# Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software

^{1}

^{2}

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## Abstract

**:**

_{a}—mS/m), can often be correlated directly with measured topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) clay and CEC. In this study, we explore the potential to use this approach and considering a linear regression (LR) between EM38 acquired EC

_{a}in horizontal (EC

_{ah}) and vertical (EC

_{av}) modes of operation and the soil properties at each of these depths. We compare this approach with a universal LR relationship developed between calculated true electrical conductivity (σ—mS/m) and laboratory measured clay and CEC at various depths. We estimate σ by inverting EC

_{ah}and EC

_{av}data, using a quasi-3D inversion algorithm (EM4Soil). The best LR between EC

_{a}and soil properties was between EC

_{ah}and subsoil clay (R

^{2}= 0.43) and subsoil CEC (R

^{2}= 0.56). We concluded these LR were unsatisfactory to predict clay or CEC at any of the three depths, however. In comparison, we found that a universal LR could be established between σ with clay (R

^{2}= 0.65) and CEC (R

^{2}= 0.68). The LR model validation was tested using a leave-one-out-cross-validation. The results indicated that the universal LR between σ and clay at any depth was precise (RMSE = 2.17), unbiased (ME = 0.27) with good concordance (Lin’s = 0.78). Similarly, satisfactory results were obtained by the LR between σ and CEC (Lin’s = 0.80). We conclude that in a field where a direct LR relationship between clay or CEC and EC

_{a}cannot be established, can still potentially be mapped by developing a LR between estimates of σ with clay or CEC if they all vary with depth.

## 1. Introduction

_{a}—mS/m). [15] were among the first to identify a linear regression (LR) between EM34 EC

_{a}and average (0–15 m) clay (R

^{2}= 0.73). [16] developed a LR between EM38 EC

_{a}and average (0–1.5 m) clay (R

^{2}= 0.77) to map clay across a cotton field (244 ha). [17] similarly found a good LR (R

^{2}= 0.76) and mapped clay across different fields. In their comprehensive review, [18] demonstrated many other LR of variable strength (R

^{2}= 0.01–0.94). In terms of CEC, [19] found a LR between topsoil (0–0.2 m) CEC and EC

_{a}, while [20] found a strong LR (R

^{2}= 0.74) between an EM38 and topsoil (0–0.3 m) CEC across various fields. [21] showed how a LR between an EM38 and average (0–0.2 m) CEC (R

^{2}= 0.81) could then be used to map CEC, while [12] established a separate LR to map different topsoil (0–0.075, 0.075–0.15 and 0.15–0.3 m) CEC across a field in Missouri, USA. Again, [18] provided another example of LR between EC

_{a}and CEC (0.50–0.76 m).

_{2}O

_{5}) and potassium (K

_{2}O) would be 113, 38 and 113 kg/ha, respectively. Alternatively, a compost fertiliser rate of 25 t/ha is suggested. This is similarly the case for CEC. In this research our interest is seeing if we can assist farmers with applying these guidelines by developing digital soil maps (DSM). The first aim is to see if we can develop a LR relationship between EM38 EC

_{a}directly with either topsoil (0–0.3 m), subsurface (0.3–0.6 m) or subsoil (0.6–0.9 m) clay and CEC. We compare this approach with a universal LR we develop between the calculated true electrical conductivity (σ—mS/m) and laboratory measured clay and CEC at various depths, because of recent success in mapping salinity [24] and moisture [25] by inverting EC

_{a}data. While a similar approach was used to map CEC in 3-dimensions by [26], they used a Veris-3100 instrument. Herein, we validate the universal LR using a leave-one-out-cross-validation, considering accuracy, bias and Lin’s concordance.

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Data Collection and Interpolation

_{a}data was an EM38 [30]. The instrument consists of a transmitter and receiver coil located at either end and spaced 1.0 m apart. The depth of exploration depends on coil configuration. In the horizontal mode, the EM38 measures EC

_{ah}and within a theoretical depth of 0–0.75 m. In the vertical mode, the EM38 measures EC

_{av}and within a theoretical depth of 0–1.5 m.

_{a}data in these two modes, 17 parallel transects were defined and spaced approximately 10 m apart in essentially an east–west orientation. Figure 1b shows the spatial distribution of these transects, which were of unequal length. The survey was conducted on 17 January 2018. In all, 467 measurement sites were visited to measure the EM38 EC

_{ah}and EC

_{av}. All EC

_{a}measurements were georeferenced using a Garmin Etrex Legend G [31] submeter GPS.

