the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Oct 2024
Quantitative soil characterization using frequency domain electromagnetic induction method in heterogeneous fields
Abstract. 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.
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Status: open (until 05 Dec 2024)
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RC1: 'Comment on egusphere-2024-2693', Jacopo Boaga, 24 Oct 2024
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The paper deals with the use of FDEM data for the quantitative characterisation of the first subsoil. The topic is of interest to the journal and the case study presented is a relevant example of an advance in the use of the EMI technique. Results are supported by the data presented and I encourage the publication. I suggest some minor revisions to be addressed before:
- Ln 72 a ratio of the same quantity is by definition a-dimensional, so please remove ppm
- some part of the paper cite ppm, other ppt (fig2), please homogenise it
- All the equations labels have layout issue that do not agree with the journal editing rules
- Eq1 QP means the ratio of the imaginary part of primary and secondary fields ? Please specify
- Ln 202-205 please provide references for the different polynomial approaches
- Ln 232 please provide the used open-source software reference
-Fig.5 To help reading I suggest to invert ideal and FDEM inverted EC columns, so reader can appreciate similarity between the former and the probe
- Ln 304 missing dot before 'However' ?
- Ln 300-308 all this paragraph is not clear and should be re-written
Citation: https://doi.org/10.5194/egusphere-2024-2693-RC1 -
AC1: 'Reply on RC1', Gaston Matias Mendoza Veirana, 15 Nov 2024
reply
RC1:
The paper deals with the use of FDEM data for the quantitative characterisation of the first subsoil. The topic is of interest to the journal and the case study presented is a relevant example of an advance in the use of the EMI technique. Results are supported by the data presented and I encourage the publication. I suggest some minor revisions to be addressed before:
Comments:
RC1.1
- Ln 72 a ratio of the same quantity is by definition a-dimensional, so please remove ppm
Response: thanks for your suggestion. Despite indeed a ratio of the same quantity is a-dimensional, ‘ppm’ refers to parts per million (1*10-6) and does not refer to a unit.
RC1.2
- some part of the paper cite ppm, other ppt (fig2), please homogenise it
Response: Right, this was corrected to ppm.
RC1.3
- All the equations labels have layout issue that do not agree with the journal editing rules
Response: all the equations were adapted to the journal’s format
RC1.4
- Eq1 QP means the ratio of the imaginary part of primary and secondary fields ? Please specify
Response: this is now defined in the previous sentences. QP represents the imaginary component of the ratio between secondary and primary field.
RC1.5
- Ln 202-205 please provide references for the different polynomial approaches
Response: we reformulated this paragraph, now it reads:
‘Three distinct approaches to polynomial development were utilized. A first approach, named “Layers Together” (ST-LT) consisted of combining data from different soil depths, so that no differentiation was made between top- and subsoil samples for model development. Secondly, these sample sets were considered separately in an approach whereby different polynomials were developed for each soil layer (“Layers Separate”(ST-LS)). In this modelling approach, the same polynomial degree was maintained for both top – and subsoil data. Finally, the ST-LS2 approach was like ST-LS but permitted different polynomial degrees for the models of each layer.’
RC1.6
- Ln 232 please provide the used open-source software reference
Response: The reference was added.
Mendoza Veirana, G., & Philippe De Smedt. (2024c). orbit-ugent/Pedophysics: First release 0.1 (Version 0.1) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.13465700RC1.7
-Fig.5 To help reading I suggest to invert ideal and FDEM inverted EC columns, so reader can appreciate similarity between the former and the probe
Response: Fig 5 is updated
RC1.8
- Ln 304 missing dot before 'However' ?
Response: changed as suggested
RC1.9
- Ln 300-308 all this paragraph is not clear and should be re-written
Response: changed as suggested:
‘When the best performing models for 𝜃 prediction are implemented using the entire dataset (Figure 7) – using both training and test data – this outperforms the modelling presented in Figure 6, where only test data are incorporated in error assessment. While this is a common approach, we want to highlight this is improper practice to critically evaluate model performance as the inclusion of training data in error estimation results in an overestimation of model performance (Altdorff et al., 2017; Lipinski et al., 2008; Tibshirani et al., 2001). In other words, implemented model errors should not be confused with actual expected accuracy of target property predictions.
To evaluate the influence of other soil properties in 𝜃 prediction, the residuals of the implemented stochastic models were correlated with other soil properties, but these were not significant.’
Citation: https://doi.org/10.5194/egusphere-2024-2693-AC1
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AC1: 'Reply on RC1', Gaston Matias Mendoza Veirana, 15 Nov 2024
reply
Data sets
Data set. Quantitative soil characterization using frequency domain electromagnetic induction method in heterogeneous fields Gaston Matias Mendoza Veirana et al. https://doi.org/10.5281/zenodo.13465721
Model code and software
FDEM_quantitative_soil Public Gaston Matias Mendoza Veirana et al. https://doi.org/10.5281/ZENODO.13385389
Interactive computing environment
FDEM_quantitative_soil Public Gaston Matias Mendoza Veirana et al. https://doi.org/10.5281/ZENODO.13385389
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