Quantitative Two‐Layer Conductivity Inversion of Multi‐Configuration Electromagnetic Induction Measurements

Mester, Achim; Kruk, Jan van der; Zimmermann, Egon; Vereecken, Harry

doi:10.2136/vzj2011.0035

Cited by 71 publications

(75 citation statements)

References 37 publications

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“…EMI measures apparent electrical conductivity, σ a , by inductive coupling (e.g., Mester et al 2011), without the need for contact with the soil surface. EMI is a quick and repeatable method that can be employed at the field and plot scale .…”

Section: Electromagnetic Inductancementioning

confidence: 99%

“…When s is increased the depth of soil contributing to the σ a measurement increases (e.g., McNeill 1980;Callegary et al 2007). Given a set σ a measurements obtained with different coil spacing and orientations, a 1-D vertical profile of soil conductivity can be estimated by inverse modelling (Mester et al 2011;von Hebel et al 2014).…”

Section: Electromagnetic Inductancementioning

confidence: 99%

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Methods to estimate changes in soil water for phenotyping root activity in the field

et al. 2017

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Background and aims There is an urgent need to develop new high throughput approaches to phenotype roots in the field. Excavating roots to make direct measurements is labour intensive. An alternative to excavation is to measure soil drying profiles and to infer root activity. Methods We grew 23 lines of wheat in 2013, 2014 and 2015. In each year we estimated soil water profiles with electrical resistance tomography (ERT), electromagnetic inductance (EMI), penetrometer measurements and measurements of soil water content. We determined the relationships between the measured variable and soil water content and matric potential. Results We found that ERT and penetrometer measurements were closely related to soil matric potential and produced the best discrimination between wheat lines.We found genotypic differences in depth of water uptake in soil water profiles and in the extent of surface drying. Conclusions Penetrometer measurements can provide a reliable approach to comparing soil drying profiles by different wheat lines, and genotypic rankings are repeatable across years. EMI, which is more sensitive to soil water content than matric potential, and is less effective in drier soils than the penetrometer or ERT, nevertheless can be used to rapidly screen large populations for differences in root activity.

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Section: Electromagnetic Inductancementioning

confidence: 99%

Section: Electromagnetic Inductancementioning

confidence: 99%

Methods to estimate changes in soil water for phenotyping root activity in the field

et al. 2017

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“…The approach by Santos was lately developed further to a quasi-3D approach, but still under the LIN approximation [23]. Other studies have also aimed at deriving the layering of the subsurface while assuming the LIN-approximation [24][25][26]. However, even if the LIN approximation is easy to use and implement, there has been a consistent use of the direct measurement values (typically converted to apparent conductivity or resistivity) as the final product [11,27,28].…”

Section: Introductionmentioning

confidence: 99%

Improved Geoarchaeological Mapping with Electromagnetic Induction Instruments from Dedicated Processing and Inversion

et al. 2016

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Increasingly, electromagnetic induction methods (EMI) are being used within the area of archaeological prospecting for mapping soil structures or for studying paleo-landscapes. Recent hardware developments have made fast data acquisition, combined with precise positioning, possible, thus providing interesting possibilities for archaeological prospecting. However, it is commonly assumed that the instrument operates in what is referred to as Low Induction Number, or LIN. Here, we detail the problems of the approximations while discussing a best practice for EMI measurements, data processing, and inversion for understanding a paleo-landscape at an Iron Age human bone depositional site (Alken Enge) in Denmark. On synthetic as well as field data we show that soil mapping based on EMI instruments can be improved by applying data processing methodologies from adjacent scientific fields. Data from a 10 hectare study site was collected with a line spacing of 1-4 m, resulting in roughly 13,000 processed soundings, which were inverted with a full non-linear algorithm. The models had higher dynamic range in the retrieved resistivity values, as well as sharper contrasts between structural elements than we could obtain by looking at data alone. We show that the pre-excavation EMI mapping facilitated an archaeological prospecting where traditional trenching could be replaced by a few test pits at selected sites, hereby increasing the chance of finding human bones. In a general context we show that (1) dedicated processing of EMI data is necessary to remove coupling from anthropogenic structures (fences, phone cables, paved roads, etc.), and (2) that carrying out a dedicated full non-linear inversion with spatial coherency constraints improves the accuracy of resistivities and structures over using the data as they are or using the Low Induction Number (LIN) approximation.

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“…To date, this issue has been tackled by applying two different strategies: the first is to use empirical calibration relations relating the depth-integrated EC a readings to the σ b values measured by alternative methods -like Time-domain reflectometry (TDR) -within discrete depth intervals (Rhoades and Corwin, 1981;Lesch et al, 1992;Triantafilis et al, 2000;Amezketa, 2006;Yao and Jingsong, 2010;Coppola et al, 2016). The second consists of the 1-D inversion of the observations from the EMI sensor to reconstruct the vertical conductivity profile (Borchers et al, 1997;Hendrickx et al, 2002;Monteiro Santos et al, 2010;Lavoué et al, 2010;Mester et al, 2011;Minsley et al, 2012;Deidda et al, 2014;Von Hebel et al, 2014).…”

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confidence: 99%

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

Dragonetti

Comegna

Ajeel

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. This paper deals with the issue of monitoring the spatial distribution of bulk electrical conductivity, σ b , in the soil root zone by using electromagnetic induction (EMI) sensors under different water and salinity conditions. To deduce the actual distribution of depth-specific σ b from EMI apparent electrical conductivity (EC a ) measurements, we inverted the data by using a regularized 1-D inversion procedure designed to manage nonlinear multiple EMI-depth responses. The inversion technique is based on the coupling of the damped Gauss-Newton method with truncated generalized singular value decomposition (TGSVD). The illposedness of the EMI data inversion is addressed by using a sharp stabilizer term in the objective function. This specific stabilizer promotes the reconstruction of blocky targets, thereby contributing to enhance the spatial resolution of the EMI results in the presence of sharp boundaries (otherwise smeared out after the application of more standard Occamlike regularization strategies searching for smooth solutions). Time-domain reflectometry (TDR) data are used as groundtruth data for calibration of the inversion results. An experimental field was divided into four transects 30 m long and 2.8 m wide, cultivated with green bean, and irrigated with water at two different salinity levels and using two different irrigation volumes. Clearly, this induces different salinity and water contents within the soil profiles. For each transect, 26 regularly spaced monitoring soundings (1 m apart) were selected for the collection of (i) Geonics EM-38 and (ii) Tektronix reflectometer data. Despite the original discrepancies in the EMI and TDR data, we found a significant correlation of the means and standard deviations of the two data series; in particular, after a low-pass spatial filtering of the TDR data. Based on these findings, this paper introduces a novel methodology to calibrate EMI-based electrical conductivities via TDR direct measurements. This calibration strategy consists of a linear mapping of the original inversion results into a new conductivity spatial distribution with the coefficients of the transformation uniquely based on the statistics of the two original measurement datasets (EMI and TDR conductivities).

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Quantitative Two‐Layer Conductivity Inversion of Multi‐Configuration Electromagnetic Induction Measurements

Cited by 71 publications

References 37 publications

Methods to estimate changes in soil water for phenotyping root activity in the field

Methods to estimate changes in soil water for phenotyping root activity in the field

Improved Geoarchaeological Mapping with Electromagnetic Induction Instruments from Dedicated Processing and Inversion

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

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