#### 2.3. Soil Sampling and Laboratory Analysis

_{a}or σ could be developed with topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) clay or CEC, 46 soil sampling locations were selected. The sampling points were selected according to two criteria, as suggested by [21]. Firstly, locations with small, intermediate and large EC

_{a}were selected; and secondly, samples were spaced evenly across the field. The samples were collected on the 25 January 2018. Figure 1c shows the location of the 46 sampling locations.

_{4}

^{+}using NaCl (Na

^{+}). Distillation apparatus and titrate method was used to determine CEC [33].

#### 2.4. Quasi-3D Inversion of EM38

_{ah}and EC

_{av}to calculate true electrical conductivity (σ—mS/m) and develop electromagnetic conductivity images (EMCI). The quasi-3D inversion algorithm was used in this study. In brief, quasi-3D is a 1-dimensional spatial constrained technique and a forward modelling approach. It assumes that below each EC

_{a}measurement location, an estimate of the 1-dimensional variation of σ is constrained because the EM4Soil software considers neighboring locations where the EM38 EC

_{a}was measured [35].

_{a}data was first gridded using a nearest neighbor technique onto a grid spacing of 10 × 10 m using the gridding tool available in EM4Soil. The initial model of σ was set equal to 10 mS/m with the maximum number of iterations equal to 10. A homogeneous five-layer initial model was also considered with depths to the top of each layer being 0, 0.3, 0.6, 0.9 and 1.05 m. The same depths would be used by the EM4Soil software to estimate true σ at these depths and for 3D prediction.

_{a}cumulative response and is used to convert depth profile conductivity to σ [37] considering the condition of low induction numbers. The FS model is based on the Maxwell equations [38] and is not limited to the small induction number condition. Therefore, the FS can improve models calculated from EC

_{a}data acquired over highly conductive soils (i.e., >100 mS/m). The damping factor (λ) was progressively increased with smaller increments initially and in large increments thereafter. The λ values used in this study were 0.07, 0.3, 0.6 and 0.9 to balance between rough and smooth EMCIs.

#### 2.5. Validation and Comparison with LR

_{a}(i.e., EC

_{ah}and EC

_{av}) data and clay and CEC in either the topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m). Secondly, we look to see if a satisfactory LR model can be developed between true electrical conductivity (σ—mS/m) as inverted from EM38 EC

_{ah}and EC

_{av}, and clay (%) and CEC (cmol(+)/kg) at all depths using a universal LR, which is applicable at any depth.

#### 2.6. Prediction Interval (PI)

## 3. Results and Discussion

#### 3.1. Preliminary EC_{ah} and EC_{av} Data Analysis

_{a}measured sites during the EM38 survey. The mean EC

_{ah}(0–0.75 m) was 23.1 mS/m with a minimum of 14 mS/m and maximum of 35 mS/m. The median (23) was close to the mean, with the EC

_{ah}slightly positively skewed (0.2) with a coefficient of variation (CV) of 19.4%. In comparison, the EC

_{av}(0–1.5 m) had a larger mean (28.8 mS/m) with a minimum of 18 mS/m and maximum of 45 mS/m. The median EC

_{av}was again slightly larger (29.0 mS/m) than the mean with the skewness positive again (0.3) and CV slightly smaller (20.5%).

_{a}data at the 46 calibration locations were relatively close to the surveyed data. The mean EC

_{ah}was 22.3 mS/m with a minimum of 15 mS/m and maximum of 33 mS/m. The median was close to the mean (22), with the EC

_{ah}slightly positively skewed (0.4) and with a coefficient of variation (CV) of 19.5%. In comparison, the EC

_{av}had a larger mean (27.5 mS/m) with a minimum of 19 mS/m and maximum of 42 mS/m. The median EC

_{av}was the same value (27.5 mS/m) to mean with the skewness positive again (0.67) and CV slightly smaller (19.5%).

_{ah}. The study field was characterized by intermediate-small (15–25 mS/m) EC

_{ah}in the northern half. Whereas, intermediate-small to intermediate EC

_{ah}(25–35 mS/m) defines the southern.

