Calibrating electromagnetic induction conductivities  with time-domain reflectometry measurements

Dragonetti, Giovanna; Comegna, Alessandro; Ajeel, Ali; Deidda, Gian Piero; Lamaddalena, Nicola; Rodriguez, Giuseppe; Vignoli, Giulio; Coppola, Antonio

doi:10.5194/hess-22-1509-2018

Cited by 29 publications

(25 citation statements)

References 65 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ECa depends on the soil profile distribution of water electrical conductivity, σ w , volumetric water content, θ, tortuosity, τ, of the soil pore system, as well as on other factors related to the solid phase such as bulk density, clay content and mineralogy. Consequently, separating the effect of single soil properties (e.g., soil water) on ECa is no simple task (see for example, [17,18]). However, in two separate studies, [19,20] found spatial variation in soil water stored within the top 0.5 and 1.7 m to be highly correlated with the spatial variation in ECa measured with EMI sensors.…”

Section: Evaluating Model Simulations By Direct Water Content Measurementioning

confidence: 99%

Identifying Optimal Irrigation Water Needs at District Scale by Using a Physically Based Agro-Hydrological Model

Coppola

Dragonetti

Sengouga

et al. 2019

Water

Self Cite

View full text Add to dashboard Cite

: This paper mainly aims to illustrate an irrigation management tool to simulate scheduling of district-level water needs over the course of an irrigation season. The tool is mostly based on a daily model for simulating flow of water (and solutes) in heterogeneous agri-environmental systems (called FLOWS-HAGES). The model produces information on the daily evolution of: soil water contents and pressure potentials in the soil profile; water uptake and actual evapotranspiration; stress periods for each crop; return fluxes to the groundwater and their quality in terms of solute concentrations (e.g., nitrates). FLOWS-HAGES provides a daily list of hydrants to be operated according to water or crop-based criteria. The daily optimal sequence of hydrant use may thus be established by passing the volumes to be delivered on to the model for simulating the hydraulics of the irrigation network, in order to ensure that the discharges flowing inside the network of distribution pipes are delivered under optimal pressure head distribution in the system. All the above evaluations can be carried out in a stochastic framework to account for soil heterogeneity and climate changes. To illustrate the potential of FLOWS-HAGES, a case study was considered for a selected sector of the Irrigation District 10 in the “Sinistra Ofanto” irrigation system (southern Italy, Apulia region). In a 139 ha area (Sector 6 of the Irrigation District), soil profiles were analyzed for characterization of hydraulic properties variability. Hydraulic properties were determined by a combination of field and laboratory measurements. Model simulations were validated by comparing soil water storage simulated and measured by a sensor based on electromagnetic induction technique. Irrigation water volumes and frequency calculated by the model were compared to the volumes actually supplied by the farmers. Compared to the farmers behavior, the model simulates more frequent irrigations with lower irrigation volumes. Finally, some indexes of irrigation performance were calculated for each farm under study. The resulting maps provide useful information on the spatial distribution of farmer behavior, indicating the abuse or underuse of water as well as the fraction of the water lost by drainage following the irrigation method applied.

show abstract

Section: Evaluating Model Simulations By Direct Water Content Measurementioning

confidence: 99%

Identifying Optimal Irrigation Water Needs at District Scale by Using a Physically Based Agro-Hydrological Model

Coppola

Dragonetti

Sengouga

et al. 2019

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…All these models are purely empirical and usually calibrated by means of simple linear regression (e.g., McKenzie et al, 1989), multiple linear regression (e.g., Díaz & Herrero, 1992) or geostatistical techniques (e.g., García‐Tomillo et al, 2017). There are also consolidated mathematical techniques for the calculation of soil σ b values from EMI measurements (Zhdanov, 2018), which have been compared to TDR‐measured σ b values (Dragonetti et al, 2018). In this work, however, a semi‐empirical model was developed to predict, not the basic properties, but the EMI measurements themselves, specifically, the EM38 measurements at the two dipole orientations and various heights over the ground on the basis of the main five soil properties, besides temperature, on which soil conductivity depends at various depths.…”

Section: Discussionmentioning

confidence: 99%

A semi‐empirical model to predict the EM38 electromagnetic induction measurements of soils from basic ground properties

