–3     

TDR estimation of electrical conductivity and saline solute concentration in a volcanic soil

Relative to montmorillonitic or kaolinitic soils, volcanic soils have atypical dielectric characteristics that interfere with the applicability of the Time Domain Reflectometry (TDR) technique for soil moisture (θ) determination when common, empirical calibration equations are used. This particular dielectric response affects estimation of salinity in volcanic soils. Six TDR-based methods to estimate bulk electrical conductivity (σ_a) on a range of KCl saline reference solutions were compared, with Nadler's method giving the best results (R_1:1²=0.988). Three models (linear, non-linear and empirical) for predicting soil solution electrical conductivity (σ_w) based on σ_a and θ, were experimentally tested on 24 hand-packed soil columns varying in salinity (Br⁻) from 0.2 to 4.0 dS m⁻¹, each in four θ levels (36–58%). Rhoades' linear model performed better, especially for large water contents, than the other two (R_1:1²=0.986 vs. 0.976 and 0.983, respectively). An interpretation in terms of mobile vs. immobile volumetric fractions of water present in volcanic soils is suggested as a possible explanation for these results. The empirical model resulted over-parameterized and an alternative equation with fewer non-correlated parameters, σ_a=(aθ²+bθ)σ_w+cθ², is proposed and tested with good results in volcanic soils from the Canary Islands and New Zealand. The equation encompasses both the relative dielectric dominance of the mobile water fraction at high water content typical of volcanic soils, and of the immobile fraction at low water contents. Simultaneous measurements made with a standard four-electrode probe and TDR gave good correlation (R²=0.964). A good linear correlation was also found between tracer concentration in the soil solution and σ_w (R²=0.960). Nadler's and the new empirical model also tested with good results under dynamic (flow) conditions during a miscible displacement experiment in a large monolith using bromide as a tracer. The method reveals itself as a robust tool for solute transport studies under controlled salinity conditions in a volcanic soil.     

Establishing soil electrical conductivity ‐ Depth relations from electromagnetic induction measurements

Abstract

Principles of electromagnetic induction (EM) and field calibration approaches are discussed as they pertain to the application of EM to soil systems for the purpose of deriving soil electrical conductivity ‐ depth relations. Evidence is provided to support the utility of EM‐derived estimates of EC_a‐depth relations. Limitations of using electromagnetic induction to determine EC_a for discrete depth intervals through the soil are discussed. Current research designed to increase the accuracy of EC_a‐depth determinations by dealing with the spatial variability problem associated with salinity in soil and by mitigating some of the inherent limitations of the calibration approaches is described.     

Spatial evaluation of soil salinity using the WET sensor in the irrigated area of the Segura river lowland

The electrical conductivity of the water within the soil pores (EC_p) measured with the WET sensor, appears to be a reliable estimate of soil salinity. A methodology combining the use of the WET sensor along with geostatistics was developed to delimit and evaluate soil salinity within an irrigated area under arid to semiarid Mediterranean climate in SE Spain. A systematic random sampling of 104 points was carried out. The association between EC_p and the saturation‐extract electrical conductivity (EC_se) was assessed by means of correlation analysis. The semivariograms for EC_p were obtained at three different soil depths. Interpolation techniques, such as ordinary kriging and cokriging, were applied to obtain EC_p levels in the unknown places. For each one of the soil depths, a model able to predict EC_se from EC_p was developed by means of ordinary least squares regression analysis. A good correlation (r = 0.818, p < 0.001) between EC_p and EC_se was found. Spherical spatial distribution was the best model to fit to experimental semivariograms of EC_p at 10, 30, and 50 cm soil depths. Nevertheless, cokriging using the EC_p of an adjacent soil depth as an auxiliary variable provided the best results, compared to ordinary kriging. An analytical propagation‐error methodology was found to be useful to ascertain the contribution of the spatial interpolation and ordinary least squares analysis to the uncertainty of the EC_se mapping. This methodology allowed us to identify 98% of the study area as affected by salinity problems within a rooting depth of 50 cm, with the threshold of EC_se value at 2 dS m^–1. However, considering the crops actually grown and 10% potential reduction yield, the soil‐salinity‐affected area decreased to 83%. The use of sensors to measure soil salinity in combination with geostatistics is a cost‐effective way to draw maps of soil salinity at regional scale. This methodology is applicable to other agricultural irrigated areas under risk of salinization.     

