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Article

Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

by
Emmanuel Badewa
1,
Adrian Unc
1,
Mumtaz Cheema
1,
Vanessa Kavanagh
2 and
Lakshman Galagedara
1,*
1
School of Science and the Environment, Grenfell Campus, Memorial University of Newfoundland, Corner Brook, NL A2H 5G4, Canada
2
Department of Fisheries and Land Resources, Government of Newfoundland and Labrador, Pasadena, NL A0L 1K0, Canada
*
Author to whom correspondence should be addressed.
Agronomy 2018, 8(10), 224; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy8100224
Submission received: 15 August 2018 / Revised: 6 October 2018 / Accepted: 8 October 2018 / Published: 10 October 2018
(This article belongs to the Section Water Use and Irrigation)
Browse Figures
Versions Notes

Abstract

:
Precision agriculture (PA) involves the management of agricultural fields including spatial information of soil properties derived from apparent electrical conductivity (ECa) measurements. While this approach is gaining much attention in agricultural management, farmed podzolic soils are under-represented in the relevant literature. This study: (i) established the relationship between ECa and soil moisture content (SMC) measured using time domain reflectometry (TDR); and (ii) evaluated the estimated SMC with ECa measurements obtained with two electromagnetic induction (EMI) sensors, i.e., multi-coil and multi-frequency, using TDR measured SMC. Measurements were taken on several plots at Pynn’s Brook Research Station, Pasadena, Newfoundland, Canada. The means of ECa measurements were calculated for the same sampling location in each plot. The linear regression models generated for SMC using the CMD-MINIEXPLORER were statistically significant with the highest R2 of 0.79 and the lowest RMSE (root mean square error) of 0.015 m3 m−3 but were not significant for GEM-2 with the lowest R2 of 0.17 and RMSE of 0.045 m3 m−3; this was due to the difference in the depth of investigation between the two EMI sensors. The validation of the SMC regression models for the two EMI sensors produced the highest R2 = 0.54 with the lowest RMSE prediction = 0.031 m3 m−3 given by CMD-MINIEXPLORER. The result demonstrated that the CMD-MINIEXPLORER based measurements better predicted shallow SMC, while deeper SMC was better predicted by GEM-2 measurements. In addition, the ECa measurements obtained through either multi-coil or multi-frequency sensors have the potential to be successfully employed for SMC mapping at the field scale.