_{av}. Again, the study field was characterized by intermediate-small EC

_{av}in the northern half which started from the east through the north west corner and intermediate-large EC

_{av}(35–45 mS/m) in the west. From Figure 3b,c and Table 3, we surmise that the subsurface and subsoil were likely to be more conductive than the topsoil.

#### 3.2. Preliminary Clay and CEC Data Analysis

#### 3.3. Spatial Distribution of Clay and CEC Data

_{ah}and EC

_{av}from north to south as shown in Figure 3b,c, respectively.

#### 3.4. Linear Regression of Clay and CEC of Individual Depth Increment and ECa

_{a}. Figure 5a shows EC

_{ah}and topsoil (0–0.3 m) clay was small (R

^{2}= 0.21). Slightly better correlations were achieved between EC

_{ah}and subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) clay (R

^{2}= 0.33 and 0.43, respectively). Figure 5b shows EC

_{av}and topsoil clay was also small (0.19), with similarly poor correlations achieved between EC

_{av}and subsurface (R

^{2}= 0.3) and subsoil (R

^{2}= 0.42). Figure 5c shows that equivalent results were achieved between EC

_{ah}and CEC, however the correlations were larger in the topsoil (0.50), subsurface (R

^{2}= 0.47) and subsoil clay (R

^{2}= 0.56) as compared to clay. Figure 5d shows again the same trend of correlation, with the linear regression between EC

_{av}and measured CEC equivalent to topsoil (R

^{2}= 0.51), subsurface (R

^{2}= 0.47) and subsoil (R

^{2}= 0.56) CEC. We conclude that there was no satisfactory correlation between the measured soil properties and EC

_{ah}and EC

_{av}and with increasing depths and therefore no valid LR calibrations to predict these soil properties across our study field. We attribute this to the small CV and subtle differences in clay and CEC across the field.

#### 3.5. Linear Regression of Clay for All Depth Increment and σ

^{2}) could be obtained between clay and σ, we inverted the EM38 EC

_{ah}and EC

_{av}using EM4Soil. Table 5 shows the R

^{2}between estimated σ (mS/m) obtained from EM4Soil (quasi-3D) and measured clay at all depths (i.e., topsoil, subsurface and subsoil). Table 5 shows that with increase in the damping factor (i.e., λ), the correlation between σ and clay decreases, regardless of algorithm (S1 or S2) or forward model (CF or FS). The best coefficient of determination (R

^{2}= 0.65) was when S1 algorithm and FS forward model were used with a λ value of 0.07. The σ values calculated using these parameters were selected to establish a linear regression (LR) between σ and clay. This R

^{2}was a little smaller to that achieved by [38] who developed a LR (R

^{2}= 0.74) between σ and clay along a single transect in a large irrigated area. We attribute this to the fact that we predicted σ using a quasi-3D inversion, compared to the quasi-2D used along the transect. The effect was greater smoothing herein, as a larger number of neighbours were used to estimate σ.

_{av}being larger than EC

_{ah}, as shown in Figure 3b,c, respectively. The reason for the increasing σ was due to increasing clay with depth.

^{2}= 0.64).

#### 3.6. Linear Regression of CEC for All Depth Increments and σ

_{ah}and EC

_{av}EM38 data and using EM4Soil. Table 5 also shows the coefficient of determination (R

^{2}) between estimated σ (mS/m) and CEC at all depths (i.e., topsoil, subsurface and subsoil). As was the case for clay, when λ increased the coefficient between σ and CEC decreased, regardless of algorithm (S1 or S2) or forward model (CF or FS). However, this was the not the case for S1, where the best R

^{2}(0.68) overall was achieved when the FS forward model was used with a λ value of 0.9. While this coefficient was equivalent to that achieved for clay, [39] managed to develop a LR (R

^{2}= 0.89) between σ and CEC in a small field in Spain. We attribute their success to the fact the EC

_{a}data, while collected on 12 m transect spacings, was collected continuously (Veris-3100) and gridded onto a 5 × 5 m grid. In addition, the range in CEC (i.e. range) was much larger in Spain (1–23 cmol(+)/kg) than in this field (2.3–6.7 cmol(+)/kg).

_{av}(Figure 3c) being larger than EC

_{ah}(Figure 3b). As would therefore be expected, the estimates of subsoil σ were larger than topsoil σ.

^{2}= 0.66).