The power-function model for the soil moisture characteristic

The minimum number of parameters required to model the unsaturated soil moisture characteristic, relating volumetric water content (θ) and matric suction (χ), is shown to be two. A third parameter, θ=θ_s at saturation, is required to define its saturation limit. The popular power-function χ/χ_e, = (θ/θ_s)^b is the most general three-parameter model, with χ normalized by a notional air-entry potential, χ_e. When log-transformed, e.g. as In χ= a + bln(θ/θ_s), it gives a good fit to observations over varying ranges of χ. We show, using US, Australian, UK and NZ data, that a,b and θ_s, in this formulation are uncorrelated across a wide range of textures, thus providing a ‘basis set’ of independent parameters. Gregson et al. (1987) used the alternative formulation In χ= a″+ bln(100θ), with χ rescaled to a percentage. Their reported correlation between a″ and b, which led to their ‘one-parameter model’ of the characteristic, is shown to be a mathematical artefact, arising from absorption of the term – b[ln(100)+ ln θ_s] into a″.     

Modelling soil salinity across a gilgai landscape by inversion of EM38 and EM31 data

The soil in arid and semi‐arid areas is often markedly saline, which can severely limit agricultural productivity. Increasingly, geophysical methods are being implemented to map the levels and areal extent of soil salinity. One of the most effective methods is electromagnetic (EM) induction with instruments designed to measure apparent soil electrical conductivity (EC_a). This study describes the generation of electromagnetic conductivity images (EMCIs) by inverting EC_a data obtained with the EM38 and EM31 devices along two closely‐spaced transects by the EM inversion approach in the EM4Soil package. The EM38 EC_a data are shown to be a more effective predictor of soil EC_e. Calibration equations based on calculated true electrical conductivity (σ) and measured electrical conductivity of a saturated soil‐paste extract (EC_e) provide reliable estimates of EC_a. The patterns of σ in a test of the method in soil developed over thick alluvium on a clay plain in central New South Wales, Australia, compare favourably with existing pedological mapping; the mounds and depressions of gilgai were strongly differentiated from the more sandy alluvial sediments that characterize prior stream channels. The overall approach is potentially useful for generating a single calibration equation that can be used to predict EC_e at various depths in the soil. Improvements in EMCI modelling can also be sought by joint inversion of EM with other geophysical datasets.     

Identifying potential management zones in a layered soil using several sources of ancillary information

Variation in soil texture has a profound effect on soil management, especially in texturally complex soils such as the polder soils of Belgium. The conventional point sampling approach requires high sampling intensity to take into account such spatial variation. In this study we investigated the use of two ancillary variables for the detailed mapping of soil texture and subsequent delineation of potential management zones for site‐specific management. In an 11.5 ha arable field in the polder area, the apparent electrical conductivity (EC_a) was measured with an EM38DD electromagnetic induction instrument. The geometric mean values of the EC_a measured in both vertical and horizontal orientations strongly correlated with the more heterogeneous subsoil clay content (r = 0.83), but the correlation was weaker with the homogenous topsoil clay content (r = 0.40). The gravimetric water content at wilting point (θg_{(?1.5 MPa)}) correlated very well (r = 0.96) with the topsoil clay content. Thus maps of topsoil and subsoil clay contents were obtained from 63 clay analyses supplemented with 117θg_{(?1.5 MPa)} and 4048EC_a measurements, respectively, using standardized ordinary cokriging. Three potential management zones were identified based on the spatial variation of both top and subsoil clay contents. The influence of subsoil textural variation on crop behaviour was illustrated by an aerial image, confirming the reliability of the results from the small number of primary samples.     

