Jeroen Verhegge

Acknowledgements: funded by the Ghent University Special Research fund and Scientific Research Fund (FWO; K208225N).

This study explores the relationship between soil magnetic susceptibility (κ) and cation exchange capacity (CEC) across diverse European soils, aiming to enhance pedotransfer functions (PTFs) for soil CEC using near-surface electromagnetic geophysics. We hypothesize that soil κ, can improve the prediction of CEC by reflecting the soil's mineralogical composition, particularly in sandy soils. We collected data from 49 soil samples in vertical profiles across Belgium, the Netherlands, and Serbia, including κ in situ conditions (κ_∗), low and high frequency κ in the laboratory, in-site electrical conductivity (σ), iron content, soil texture, humus content, bulk density, water content, water pH, and CEC. We used these properties as features to develop univariable and multivariable (in pairs) polynomial regressions to predict CEC for sandy and clayey soils. Results indicate that κ_∗ significantly improves CEC predictions in sandy soils, independent of clay content, with a combined κ_∗-σ model achieving the highest predictive performance (R² = 0.94). In contrast, laboratory-measured κ was less effective, likely due to sample disturbance. This study presents a novel CEC PTF based on σ and κ_∗, offering a rapid, cost-effective method for estimating CEC in field conditions. While our findings underscore the value of integrating geophysical measurements into soil characterization, further research is needed to refine the κ–CEC relationship and develop a more widely applicable model.

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.

This study explores the relationship between soil magnetic susceptibility (𝜅) and cation exchange capacity (𝐶𝐸𝐶) across diverse European soils, aiming to enhance pedotransfer functions (PTFs) for soil 𝐶𝐸𝐶 using near-surface electromagnetic geophysics. We hypothesize that soil 𝜅, can improve the prediction of 𝐶𝐸𝐶 by reflecting the soil’s mineralogical composition, particularly in sandy soils. We collected data from 49 soil samples in vertical profiles across Belgium, the Netherlands, and Serbia, including 𝜅 in field conditions (𝜅^∗), low and high frequency 𝜅 in the laboratory, in-site electrical conductivity (𝜎), iron content, soil texture, humus content, bulk density, water content, water pH, and 𝐶𝐸𝐶. We used these properties as features to develop univariable and multivariable (in pairs) polynomial regressions to predict 𝐶𝐸𝐶 for sandy and clayey soils. Results indicate that 𝜅^∗ significantly improves 𝐶𝐸𝐶 predictions in sandy soils, independent of clay content, with a combined 𝜅^∗ - 𝜎 model achieving the highest predictive performance (R² = 0.94). In contrast, laboratory-measured 𝜅 was less effective, likely due to sample disturbance. This study presents a novel 𝐶𝐸𝐶 PTF based on 𝜎 and 𝜅^∗, offering a rapid, cost-effective method for estimating 𝐶𝐸𝐶 in field conditions. While our findings underscore the value of integrating geophysical measurements into soil characterization, further research is needed to refine the 𝜅- 𝐶𝐸𝐶 relationship and develop a more widely applicable model.

This study explores the relationship between soil magnetic susceptibility (𝜅) and cation exchange capacity (𝐶𝐸𝐶) across diverse European soils, aiming to enhance pedotransfer functions (PTFs) for soil 𝐶𝐸𝐶 using near-surface electromagnetic geophysics. We hypothesize that soil 𝜅, can improve the prediction of 𝐶𝐸𝐶 by reflecting the soil’s mineralogical composition, particularly in sandy soils. We collected data from 49 soil samples in vertical profiles across Belgium, the Netherlands, and Serbia, including 𝜅 in field conditions (𝜅^∗), low and high frequency 𝜅 in the laboratory, in-site electrical conductivity (𝜎), iron content, soil texture, humus content, bulk density, water content, water pH, and 𝐶𝐸𝐶. We used these properties as features to develop univariable and multivariable (in pairs) polynomial regressions to predict 𝐶𝐸𝐶 for sandy and clayey soils. Results indicate that 𝜅^∗ significantly improves 𝐶𝐸𝐶 predictions in sandy soils, independent of clay content, with a combined 𝜅^∗ - 𝜎 model achieving the highest predictive performance (R² = 0.94). In contrast, laboratory-measured 𝜅 was less effective, likely due to sample disturbance. This study presents a novel 𝐶𝐸𝐶 PTF based on 𝜎 and 𝜅^∗, offering a rapid, cost-effective method for estimating 𝐶𝐸𝐶 in field conditions. While our findings underscore the value of integrating geophysical measurements into soil characterization, further research is needed to refine the 𝜅- 𝐶𝐸𝐶 relationship and develop a more widely applicable model.