1. Introduction

Development of site-specific management (SSM) over large fields is the goal of precision agriculture (PA). PA encompasses the use of spatial and temporal information to support decisions on agronomic practices that best match soil and crop requirements as they vary in the field [1,2]. Lesch et al. [3] have shown that different types of spatial information such as soil texture and salinity derived from bulk apparent electrical conductivity (ECa) obtained by electromagnetic induction (EMI) surveys can offer significant support to the development of accurate management decisions for agricultural fields. PA provides a way to automate SSM using information technology, thereby making SSM practical in commercial agriculture. It includes all those agricultural production practices that use information technology either to tailor input to achieve desired outcomes, or to monitor those outcomes (e.g., variable rate application of pesticides and fertilizers) [4]. Hence, the measurement of ECa using the EMI technology has been proposed as an effective and rapid response methodology in support of PA [5,6].
Literature shows that ECa has the potential to become a widely adopted means for characterizing the spatial variability of soil properties at field and landscape scales [7,8]. Spatial variability of soil properties can also be characterized by other means such as ground penetrating radar (GPR) [9,10], time domain reflectometry (TDR) [11,12], cosmic-ray neutrons [13,14], aerial photography [5,15], multi- and hyper-spectral imagery [16,17], or by a combination of several approaches as shown by Rudolph et al. [18].
Soil Moisture Content (SMC) is a major factor that influences ECa and agricultural practices. Factors affecting ECa include SMC, soil temperature, high clay content, and soluble salts (i.e., pore water conductivity) [19,20,21,22]. When soil salinity, texture, mineralogy and temperature are constant, ECa is a direct function of SMC [23,24]. Under such conditions, several authors have established that there is a linear relationship between SMC and ECa [25,26,27,28]. However, other researchers have established relationships between ECa and other soil properties such as soil salinity [29,30], saturated percentage [7,31] and soil bulk density [32,33]. Furthermore, SMC is widely recognized as a driving factor for agricultural productivity as it governs germination and plant growth [34]. Given the time, labour, and cost of traditional soil sampling (Huang et al. 2014) [35], the development of an accurate proxy alternative for measuring the spatio-temporal variability of SMC, such as EMI, is essential for efficient soil and crop management at large scales [36].
Few research studies have been conducted to evaluate the potential of multi-coil and multi-frequency EMI sensors such as CMD-MINIEXPLORER (GF Instruments, Brno, Czech Republic) and GEM-2 (Geophex Ltd., Raleigh, NC, USA), respectively, to estimate soil properties. Multi-coil EMI sensors have multiple coil spacing and coil orientations which operate with one frequency and have one transmitter and three coplanar receiver coils at different distances [33]. Multi-frequency EMI sensors have different frequencies and coil orientations which operate with one transmitter coil and a receiver coil separated at a specific distance [37]. In general, the theoretical depth of investigations (DOIs) is calculated based on the knowledge of coil separation and frequency [38,39]. Most research studies to date have adopted the use of EMI instruments to estimate soil properties under different soils and crop systems e.g., [40,41,42]; however, the use of multi-coil and multi-frequency EMI sensors to determine the variability in podzolic soils is still limited [43]. This might be attributed to the noisy data and low ECa values generated from the application of EMI sensors to podzolic soils.
Podzols are coarse to medium textured soils formed from acidic parent material. They are distinctively characterized by illuviated B horizons where humified organic matter combined in varying degrees with Al and Fe accumulate, often overlain by a light coloured eluviated (Ae) horizon [44]. Globally, podzolic soils are widely spread in the temperate and boreal regions of the Northern Hemisphere and occupy approximately 4% (485 million ha) of the earth’s total land surface [45]. The adaptation of podzolic soils for agriculture is growing because of the increased demand on the current agricultural land base, application of intensive mechanization, fertilization, and water management practices [46], and is favoured by climate-change related northward shift in favourable climatic parameters [47]. In addition, due to podzolic soils’ distinctive morphology, the conversion to agricultural land can significantly affect their hydrological parameters such as SMC [48,49]. Despite their uniqueness, there is limited information available on water management on podzolic soils for effective agricultural production [46]. Furthermore, literature suggests that little or no work has been carried out to estimate spatio-temporal variability of SMC for managed podzols [43].
The objectives of this study were: (i) to evaluate the multi-coil (CMD-MINIEXPLORER) and multi-frequency (GEM-2) EMI sensor data and the various combinations of these instruments for agricultural systems on managed podzols; (ii) to develop a relationship between ECa, as estimated by both instruments, and SMC measured using HD2-TDR; and (iii) to estimate the accuracy of regression models between the ECa and SMC.

2. Materials and Method

2.1. Study Site

The study was carried out at Pynn’s Brook Research Station (PBRS) (49°04′20″ N, 57°33′35″ W), Pasadena, Newfoundland (Figure 1), Canada. The reddish brown to brown podzolic soil developed on a gravelly sandy fluvial deposit with > 100 cm depth to bedrock and a 2%–5% slope [50]. Soil samples from the topsoil (n = 7) analysed for the study site revealed a gravelly loamy sand soil (sand = 82.0% (±3.4); silt = 11.6% (±2.4); clay = 6.4% (±1.2)), which is classified as orthic Humo-ferric podzol, according to Canadian Soil Taxonomy [50]. The average bulk density and porosity for the study site (n = 28) at 15 cm soil depth were 1.31 g cm−3 (±0.07) and 51% (±0.03), respectively. Based on the 30-year data (1986–2016) of the nearby Deer Lake weather station from Environment Canada (http://climate.weather.gc.ca/), the area receives an average precipitation of 1113 mm per year with less than 410 mm falling as snow, and has an annual mean temperature of 4 °C.