Investigating soil physical properties and yield response in a grassland field using a dual‐sensor penetrometer and EM38

Precision‐farming applications are mainly based on site‐specific information of soil properties at the field scale. For this purpose, a number of novel sensor techniques have been developed but not intensively tested under different field conditions. This study presents a combined application of a self‐developed dual‐sensor vertical penetrometer (DVP) for measuring volumetric soil water content (VSWC) and cone index (CI), and an EM38 for soil apparent electrical conductivity (EC_a) in a pasture (1.4 ha). To verify the feasibility of the DVP for interpreting the depth‐specific information in the field, not only the soil physical properties and their geographical coordinates were measured, but also geo‐referenced yield data were collected. We found that the yield pattern was quite similar to the soil water‐content pattern of each layer (layer‐1: 5–15 cm; layer‐2: 15–25 cm, layer‐3: 25–35 cm) and EC_a pattern. Using the map‐based comparisons in conjunction with the statistical analyses, the effect of each measured soil physical property (VSWC, CI, and EC_a) on the yield was investigated. The regression between the yield and VSWC at each layer fitted a quadratic equation (R² = 0.515 at 5–15 cm; R² = 0.623, at 15–25 cm; R² = 0.406 at 25–35 cm). The negative correlation between yield and CI at each layer fitted a linear model with R² ≥ 0.510.     

Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China

In coastal China, there is an urgent need to increase land for agriculture. One solution is land reclamation from coastal tidelands, but soil salinization poses a problem. Thus, there is need to map saline areas and identify appropriate management strategies. One approach is the use of digital soil mapping. At the first stage, auxiliary data such as remotely sensed multispectral imagery can be used to identify areas of low agricultural productivity due to salinity. Similarly, proximal sensing instruments can provide data on the distribution of soil salinity. In this study, we first used multispectral QuickBird imagery (Bands 1–4) to provide information about crop growth and then EM38 data to indicate relative salt content using measurements of apparent soil electrical conductivity (EC_a) in the horizontal (EC_h) and vertical (EC_v) modes of operation. Second, we used a fuzzy k‐means (FKM) algorithm to identify three salinity management zones using the normalized difference vegetation index (NDVI), EC_h and EC_v/EC_h. The three identified classes were statistically different in terms of auxiliary and topsoil properties (e.g. soil organic matter) and more importantly in terms of the distribution of soil salinity (EC_e) with depth. The resultant three classes were mapped to demonstrate that remote and proximally sensed auxiliary data can be used as surrogates for identifying soil salinity management zones.     

Prediction of hydraulic conductivity and sorptivity in soils at steady-state infiltration

The purpose of this study was (1) to find a matching factor (u) between infiltration rate and hydraulic conductivity during steady-state infiltration, and (2) to propose equations based on infiltration and soil moisture-retention functions for prediction of the hydraulic conductivity K(θ) within the rapidly (non-capillary) drainable pores (RDP) and capillary-matrix pores of soils. The K(θ) of capillary pores was divided into K(θ)_SDP, K(θ)_WHP and K(θ)_FCP within slowly drainable pores (SDP), water-holding pores (WHP) and fine capillary pores (FCP), respectively. Five soil profiles of calcareous sandy loam, alluvial saline and non-saline clay, located at the Nile Delta, were used to apply the proposed equations. The highest and the lowest values of K(θ)_RDP were observed in calcareous and saline clay soil profiles, respectively. Values of K(θ)_RDP remained higher than those for capillary pores in the studied soils. The predicted values of K(θ) in capillary and non-capillary pores classes were in the expected range for unsaturated hydraulic conductivity. Water sorptivity (S) was determined at initial unsaturated soil water conditions and calculated at steady-state infiltration (S _w) using a derived equation. There was a decrease in S with an increase in soil water content; i.e. at steady-state infiltration, S decreased by 35–40% in calcareous soils and by 45–60% in alluvial clay soils. The parameter values of u and S _w tended to be uniform in calcareous soils, but nonuniform in saline and non-saline clay soils.     