#### 3.7. Digital Soil Maps of Clay and CEC

_{a}data collected from the EM38 EC

_{a}across the rest of the area and through EM4Soil and the quasi-3D algorithm. Figure 7b was generated after applying the LR (CEC = 1.46 + 0.13σ) developed between σ and CEC shown in Figure 6b, with predictions applied to the quasi-3D modelled EM38 EC

_{a}data as described above for clay.

#### 3.8. Mapping the Prediction Interval (PI) of Predicted Clay and CEC

_{ah}and EC

_{av}having a much larger theoretical depth of measurement of 0–0.75 and 0–1.5 m, respectively, compared with estimating σ to a depth of 0–0.3 m. Our ability to resolve topsoil σ, and hence predict clay or CEC, was therefore poor. To improve the estimation of σ and perhaps reduce the PI, we could collect EC

_{a}at various heights. This was the approach of [42], who showed that in combination with EM31 EC

_{ah}and EC

_{av}, EM38 EC

_{ah}and EC

_{av}at a height of 0.6 m was optimal to make a LR between σ and CEC to predict CEC at 0.3 m increments and to a depth of 2.0 m along a single transect.

_{a}data on an approximate 10 × 10 m grid. To better account for some of the short scale variation, and reduce the PI, the approach of [26] would be appropriate. This is because they collected EC

_{a}from a DUALEM-21 instrument which was mobilized and coupled with a GPS and data logging capabilities. This will allow more detailed and closely spaced EC

_{a}data to be collected. We believe a similar approach here, instead of the grid (10 × 10 m) we used, will reduce smoothing and improve prediction accuracy.

#### 3.9. Soil Improvement Guidelines Based on Clay and CEC Maps

_{2}O

_{5}) and potassium (K

_{2}O) can be recommended at 113 (kg/ha), 38 (kg/ha) and 113 (kg/ha), respectively. Alternatively, compost fertiliser at the rate of 25 t/Ha can be suggested. In the southern half of the study area, the rate could be reduced, owing to the slightly larger subsurface clay (15–18%) and specifically for chemical fertiliser for N (113 kg/ha), P

_{2}O

_{5}(38 kg/ha) and K

_{2}O (75 kg/ha), while a compost fertiliser (19 t/ha) could also be considered.

## 4. Conclusions and Discussion

_{ah}and EC

_{av}with measured topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) clay (%) or CEC (cmol(+)/kg). We attribute this to the small variation in EC

_{a}as well as clay and CEC across the study field and at these three depths. However, the estimates of true electrical conductivity (σ—mS/m) generated by inverting EC

_{ah}and EC

_{av}and using a quasi-3D algorithm (EM4Soil), enabled the development of a universal LR calibration for both clay and CEC and which included the capability to predict both soil properties in the topsoil, subsurface and subsoil. For clay we found the S1 inversion algorithm with full-solution (FS) and using a damping factor (λ) = 0.07 was optimal (R

^{2}= 0.65) with the LR expressed as follows: clay (%) = 6.04 + 0.50σ. For CEC the S1 inversion algorithm, full-solution (FS) and a damping factor (λ) = 0.9 was optimal (R

^{2}= 0.68) and could be estimated as follows: CEC (cmol(+)/kg) = 1.46 + 0.13σ.

_{ah}and EC

_{av}having a theoretical depth of measurement of 0–0.75 and 0–1.5 m, respectively. Given that we were estimating σ to a depth of a topsoil, our ability to do this was not completely satisfactory. This was similarly the case for the larger PI associated with the subsoil depth.

_{a}data to better estimate σ. Using our existing EM38, we could collect additional EC

_{a}at various heights or by collecting additional data with an EM31. This was the approach carried out by [43], who showed that in combination with EC

_{ah}or EC

_{av}of EM31, and EC

_{ah}or EC

_{av}of EM38 at a height of 0.6 m was optimal to make a LR with CEC at 0.3 m increments and to a depth of 2.0 m along a single transect. Alternatively, EC

_{a}data could be collected using a multiple-coil EM instrument such as a DUALEM-421 as shown by [44,45,46].