2.2. SMC Data Recording and TDR Calibration

During the study, SMC was measured using a hand-held time domain reflectometry (TDR) probe. The TDR measured SMC data were first compared with the calculated SMC, which was determined by multiplying gravimetric SMC (θg) with the measured average soil bulk density of 1.31 g cm−3. The TDR measured SMC was compared with the calculated SMC to evaluate the field scale accuracy of the TDR probe. The average gravimetric SMC, θg (g g−1) was determined for the 0–20 cm (θg(0–20)) depth range by oven drying moist soil samples at 105 °C for 48 h. An integrated TDR, known as HD2-TDR (IMKO Micromodultechnik GmbH, Germany) with probe lengths of 11 cm (θv(0–11)), 16 cm (θv(0–16)) and 30 cm (θv(0–30)) [51] was used. Also, the mean soil temperature measured from the HD2-TDR precision soil moisture probe was used for the temperature conversion of measured ECa data.

2.3. EMI Survey

In this study, ECa was measured using the multi-coil CMD-MINIEXPLORER (GF instruments, Brno, Czech Republic) and the multi-frequency GEM-2 (Geophex, Ltd., Raleigh, NC, USA). The CMD-MINIEXPLORER has 3 coil separations and can be operated at vertical coplanar (VCP) and horizontal coplanar (HCP) coil configurations. The CMD-MINIEXPLORER therefore generates six pseudo depths (PDs), also known as depths of investigation (DOI), of 25, 50 and 90 cm when using VCP modes, and 50, 100, 180 cm when using HCP modes [33]. The theoretical calculation of the DOI for GEM-2 is at a deeper depth compared to the CMD-MINIEXPLORER [52]. However, the accuracy of the DOI of GEM-2 with varying frequencies under heterogenic field conditions is yet to be determined. Based on the preliminary data obtained on the site, the CMD-MINIEXPLORER with the largest coil separation (coil 3 = 118 cm) with PDs of 90 and 180 for VCP and HCP modes, respectively, and a 38 kHz frequency of GEM-2 (the coil separation is 166 cm) were employed in this study. The CMD-MINIEXPLORER at the VCP configuration is represented with ECa-L and at the HCP configuration is represented with ECa-H while GEM-2 at the HCP configuration is represented with ECa-38 kHz. Surveys with CMD-MINIEXPLORER were conducted at a height of 15 cm. The GEM-2 device was carried with the supplied shoulder strap at an average height of 100 cm.
Several studies suggested temperature conversion of raw ECa to a standard soil temperature (25 °C) e.g., [1,53] using:
EC25 = ECt × (0.4470 + 1.4034 e−t/26.815)
where ECt is the ECa data collected at measured soil temperature (°C) and EC25 is the temperature corrected ECa.
To avoid data shifts, both sensors were allowed a warm up period of at least 30 min before measurements were recorded [54]. However, no instrumental drift was expected in the ECa due to the high temperature stability of the CMD-MINIEXPLORER and GEM-2 [37,55].
EMI surveys were conducted on a small field (45 m × 8.5 m) and a large field (0.45 ha) using CMD-MINIEXPLORER and GEM-2. The large field comprises of the grass, silage-corn and soybean plots while the small field is a portion of the silage-corn experimental plot selected for a detailed field study (Figure 1). The ECa measurements were carried out on 30 September, 6 October, and 18 November in Fall 2016, and on 31 May in Spring 2017. The relationship between CMD-MINIEXPLORER and GEM-2 was assessed by comparing the patterns and trends of measured ECa data from both instruments using a 45 m linear transect collected on 30 September on the small field.