Visconti

Paz

2020

European J Soil Science

View full text Add to dashboard Cite

Electromagnetic induction (EMI) measurements (σb*) are widely used for the survey of several soil attributes, among which basic properties such as salinity (σe), water content (θw), clay (wc), organic matter (wom) and bulk density (ρb) stand out. In usual practice, purely empirical models relating one of these properties to σb* are calibrated at selected sites. However, this calibration is site and time specific and has to be repeated time and again. In order to understand where the variability of the EMI empirical models comes from, it is necessary to know how the different soil properties contribute to them and, with this aim, a more physically based relationship between σb* and, at least, σe, θw, wc, wom and ρb was developed in this work, additionally including soil temperature (t). It was calibrated and cross‐validated with the data from one survey carried out in a wide agricultural irrigation area in SE Spain, taking σb* measurements with the Geonics EM38 in the horizontal and vertical dipole modes and at various heights over the ground. Then, it was externally validated with the data from a second survey carried out 4 years later in the same area but in a different season. In the calibration, R2 and root mean square error (RMSE) were 0.84 and 0.18 dS m−1 (41%), respectively, for the vertical dipole orientation and 0.90 and 0.11 dS m−1 (39%) for the horizontal one. In the external validation, R2 and RMSE were 0.80 and 0.24 dS m−1 (44%), respectively, for the vertical dipole orientation and 0.90 and 0.13 dS m−1 (38%) for the horizontal one. Therefore, because the performance of the model barely worsened as time passed by, it can be considered to represent the underlying physical process and, therefore, to increase our understanding of how the soil EMI signals are generated, with potential benefits for the planning and comparability of EMI soil measurements, specifically with the EM38, among different areas.Highlights A semi‐empirical model was developed to predict soil EMI measurements from basic ground properties. Salinity, water content, clay, organic matter, bulk density and temperature were used as predictors. The model was able to explain between 80 and 90% of the variance in EMI measurements in the validation. This model helps us understand how the basic soil properties contribute to the EMI measurements.

show abstract

“…The calibrations have been developed mostly under laboratory conditions using sandy soils [22,33,34]. Besides, some comparisons between different techniques for σ b measurement have been carried out, e.g., ER vs. TDR [22], TDR vs. FDR [35], ER vs. FDR [21], and EMI vs. TDR [18,36]. However, a comparison between more than two techniques, such as ER, EMI, and FDR, and for the appraisal of soil salinity in a large agricultural irrigated area of diverse soil texture has not been done up to date to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Field Comparison of Electrical Resistance, Electromagnetic Induction, and Frequency Domain Reflectometry for Soil Salinity Appraisal

Visconti

Paz

2020

Soil Systems

View full text Add to dashboard Cite

By using different physical foundations and technologies, many probes have been developed for on-site soil salinity appraisal in the last forty years. In order to better understand their respective technical and practical advantages and constraints, comparisons among probes are needed. In this study, three different probes, based on electrical resistance (ER), electromagnetic induction (EMI), and frequency domain reflectometry (FDR), were compared during a field survey carried out in a large salt-threatened agricultural area. Information about the soil bulk electrical conductivity (σb) at different depths was obtained with each of the probes and, additionally, other soil properties were also measured depending on the specifications of each instrument and, moreover, determined in samples. On average, the EMI and FDR techniques could be regarded as equivalent for σb measurement, whereas ER gave higher σb values. Whatever the case, EMI, and also ER, had to be supplemented with information about soil clay, organic matter, and water mass fractions to attain, despite this effort, poor soil salinity estimations by means of multiple linear regression models (R2 < 0.5). On the contrary, FDR needed only probe data to achieve R2 of 0.7, though root mean standard error (RMSE) was still 1.5 dS m−1. The extra measurements and calculations that modern electrical conductivity contact probes integrate, specifically, those based on FDR, remarkably increase their ability for soil salinity appraisal, although there is still room for improvement.

show abstract

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

Cited by 29 publications

References 65 publications

Identifying Optimal Irrigation Water Needs at District Scale by Using a Physically Based Agro-Hydrological Model

Identifying Optimal Irrigation Water Needs at District Scale by Using a Physically Based Agro-Hydrological Model

A semi‐empirical model to predict the EM38 electromagnetic induction measurements of soils from basic ground properties

Field Comparison of Electrical Resistance, Electromagnetic Induction, and Frequency Domain Reflectometry for Soil Salinity Appraisal

Contact Info

Product

Resources

About