Data calibration; Data inversion; Deterministic approach; Deterministic modeling; Electromagnetics; Empirical approach; Empirical model; Field surveys; Frequency domains; Induction method; Water Science and Technology; Earth and Planetary Sciences (miscellaneous)
Abstract :
[en] The frequency domain electromagnetic induction (FDEM) method is a widely used tool for geophysical soil exploration. Field surveys using FDEM provide apparent electrical conductivity (ECa), which is typically used for qualitative interpretations. Quantitative estimations of soil properties remain challenging, especially in heterogeneous fields. Quantitative approaches are based on either deterministic or empirical modeling. While the deterministic approach faces limitations related to instrumental drift, data calibration, inversion, and pedophysical modeling, the empirical approach requires developing a local model, which involves extensive field sampling. This study aims to evaluate the effectiveness of FDEM modeling based on either a deterministic or empirical approach, identify its limitations, and search for optimal field protocols. We provide practical guidelines for end users to quantitatively predict soil water content, bulk density, clay content, cation exchange capacity, and water EC in heterogeneous fields. Two field surveys were conducted in Belgium, where FDEM data were collected using Dualem-421S and Dualem-21HS sensors, along with data taken from electrical resistivity tomography (ERT) measurements and an impedance moisture probe and soil sampling. A comprehensive sensitivity analysis revealed that deterministic modeling procedures could not predict water content more accurately than a mean value approximation (negative R2). This analysis also highlighted the sensitivity of the minimization method used in FDEM data inversion and the applied pedophysical model. Empirical modeling, which does not require FDEM data calibration or inversion, outperformed the deterministic approach. However, its prediction accuracy is limited, particularly if soil sample depth is not considered.
Disciplines :
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others
Author, co-author :
Mendoza, Gaston ; Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
Blanchy, Guillaume ; Université de Liège - ULiège > Département ArGEnCo > Géophysique appliquée ; F.R.S-FNRS (Fonds de la Recherche Scientifique), Brussels, Belgium
De Vijver, Ellen Van ; Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
Verhegge, Jeroen ; Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium ; Department of Archaeology, Ghent University, Ghent, Belgium
Cornelis, Wim; Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
De Smedt, Philippe; Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium ; Department of Archaeology, Ghent University, Ghent, Belgium
Language :
English
Title :
Quantitative soil characterization using the frequency domain electromagnetic induction method in heterogeneous fields
Altdorff, D., von Hebel, C., Borchard, N., van der Kruk, J., Bogena, H. R., Vereecken, H., and Huisman, J. A.: Potential of catchment-wide soil water content prediction using electromagnetic induction in a forest ecosystem, Environ. Earth Sci., 76, 111, https://doi.org/10.1007/s12665-016-6361-3, 2017.
Bárdossy, A. and Singh, S. K.: Robust estimation of hydrological model parameters, Hydrol. Earth Syst. Sci., 12, 1273–1283, https://doi.org/10.5194/hess-12-1273-2008, 2008.
Binley, A.: Tools and Techniques: Electrical Methods, in: Treatise on Geophysics, 233–259, Elsevier, https://doi.org/10.1016/B978-0-444-53802-4.00192-5, 2015.
Binley, A. and Kemna, A.: DC Resistivity and Induced Polarization Methods, edited by: Rubin, Y. and Hubbard, S. S., Hydrogeop., 50, 129–156, Springer Netherlands, https://doi.org/10.1007/1-4020-3102-5_5, 2005
Blanchy, G., Saneiyan, S., Boyd, J., McLachlan, P., and Binley, A.: ResIPy, an intuitive open source software for complex geoelectrical inversion/modeling, Comput. Geosci., 137, 104423, https://doi.org/10.1016/j.cageo.2020.104423, 2020.
Boaga, J.: The use of FDEM in hydrogeophysics: A review, J. Appl. Geophys., 139, 36–46, https://doi.org/10.1016/j.jappgeo.2017.02.011, 2017
Brus, D. J.: Sampling for digital soil mapping: A tutorial supported by R scripts, Geoderma, 338, 464–480, https://doi.org/10.1016/j.geoderma.2018.07.036, 2019.
Callegary, J. B., Ferre, T. P. A., and Groom, R. W.: Vertical spatial sensitivity and exploration depth of low-induction-number 35 electromagnetic-induction instruments, Vadose Zone J., 6, 158–167, https://doi.org/10.2136/vzj2006.0120, 2007.
Cavka, D., Mora, N., and Rachidi, F.: A Comparison of Frequency-Dependent Soil Models: Application to the Analysis of Grounding Systems, IEEE T. Electromagn. C., 56, 177–187, https://doi.org/10.1109/TEMC.2013.2271913, 2014.
Ciesielski, H., Sterckeman, T., Santerne, M., and Willery, J. P.: A comparison between three methods for the determination of cation exchange capacity and exchangeable cations in soils, Agronomie, 17, 9–16, https://doi.org/10.1051/agro:19970102, 1997a.