2.4. Field Calibration and Validation

The small field was used to calibrate and validate the relationship between SMC and ECa using data collected on 30 September and 6 October 2016, respectively. The calibration was carried out using the ECa data (ECa-L, ECa-H and ECa-38 kHz) and measured SMC data collected with the HD2-TDR probes (0–11, 0–16, and 0–30 cm) on 30 September 2016. The validation was then carried out using the ECa data and HD2-TDR measured SMC at 0–11 and 0–16 cm depths on 6 October 2016. The validation was further carried out on a 30 m transect on the silage-corn plot and the grass plot at the study site using the data collected on 31 May 2017. The small field survey was carried out on a gridded plot (without GPS) for constant precise point calibration and validation. The proximally sensed ECa was determined using the mean ECa measurements (n = 20) generated on the small field from CMD-MINIEXPLORER and GEM-2 survey data collected on the same day along each of the selected twenty sampling locations similar to Zhu et al. [56].
To test ECa response to SMC variability at a larger spatial scale, a large field study was conducted to validate the regression model generated from the small field on 18 November 2016. The EMI survey on the large field was carried out by walking across the entire field with a GPS connected to CMD-MINIEXPLORER and GEM-2 to obtain geo-referenced ECa data. Also, twenty-seven geo-referenced SMC data points (θv(0–16)) were collected using the HD2-TDR 0–16 cm length probe only and a hand held GPS according to the stratified sampling locations.

2.5. Soil Sampling

The silage-corn trial on the small field consisted of 4 m × 1 m plots that received different nutrient management treatments using biochar (BC), dairy manure (DM), inorganic fertilizer, or a combination of these. Soil samplings on the small field were done by selecting twenty sampling locations based on the BC and DM application, though the treatment effects were not significant across the small field [28]. Soil samples were collected from the depths of 0–10 cm and 10–20 cm using a gouge auger and a hammer. Samples were placed in airtight bags and stored in a polystyrene cooler until gravimetric SMC (θg) measurements were carried out in the laboratory.

2.6. Data Analysis

Descriptive statistics (min, max, mean, median, skewness, kurtosis and coefficient of variation, CV) were carried out to evaluate the EMI data and SMC data. Paired sample t-tests were carried out to determine if there were any statistically significant differences between the ECa and SMC means. Pearson’s correlation coefficients (r) were used to establish the relationship between ECa data and SMC data. The coefficient of determination (R2) was used to evaluate the relationship among EMI results. The root mean square error (RMSE) was used to evaluate the accuracy of the HD2-TDR measured SMC. The root mean square error of prediction (RMSEP) was used to estimate the accuracy of predicted SMC using the ECa and TDR measured SMC data. A simple linear regression was used to evaluate the relationship between ECa and SMC data. All analyses were performed with Minitab 17 (Minitab 17 Statistical Software, 2010) and ECa maps were generated using Surfer 8 (Golden Software, 2002).

3. Results

3.1. SMC Results

A good match between the measured SMC (θv) from HD2-TDR and the calculated SMC (θv) by using gravimetric SMC (θg) was obtained, with an R2 of >0.88 and a RMSE < 0.04 m3 m−3 for all three TDR probe lengths (Figure 2 and Table 1). Accuracy of the HD2-TDR for the 16 cm probe length is similar to the RMSE of 0.013 m3 m−3 by Topp et al. [11] while the HD2-TDR for the 11 and 30 cm probe lengths has RMSE values of 0.040 m3 m−3 and 0.018 m3 m−3, respectively (Figure 2 and Table 1).

3.2. EMI Results

The ECa patterns and trends along the 45 m transect on the small field were similar for CMD-MINIEXPLORER and GEM-2, despite different DOIs (Figure 3 and Figure 4). The data from CMD-MINIEXPLORER plotted against the GEM-2 data (Figure 5) show that ECa values of ECa-H are closely associated to that of GEM-2 (R2 = 0.71) compared to ECa-L (R2 = 0.40). The possibility of integrating the mean ECa measurements from the CMD-MINIEXPLORER and the GEM-2 was evaluated with the average of ECa-L, ECa-H and ECa-38 kHz and analysed using the backward stepwise multiple linear regression (MLR). The results indicated that they were redundant.