This study explores the relationship between soil magnetic susceptibility (𝜅) and cation exchange capacity (𝐶𝐸𝐶) across diverse European soils, aiming to enhance pedotransfer functions (PTFs) for soil 𝐶𝐸𝐶 using near-surface electromagnetic geophysics. We hypothesize that soil 𝜅, can improve the prediction of 𝐶𝐸𝐶 by reflecting the soil’s mineralogical composition, particularly in sandy soils. We collected data from 49 soil samples in vertical profiles across Belgium, the Netherlands, and Serbia, including 𝜅 in field conditions (𝜅^∗), low and high frequency 𝜅 in the laboratory, in-site electrical conductivity (𝜎), iron content, soil texture, humus content, bulk density, water content, water pH, and 𝐶𝐸𝐶. We used these properties as features to develop univariable and multivariable (in pairs) polynomial regressions to predict 𝐶𝐸𝐶 for sandy and clayey soils. Results indicate that 𝜅^∗ significantly improves 𝐶𝐸𝐶 predictions in sandy soils, independent of clay content, with a combined 𝜅^∗ - 𝜎 model achieving the highest predictive performance (R² = 0.94). In contrast, laboratory-measured 𝜅 was less effective, likely due to sample disturbance. This study presents a novel 𝐶𝐸𝐶 PTF based on 𝜎 and 𝜅^∗, offering a rapid, cost-effective method for estimating 𝐶𝐸𝐶 in field conditions. While our findings underscore the value of integrating geophysical measurements into soil characterization, further research is needed to refine the 𝜅- 𝐶𝐸𝐶 relationship and develop a more widely applicable model.

We introduce Pedophysics, an open-source Python package designed to facilitate solutions for users who work in the field of soil assessment using near-surface geophysical electromagnetic techniques. Despite the increasing application of geophysical techniques for soil characterization, challenges remain in standardizing modeling approaches, integrating diverse data sources, and ensuring robust predictions across varying environmental conditions. At the core of this software is the ability to translate geophysical data into specific soil properties (and vice versa) using pedophysical models (PMs). Pedophysical modeling techniques offer valuable insights into various realms such as precision agriculture, soil health, resource prospecting, nutrient and land management, hydrogeology, and heritage conservation. Although the implemented PM has been validated for different environmental and measuring conditions, the core innovation relies on a novel data-processing workflow that allows obtaining solutions with minimal requirements. The capabilities of Pedophysics extend to handling complex scenarios such as fusing data from different instruments, incorporating continuous monitoring measurements, and soil calibration data. With these solutions, Pedophysics automates the process of deriving targeted soil and geophysical properties with state-of-the-art accuracy. Users can rely on Pedophysics to implement specific knowledge about pedophysical modeling. The software promotes global access to advanced soil geophysical solutions by being open source and encouraging community contributions.

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 either based on deterministic or stochastic modeling. While the deterministic approach faces limitations related to instrumental drift, data calibration, inversion, and pedophysical modeling, the stochastic approach requires developing a local model, which involves extensive field sampling. This study aims to evaluate the effectiveness of the FDEM modelling based on either a deterministic or stochastic 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 was 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. Stochastic 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.

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 either based on deterministic or stochastic modeling. While the deterministic approach faces limitations related to instrumental drift, data calibration, inversion, and pedophysical modeling, the stochastic approach requires developing a local model, which involves extensive field sampling. This study aims to evaluate the effectiveness of the FDEM modelling based on either a deterministic or stochastic 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 was 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. Stochastic 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.