## Author Contributions

## Acknowledgements

## Conflicts of Interests

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**Figure 1.**(

**a**) Air-photo of study site; (

**b**) EM38 survey transects (i.e., 17) and, (

**c**) calibration locations (46) where topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) samples.

**Figure 2.**Flow chart of two different approaches to establish a linear regression (LR), between (i) EM38 EC

_{a}(mS/m) in horizontal (EC

_{ah}) or vertical (EC

_{av}) and three different depths of clay (%) or CEC (cmol(+)/kg) data (i.e., topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9m), and (ii) true electrical conductivity (σ—mS/m) inverted from EM38 EC

_{ah}and EC

_{av}with clay (%) or CEC (cmol(+)/kg) using universal LR at any depth.

**Figure 3.**Contour plot of (

**a**) elevation (m), and apparent electrical conductivity (EC

_{a}– mS/m) of EM38 measured in (

**b**) horizontal (EC

_{ah}; 0–0.75 m), and (

**c**) vertical (EC

_{av}; 0–1.5 m) modes.

**Figure 4.**Contour plots of measured (

**a**) topsoil (0–0.3 m), (

**b**) subsurface (0.3–0.6 m) and (

**c**) subsoil (0.6–0.9 m) clay (%) and (

**d**) topsoil, (

**e**) subsurface and (

**f**) subsoil cation exchange capacity (CEC—cmol(+)/kg).

**Figure 5.**Plots of linear regression (LR) between apparent electrical conductivity (EC

_{a}—mS/m) measured in (

**a**) horizontal (EC

_{ah}; 0–0.75 m) and (

**b**) vertical (EC

_{av}; 0–1.5 m) modes of EM38 and measured clay (%) in the topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9) and plots of LR between (

**c**) EC

_{ah}and (

**d**) EC

_{av}and measured cation exchange capacity (CEC—cmol(+)/kg) in the topsoil, subsurface and subsoil.

**Figure 6.**Plots of measured (

**a**) clay (%) and (

**c**) cation exchange capacity (CEC—cmol(+)/kg) versus true conductivity (σ—mS/m) using λ of 0.07 and 0.9 and S1 inversion algorithm with full solution, respectively; and, plots of measured versus predicted (

**b**) clay and (

**d**) CEC using leave-one-out cross-validation, for topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m).

**Figure 7.**Predicted (

**a**) clay (%) and (

**b**) CEC (cmol(+)/kg) generated from inversion of EM38 apparent electrical conductivity (EC

_{a}—mS/m) using EM4Soil and S1 inversion algorithm, full-solution (FS) with damping factor (λ) = 0.07 (clay) and 0.9 (CEC). Note: Calculated true electrical conductivity (σ—mS/m) used to predict clay and CEC from linear regression in Figure 6a,b, respectively.

**Figure 8.**Spatial distribution of predicted clay (%) at depths of (

**a**) topsoil (0–0.3 m), (

**b**) subsurface (0.3–0.6 m) and (

**c**) subsoil (0.6–0.9 m) generated using linear regression (LR) model (Figure 5a), and cation exchange capacity (CEC—cmol(+)/kg) in (

**d**) topsoil, (

**e**) subsurface and (

**f**) subsoil.

**Figure 9.**Contour plots of the prediction interval (PI) of the predictions made for clay (%) at different depths including (

**a**) topsoil (0–0.3 m), (

**b**) subsurface (0.3–0.6 m) and (

**c**) subsoil (0.6–0.9 m); and cation exchange capacity (CEC—cmol(+)/kg) in the (

**d**) topsoil, (

**e**) subsurface and (

**f**) subsoil.

Clay (%) | Chemical Fertilizer Rates (kg/ha) | Compost Fertilizer Rates (t/ha) | ||
---|---|---|---|---|

N | P_{2}O_{5} | K_{2}O | ||

<15 | 113 | 38 | 113 | 25 |

15–18 | 113 | 38 | 75 | 19 |

18–35 | 75 | 19 | 75 | 18 |

>35 | 72 | 38 | 38 | 18 |

**Table 2.**Liming application guidelines for sugarcane in Thailand when pH less than 5.0 [29].

CEC (cmol(+)/kg) | Lime Application (t/ha) |
---|---|

<4 | 1.25 |

4–8 | 2.5 |

8–16 | 4 |

>16 | 5 |

**Table 3.**Summary statistics of apparent electrical conductivity (EC

_{a}mS/m) measured by an EM38 instrument for the entire survey area and at the 46 calibration points.