SOIL SALINITY AND SODICITY APPRAISAL BY ELECTROMAGNETIC INDUCTION IN SOILS IRRIGATED TO GROW COTTON

In the Far West Texas region in the USA, long‐term irrigation of fine‐textured valley soils with saline Rio Grande River water has led to soil salinity and sodicity problems. Soil salinity [measured by saturated paste electrical conductivity (EC_e)] and sodicity [measured by sodium adsorption ratio (SAR)] in the irrigated areas have resulted in poor growing conditions, reduced crop yields, and declining farm profitability. Understanding the spatial distribution of EC_e and SAR within the affected areas is necessary for developing management practices. Conventional methods of assessing EC_e and SAR distribution at a high spatial resolution are expensive and time consuming. This study evaluated the accuracy of electromagnetic induction (EMI), which measures apparent electrical conductivity (EC_a), to delineate EC_e and SAR distribution in two cotton fields located in the Hudspeth and El Paso Counties of Texas, USA. Calibration equations for converting EC_a into EC_e and SAR were derived using the multiple linear regression (MLR) model included in the EC_e Sampling Assessment and Prediction program package developed by the US Salinity Laboratory. Correlations between EC_a and soil variables (clay content, EC_e, SAR) were highly significant (p ≤ 0·05). This was further confirmed by significant (p ≤ 0·05) MLRs used for estimating EC_e and SAR. The EC_e and SAR determined by EC_a closely matched the measured EC_e and SAR values of the study site soils, which ranged from 0·47 to 9·87 dS m⁻¹ and 2·27 to 27·4 mmol^1/2 L^−1/2, respectively. High R² values between estimated and measured soil EC_e and SAR values validated the MLR model results. Results of this study indicated that the EMI method can be used for rapid and accurate delineation of salinity and sodicity distribution within the affected area. Copyright © 2012 John Wiley & Sons, Ltd.     

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

To 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 (EC_a – 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 (EC_e) 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 EC_e, 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 EC_a and EC_e 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 EC_e to be made at any depth. The method can be applied elsewhere to map soil salinity in three dimensions.     

Relationship between apparent electrical conductivity and soil physical properties in a Malaysian paddy field

Site-specific crop management, well-established in some developed countries, is now being considered in developing countries such as Malaysia. The apparent electrical conductivity (EC_a) of the soil can be used as an indirect indicator of a number of soil physical properties and even crop yield. Commercially available EC_a sensors can efficiently develop the spatially dense data sets desirable in describing within-field spatial soil variability for precision farming. The main purpose of this study was to generate a variability map of soil EC_a within a Malaysian paddy field using a VerisEC sensor. The EC_a values were then compared with some soil variables within classes after delineation. Measured parameters were mapped using the kriging technique and their correlation with soil EC_a was determined. The study showed that the VerisEC can determine soil spatial variability, and can acquire soil EC_a information quickly. Spatial variability of shallow and deep EC_a showed the same patterns. Estimation of soil properties based on EC_a varied from one soil parameter to another and all could be estimated better by deep EC_a. Cross-validation results showed that shallow and deep EC_a, and also bulk density, gave more accurate estimates compared with other variables.     

SALTIRSOIL: a simulation model for the mid to long‐term prediction of soil salinity in irrigated agriculture

The SALTIRSOIL model predicts soil salinity, sodicity and alkalinity in irrigated land using basic information on soil, climate, crop, irrigation management and water quality. It extends the concept of the WATSUIT model to include irrigation and crop management practices, advances in the calculation of evapotranspiration and new algorithms for the water stress coefficient and calculation of electrical conductivity. SALTIRSOIL calculates the soil water balance and soil solution concentration over the year. A second module, SALSOLCHEM, calculates the inorganic ion composition of the soil solution at equilibrium with soil calcite and gypsum at the soil’s CO₂ partial pressure. Results from comparing predicted and experimentally determined concentrations, observations and predictions of pH, alkalinity and calcium concentration in calcite‐saturated solutions agree to the second significant figure; in gypsum‐saturated solutions the standard difference between observations and predictions is <3% in absolute values. The algorithms in SALTIRSOIL have been verified and SALSOLCHEM validated for the reliable calculation of soil salinity, sodicity and alkalinity at water saturation in well‐drained irrigated lands. In simulations for horticultural crops in southeast Spain, soil solution concentration factors at water saturation, quotients of electrical conductivity (EC₂₅) at saturation to electrical conductivity in the irrigation water, and quotients of sodium adsorption ratio (SAR) are very similar to average measured values for the area.     

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

In 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 (EC_p – 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 (EC_e – dS/m). Our interpretation based on estimates of EC_e 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.     