Ciesielski, H., Sterckeman, T., Santerne, M., and Willery, J. P.: Determination of cation exchange capacity and exchangeable cations in soils by means of cobalt hexamine trichloride. Effects of experimental conditions, Agronomie, 17, 1–7, https://doi.org/10.1051/agro:19970101, 1997b.
Cong-Thi, D., Dieu, L. P., Caterina, D., De Pauw, X., Thi, H. D., Ho, H. H., Nguyen, F., and Hermans, T.: Quantifying salinity in heterogeneous coastal aquifers through ERT and IP: Insights from laboratory and field investigations, J. Contam. Hydrol., 262, 104322, https://doi.org/10.1016/j.jconhyd.2024.104322, 2024.
Corwin, D. L. and Lesch, S. M.: Application of soil electrical conductivity to precision agriculture: Theory, principles, and guidelines, Agron J., 95, 455–471, https://doi.org/10.2134/agronj2003.4550, 2003.
Dimech, A., Isabelle, A., Sylvain, K., Liu, C., Cheng, L., Bussière, B., Chouteau, M., Fabien-Ouellet, G., Bérubé, C., Wilkinson, P., Meldrum, P., and Chambers, J.: A multiscale accuracy assessment of moisture content predictions using time-lapse electrical resistivity tomography in mine tailings, Sci. Rep.-UK, 13, 20922, https://doi.org/10.1038/s41598-023-48100-w, 2023.
Doolittle, J. A. and Brevik, E. C.: The use of electromagnetic induction techniques in soils studies, Geoderma, 223–225, 33–45, https://doi.org/10.1016/j.geoderma.2014.01.027, 2014.
Friedman, S. P.: Soil properties influencing apparent electrical conductivity: A review, Comput. Electron. Agr., 46, 45–70, https://doi.org/10.1016/j.compag.2004.11.001, 2005.
Fu, Y., Horton, R., Ren, T., and Heitman, J. L.: A general form of Archie’s model for estimating bulk soil electrical conductivity, J. Hydrol., 597, 126160, https://doi.org/10.1016/j.jhydrol.2021.126160, 2021.
Glover, P. W. J.: Geophysical Properties of the Near Surface Earth: Electrical Properties, Treatise on Geophysics, 89–137, Elsevier, https://doi.org/10.1016/B978-0-444-53802-4.00189-5, 2015.
Hanssens, D., Delefortrie, S., Bobe, C., Hermans, T., and De Smedt, P.: Improving the reliability of soil EC-mapping: Robust apparent electrical conductivity (rECa) estimation in ground-based frequency domain electromagnetics, Geoderma, 337, 1155–1163, https://doi.org/10.1016/j.geoderma.2018.11.030, 2019.
Hanssens, D., Waegeman, W., Declercq, Y., Dierckx, H., Verschelde, H., and Smedt, P. D.: High-resolution surveying with small-loop requency-domain electromagnetic systems: Efficient survey design and adaptive processing, IEEE Geosci. Remote. S., 9, 167–183, https://doi.org/10.1109/MGRS.2020.2997911, 2020.
Huang, H., SanFilipo, B., Oren, A., and Won, I. J.: Coaxial coil towed EMI sensor array for UXO detection and characterization, J. Appl. Geophys., 61, 217–226, https://doi.org/10.1016/j.jappgeo.2006.06.005, 2007.
Jougnot, D., Jiménez-Martínez, J., Legendre, R., Le Borgne, T., Méheust, Y., and Linde, N.: Impact of small-scale saline tracer heterogeneity on electrical resistivity monitoring in fully and partially saturated porous media: Insights from geoelectrical milli-fluidic experiments, Adv. Water Resour., 113, 295–309, https://doi.org/10.1016/j.advwatres.2018.01.014, 2018.
Lavoué, F., van der Kruk, J., Rings, J., André, F., Moghadas, D., Huisman, J. A., Lambot, S., Weihermuller, L., Vanderborght, J., and Vereecken, H.: Electromagnetic induction calibration using apparent electrical conductivity modelling based on electrical resistivity tomography, Near Surf. Geophys., 8, 553–561, https://doi.org/10.3997/1873-0604.2010037 2010.
Lipinski, B. A., Sams, J. I., Smith, B. D., and Harbert, W.: Using HEM surveys to evaluate disposal of by-product water from CBNG development in the Powder River Basin, Wyoming, Geophysics, 73, B77–B84, https://doi.org/10.1190/1.2901200, 2008.
Logsdon, S. D., Green, T. R., Seyfried, M., Evett, S. R., and Bonta, J.: Hydra Probe and Twelve-Wire Probe Comparisons in Fluids and Soil Cores, Soil Sci. Soc. Am. J., 74, 5–12, https://doi.org/10.2136/sssaj2009.0189, 2010.
Longmire, C. and Smith, K.: A Universal Impedance for Soils, Mission Research Corporation, Report DNA 3788T, 1975.
Lück, E., Guillemoteau, J., Tronicke, J., Rummel, U., and Hierold, W.: From point to field scale – Indirect monitoring of soil moisture variations at the DWD test site in Falkenberg, Geoderma, 427, 116134, https://doi.org/10.1016/j.geoderma.2022.116134, 2022.