EC_{a} (mS/m) | |||||||
---|---|---|---|---|---|---|---|

Data Source | n | Min | Mean | Median | Max | Skewness | CV (%) |

Survey data | |||||||

EC_{ah} | 467 | 14 | 23.1 | 23 | 35 | 0.2 | 19.4 |

EC_{av} | 467 | 18 | 28.8 | 29 | 45 | 0.3 | 20.5 |

Calibration data | |||||||

EC_{ah} | 46 | 15 | 22.3 | 22 | 33 | 0.4 | 19.5 |

EC_{av} | 46 | 19 | 27.5 | 27.5 | 42 | 0.67 | 19.5 |

**Table 4.**Summary statistics of measured clay (%) and CEC (cmol(+)/kg) at the 46 calibration locations.

Property/Depth | n | Min | Mean | Median | Max | Skewness | CV (%) |
---|---|---|---|---|---|---|---|

clay (%) | |||||||

topsoil (0–0.3 m) | 46 | 9.4 | 11.9 | 12 | 16.8 | 0.8 | 12.2 |

subsurface (0.3–0.6 m) | 46 | 10.6 | 15.6 | 15.3 | 20.6 | 0.1 | 17 |

subsoil (0.6–0.9 m) | 46 | 14.1 | 19.1 | 19 | 23.4 | 0.1 | 11.2 |

CEC (cmol(+)/kg) | |||||||

topsoil (0–0.3 m) | 46 | 2.3 | 3.3 | 3.2 | 5 | 1.3 | 14.8 |

subsurface (0.3–0.6 m) | 46 | 2.5 | 4.1 | 4 | 6.3 | 0.6 | 18.5 |

subsoil (0.6–0.9 m) | 46 | 3.5 | 4.9 | 4.7 | 6.7 | 0.7 | 15.1 |

**Table 5.**Coefficient of determination (R

^{2}) achieved between the measured clay and CEC with the estimated true electrical conductivity (σ) generated by inverting EM38 apparent electrical conductivity (EC

_{a}—mS/m) using the EM4Soil quasi-3D model, cumulative function (CF) or full solution (FS), algorithms S1 or S2, and various damping factors (λ).

Clay | λ | S1, CF | S1, FS | S2, CF | S2, FS |

0.07 | 0.631 | 0.648 | 0.596 | 0.616 | |

0.3 | 0.625 | 0.643 | 0.537 | 0.559 | |

0.6 | 0.622 | 0.643 | 0.465 | 0.487 | |

0.9 | 0.619 | 0.637 | 0.409 | 0.430 | |

CEC | |||||

0.07 | 0.666 | 0.674 | 0.656 | 0.666 | |

0.3 | 0.666 | 0.674 | 0.641 | 0.655 | |

0.6 | 0.667 | 0.672 | 0.599 | 0.617 | |

0.9 | 0.668 | 0.676 | 0.559 | 0.577 |

**Table 6.**Summary statistics of the linear regression (LR) model established between the calculated true electrical conductivity (σ) and measured clay (%) and CEC (cmol(+)/kg). The σ was estimated using the full solution (FS), S1 algorithm and a damping factor (λ) of 0.07 and 0.9 respectively.

Clay | Parameter | Estimate | SE | t-ratio | Prob > |t| | R^{2} |

Intercept | 6.04 | 0.63 | 9.62 | <0.0001* | 0.65 | |

0.07, S1, FS | 0.50 | 0.03 | 15.84 | <0.0001* | ||

CEC | ||||||

Intercept | 1.46 | 0.16 | 9.09 | <0.0001* | 0.68 | |

0.9, S1, FS | 0.13 | 0.01 | 16.85 | <0.0001* |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Khongnawang, T.; Zare, E.; Zhao, D.; Srihabun, P.; Triantafilis, J.
Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software. *Sensors* **2019**, *19*, 3936.
https://doi.org/10.3390/s19183936

**AMA Style**

Khongnawang T, Zare E, Zhao D, Srihabun P, Triantafilis J.
Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software. *Sensors*. 2019; 19(18):3936.
https://doi.org/10.3390/s19183936

**Chicago/Turabian Style**

Khongnawang, Tibet, Ehsan Zare, Dongxue Zhao, Pranee Srihabun, and John Triantafilis.
2019. "Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software" *Sensors* 19, no. 18: 3936.
https://doi.org/10.3390/s19183936