Validation of the ANOCOVA model for regional‐scale ECa to ECe calibration

Two approaches have emerged as the preferred means for assessing salinity at regional scale: (i) vegetative indices from satellite imagery (e.g., MODIS enhanced vegetative index, NDVI) and (ii) analysis of covariance (ANOCOVA) calibration of apparent soil electrical conductivity (EC_a) to salinity. The later approach is most recent and least extensively validated. It is the objective of this study to provide extensive validation of the ANOCOVA approach. The validation comprised 77 fields in California's Coachella Valley, ranging from 1.25 to 30.0 ha in size with an average size of 12.8 ha. Mobile electromagnetic induction (EMI) equipment surveyed the fields obtaining geospatial measurements of EC_a. Soil sample sites selected following EC_a‐directed soil sampling protocols characterized the range and spatial variation in EC_a across the field. From the data, a regional ANOCOVA model was developed. The regional ANOCOVA model successfully reduced cross‐validated, average log salinity prediction error (variance) estimate by more than 30% across the 77 fields and improved the depth‐averaged prediction accuracy in 58 of the 77 fields. The results show that the ANOCOVA modelling approach improves soil salinity predictions from EMI signal data in most of the surveys conducted, particularly fields where only a limited number of calibration sampling locations were available. The establishment of ANOCOVA models at each depth increment for a representative set of fields within a regional‐scale study area provides slope coefficients applicable to all future fields within the region, significantly reducing ground‐truth soil samples at future fields.     

Soil salinity and its distribution determined by soil sampling and electromagnetic techniques

Abstract. Diagnosis of soil salinity and its spatial variability is required to establish control measures in irrigated agriculture. This article shows the usefulness of electromagnetic (EM) and soil sampling techniques to map salinity. We analysed the salinity of a 1‐ha plot of surface‐irrigated olive plantation in Aragon, NE Spain, by measuring the electrical conductivity of the saturation extract (EC_e) of soil samples taken at 22 points, and by reading the Geonics EM38 sensor at 141 points in the horizontal (EM_H) and vertical (EM_V) dipole positions. EM_H and EM_V values had asymmetrical bimodal distributions, with most readings in the non‐saline range and a sharp transition to relatively high readings. Most salinity profiles were uniform (i.e. EM_H=EM_V), except in areas with high salinity and concurrent shallow water tables, where the profiles were inverted as shown by EM_H > EM_V, and by EC_e being greater in shallow than in deeper layers. The regressions of EC_e on EM readings predicted EC_e with R² > 84% for the 0–100 to 0–150 cm soil depths. We then produced salinity contour maps from the 141 EC_e values estimated from the electromagnetic readings and the 22 measured values of EC_e. Owing to the high soil sampling density, the maps were similar (i.e. mean surface‐weighted EC_e values between 3.9 dS m^?1 and 4.2 dS m^?1), although the electromagnetically estimated EC_e improved the mapping of details. Whereas soil sampling is preferred for analysing the vertical distribution of soil salinity, the electromagnetic sensor is ideal for mapping the lateral variability of soil salinity.     

Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity

The site‐specific cultivation as part of the precision‐agriculture concept is more and more introduced into practical farming. However, soil information is often not available in a spatial resolution intrinsically needed for precision farming or other site‐specific soil use and management purposes. One approach to obtain spatially high‐resolution soil data is the non‐invasive measurement of the apparent electrical conductivity (EC_a). In this study, we recorded the EC_a on three fields with an EM38 (Geonics, Canada). The EC_a data were compared with (1) ground truth data obtained by conventional drilling, (2) traditional soil maps (large scale, ≤1:5,000), (3) the growth and yield of corn. The temporal variability of the EC_a due to varying soil moisture and temperature was taken into account by repeated measurements of the same fields and subsequent averaging of the EC_a values. Significant correlations (r² = 0.76) were found between the mean weighted clay content (0–1.5 m) and the EC_a. Furthermore, in soils with differently textured layers, EC_a was used to estimate the thickness of the uppermost loess layer. A comparison of EC_a and large‐scale soil maps reveals some pros and cons of EC_a measurements. The main advantages of EC_a recordings are the high spatial resolution in combination with low efforts. Yet, the EC_a signal is no direct measure for a soil type or unit. Depending on the variability of substrates and layering, the EC_a pattern can be a precise indicator for the spatial distribution of different soils. A strong conformity of the spatial variability of plant growth (derived from orthophotos and yield maps) and EC_a patterns within a field indicates that the EC_a signal per se—without conversion to traditional soil parameters—integrates the effects of various soil variables that govern soil fertility. Altogether, EC_a surveys can be a powerful tool to facilitate and improve conventional soil mapping.