Effect of agronomic treatments on the accuracy of soil moisture mapping by electromagnetic induction

Altdorff, Daniel; Galagedara, Lakshman; Nadeem, Muhammad; Cheema, Mumtaz; Unc, Adrian

doi:10.1016/j.catena.2017.12.036

Cited by 29 publications

(32 citation statements)

References 51 publications

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“…The silage-corn trial on the small field consisted of 4 m × 1 m plots that received different nutrient management treatments using biochar (BC), dairy manure (DM), inorganic fertilizer, or a combination of these. Soil samplings on the small field were done by selecting twenty sampling locations based on the BC and DM application, though the treatment effects were not significant across the small field [28]. Soil samples were collected from the depths of 0-10 cm and 10-20 cm using a gouge auger and a hammer.…”

Section: Soil Samplingmentioning

confidence: 99%

“…When soil salinity, texture, mineralogy and temperature are constant, EC a is a direct function of SMC [23,24]. Under such conditions, several authors have established that there is a linear relationship between SMC and EC a [25][26][27][28]. However, other researchers have established relationships between EC a and other soil properties such as soil salinity [29,30], saturated percentage [7,31] and soil bulk density [32,33].…”

Section: Introductionmentioning

confidence: 99%

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Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

et al. 2018

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Precision agriculture (PA) involves the management of agricultural fields including spatial information of soil properties derived from apparent electrical conductivity (ECa) measurements. While this approach is gaining much attention in agricultural management, farmed podzolic soils are under-represented in the relevant literature. This study: (i) established the relationship between ECa and soil moisture content (SMC) measured using time domain reflectometry (TDR); and (ii) evaluated the estimated SMC with ECa measurements obtained with two electromagnetic induction (EMI) sensors, i.e., multi-coil and multi-frequency, using TDR measured SMC. Measurements were taken on several plots at Pynn’s Brook Research Station, Pasadena, Newfoundland, Canada. The means of ECa measurements were calculated for the same sampling location in each plot. The linear regression models generated for SMC using the CMD-MINIEXPLORER were statistically significant with the highest R2 of 0.79 and the lowest RMSE (root mean square error) of 0.015 m3 m−3 but were not significant for GEM-2 with the lowest R2 of 0.17 and RMSE of 0.045 m3 m−3; this was due to the difference in the depth of investigation between the two EMI sensors. The validation of the SMC regression models for the two EMI sensors produced the highest R2 = 0.54 with the lowest RMSE prediction = 0.031 m3 m−3 given by CMD-MINIEXPLORER. The result demonstrated that the CMD-MINIEXPLORER based measurements better predicted shallow SMC, while deeper SMC was better predicted by GEM-2 measurements. In addition, the ECa measurements obtained through either multi-coil or multi-frequency sensors have the potential to be successfully employed for SMC mapping at the field scale.

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Section: Soil Samplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

et al. 2018

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“…Electromagnetic induction has shown considerable potential for characterization and monitoring of soil geophysical properties at field scale (Abdu et al, 2007; Triantafilis and Monteiro Santos, 2009, 2010, 2013; Jadoon et al, 2017; Altdorff et al, 2018; Rejiba et al, 2018; Robinet et al, 2018). Electromagnetic induction returns EC a (or σ a , mS m −1 ), which is a cumulative response of the subsurface electrical conductivities.…”

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confidence: 99%

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

Moghadas

2019

Vadose Zone Journal

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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.

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“…Electromagnetic induction (EMI) surveys are routinely used in agriculture to map bulk soil variability or changes in soil type, however in recent years they have been increasing used to map variability in soil moisture. EMI surveys are relatively quick to conduct, are non-invasive, have high spatial resolution, and require less specialized skill and knowledge to process and interpret data than other geophysical approaches such as GPR [119][120][121][122][123]. Unlike dielectric or microwave-based based approaches (TDR, FDR, GPR etc.)…”

Section: Electromagnetic Induction (Emi)mentioning

confidence: 99%

“…Much of the recent research effort to improve EMI-based mapping or measurement of soil properties including soil moisture content have focused on the use of (i) multicoil and multifrequency devices [125][126][127], (ii) calibration algorithms [128][129][130], or (iii) inversion approaches for 2D or quasi 3D projection of ECa [131][132][133]. ECa is a bulk response to the proximal environment, incorporating both inherent soil properties (soil minerology and texture), and variable properties (temperature, moisture content, salinity, and soil density) [120,124,134,135]. Consequently, the relationship between ECa and soil moisture content is complex, often co-related with other soil properties, and spatially and temporally unstable [119,125,136].…”

Section: Electromagnetic Induction (Emi)mentioning

confidence: 99%

Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture

Hardie

2020

Sensors

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The measurement of soil moisture in agriculture is currently dominated by a small number of sensors, the use of which is greatly limited by their small sampling volume, high cost, need for close soil–sensor contact, and poor performance in saline, vertic and stony soils. This review was undertaken to explore the plethora of novel and emerging soil moisture sensors, and evaluate their potential use in agriculture. The review found that improvements to existing techniques over the last two decades are limited, and largely restricted to frequency domain reflectometry approaches. However, a broad range of new, novel and emerging means of measuring soil moisture were identified including, actively heated fiber optics (AHFO), high capacity tensiometers, paired acoustic / radio / seismic transceiver approaches, microwave-based approaches, radio frequency identification (RFID), hydrogels and seismoelectric approaches. Excitement over this range of potential new technologies is however tempered by the observation that most of these technologies are at early stages of development, and that few of these techniques have been adequately evaluated in situ agricultural soils.

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Effect of agronomic treatments on the accuracy of soil moisture mapping by electromagnetic induction

Cited by 29 publications

References 51 publications

Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

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

Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture

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