McLachlan, P., Blanchy, G., and Binley, A.: EMagPy: Open-source standalone software for processing, forward modeling and inversion of electromagnetic induction data, Comput. Geosci., 146, 104561, https://doi.org/10.1016/j.cageo.2020.104561, 2021.
McNeill, J. D.: Electromagnetic terrain conductivity measurement at low induction numbers, Technical Note 6, p. 13, Geonics Limited, 1980.
Mendoza Veirana, G. M.: Data set. Quantitative soil characterization using frequency domain electromagnetic induction method in heterogeneous fields, Zenodo [data set], https://doi.org/10.5281/zenodo.13465721, 2024a.
Mendoza Veirana, G. M.: orbit-ugent/FDEM_quantitative_soil:
Mendoza Veirana, G. M. and De Smedt, P.: orbit-ugent/Pedophysics: First release 0.1 (Version 0.1), Zenodo [code], https://doi.org/10.5281/ZENODO.13465700, 2024.
Minasny, B. and McBratney, A. B.: A conditioned Latin hypercube method for sampling in the presence of ancillary information, Comput. Geosci., 32, 1378–1388, 2006.
Minsley, B. J., Smith, B. D., Hammack, R., Sams, J. I., and Veloski, G.: Calibration and filtering strategies for frequency domain electromagnetic data, J. Appl. Geophys., 80, 56–66, https://doi.org/10.1016/j.jappgeo.2012.01.008, 2012.
Moghadas, D. and Badorreck, A.: Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity, Near Surf. Geophys., 17, 181–195, https://doi.org/10.1002/nsg.12036, 2019.
Pannell, D.: Sensitivity analysis of normative economic models: Theoretical framework and practical strategies, Agr. Econ., 16, 139–152, https://doi.org/10.1016/S0169-5150(96)01217-0, 1997.
Rentschler, T., Werban, U., Ahner, M., Behrens, T., Gries, P., Scholten, T., Teuber, S., and Schmidt, K.: 3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning, Vadose Zone J., 19, e20062, https://doi.org/10.1002/vzj2.20062, 2020.
Rhoades, J. D., Raats, P. A. C., and Prather, R. J.: Effects of Liquid-phase Electrical Conductivity, Water Content, and Surface Conductivity on Bulk Soil Electrical Conductivity, Soil Sci. Soc. Am. J., 40, 651–655, https://doi.org/10.2136/sssaj1976.03615995004000050017x, 1976.
Romero-Ruiz, A., Linde, N., Keller, T., and Or, D.: A Review of Geophysical Methods for Soil Structure Characterization: Geophysics and soil structure, Rev. Geophys., 56, 672–697, https://doi.org/10.1029/2018RG000611, 2018.
Saltelli, A. and Annoni, P.: How to avoid a perfunctory sensitivity analysis, Environ. Modell. Softw., 25, 1508–1517, https://doi.org/10.1016/j.envsoft.2010.04.012, 2010.
SENSE-UGent: FDEM_quantitative_soil, GitHub, https://github.com/SENSE-UGent/FDEM_quantitative_soil (last access: 3 July 2025), 2024.
Sheets, K. R. and Hendrickx, J. M. H.: Noninvasive Soil Water Content Measurement Using Electromagnetic Induction, Water Resour. Res., 31, 2401–2409, https://doi.org/10.1029/95wr01949, 1995.
Stevens Water Monitoring Systems.: The Hydra Probe® soil sensor: Comprehensive Stevens Hydra Probe user’s manual, instrument manual, Stevens Water Monitoring Systems, Beaverton, OR, 2008.
Terry, N., Day-Lewis, F. D., Lane Jr., J. W., Johnson, C. D., and Werkema, D.: Field evaluation of semi-automated moisture estimation from geophysics using machine learning, Vadose Zone J., 22, e20246, https://doi.org/10.1002/vzj2.20246, 2023.
Tibshirani, R., Friedman, J., and Hastie, T.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2001.
van der Kruk, J., von Hebel, C., Brogi, C., Sarah Kaufmann, M., Tan, X., Weihermüller, L., Huisman, J. A., Vereecken, H., and Mester, A.: Calibration, inversion, and applications of multiconfiguration electromagnetic induction for agricultural top- and subsoil characterization, SEG Technical Program Expanded Abstracts 2018, Soc. Expl. GEOP., 2546–2550, https://doi.org/10.1190/segam2018-2965257.1, 2018.
Van Ranst, E. and Sys, C.: Eenduidige legende voor de digitale bodemkaart vanVlaanderen (Schaal 1: 20000), Laboratorium voor Bodemkunde Krijgslaan 281/S8 9000 Gent, University of Ghent, https://ecopedia.s3.eu-central-1.amazonaws.com/pdfs/550.pdf (last access: 6 June 2025), 2000.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Mill-man, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C., Polat, I., Feng, Y., Moore, E., and VanderPlas, J.: SciPy 1.0: Fundamental algorithms for scientific computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020.
Wait, J. R.: Geo-Electromagnetism, Elsevier, https://doi.org/10.1016/B978-0-12-730880-7.X5001-7, 1982.