3.3. Basic Statistics

The descriptive statistics of the ECa measurements from CMD-MINIEXPLORER, GEM-2 and the TDR measured SMC in the study site are given in Table 2. According to the classification of Warrick and Nielsen [57], CVs of CMD-MINIEXPLORER were low (CV < 12%) while those of GEM-2 were moderate (12% < CV < 62%). The CVs of TDR measured SMC were moderate (CV > 12%) except for the θv(0–11) depth, which was low (Table 2).
A paired sample t-test was performed using a sample of 20 ECa data points from the small field to determine whether there was a difference between means of ECa from CMD-MINIEXPLORER and GEM-2. Results revealed that ECa means of ECa-38 kHz (3.214 ± 0.718) were significantly different from ECa-L (3.576 ± 0.323) and ECa-H (4.139 ± 0.466), with p = 0.050 and p = 0.000, respectively.
A paired sample t-test was also carried out on a sample of 20 SMC data points to determine whether there was a difference in the SMC means at different depths. The SMC mean for θv(0–11) (0.28755 ± 0.03241) was significantly different from the means obtained for θv(0–16) (0.25268 ± 0.03690) and θv(0–30) (0.2471 ± 0.0507); both differences had the same p = 0.000. Pearson’s correlation coefficients among ECa measurements and SMC are shown in Table 3. At a p-value < 0.1, ECa data (CMD-MINIEXPLORER and GEM-2) were significantly correlated with SMC measurements.

3.4. Regression Analysis

The fitted linear regressions (LRs) to estimate SMC for different integral depths using measured ECa with CMD-MINIEXPLORER or GEM-2 data are shown in Figure 6 and respective statistics for calibration and validation between ECa and TDR measured SMC are summarized in Table 4. The SMC estimates obtained using ECa-L (R2p = 0.38 and 0.54) are better than the estimates for ECa-H and ECa-38 kHz, with RMSEP 0.033 and 0.031 m3 m−3, respectively (Table 4).
Because the purpose of the large field study was to evaluate the ECa response to variability in SMC at a larger spatial scale, only the θv(0–16) depth with the highest accuracy for the study site (Table 1) was measured at 27 geo-referenced locations on the field. The linear regression between θv(0–16) and ECa-L on the small field was used for the large field study. The estimates of SMC for θv(0–16) using ECa-L were lower for the large field study than for the small field study (RMSEP = 0.076 m3 m−3).
The same linear regressions were applied to a 30 m transect in the corn-silage plot and the grass plot at the study site (Table 5) for validation of linear regressions. The estimates of SMC via ECa-L for the grass plot had lower R2 values (from 0.07 to 0.32) and higher RMSEP (from 0.039 to 0.074 m3 m−3) than for the silage-corn plot (R2 = from 0.30 to 0.59; RMSEP = from 0.041 to 0.072 m3 m−3). Overall, fitted linear regressions developed between ECa and SMC in this study have shown higher prediction accuracy for ECa-L than for ECa-H and ECa-38 kHz.

3.5. ECa Mapping

The spatial variability of ECa was mapped across the study site by variogram analysis and ordinary block kriging using Surfer 8 (Golden Software, 2002, Golden, CO, USA). The trends of ECa data from the CMD-MINIEXPLORER and the GEM-2 show similar patterns despite different DOIs (or sampling volume) and ECa values (Figure 7). For instance, larger ECa values were measured at the northwest and southeast sections of the study site while lower ECa values were found on the northeast section, which stretches across the middle area of the field. The map of SMC predicted using the ECa-L and the 27 georeferenced measurements (Figure 8) shows similar patterns with lower values (<0.28 m3 m−3) across the centre of the study site.

4. Discussion

The factory calibration of HD2-TDR is not sufficient for field applications as it was carried out in a repacked soil with uniform temperature and low bulk electrical conductivity [51]. Also, a low representative elemental volume of soils, which affects the variability of moisture content, has been reported for many current sensor technologies as well as direct sampling methods [58]. This variability has been attributed to several factors such as gravel content and position in the landscape, which influences water content variation across the field [58]. In this study, visual observations indicated a highly disturbed soil surface and high gravel content at the 0–10 cm soil depth and positions of measurement (point measurements) within the study area. This may be assumed to have led to differences between the 11 cm HD2-TDR probe data and the calculated SMC from the gravimetric SMC (Figure 2). This behaviour implies that it is not a field error (Std Dev = 0.037 m3 m−3), but a high spatial variability of the field water content within the shallow depth.
Khan et al. [43] reported a low ECa, between 2.1 and 35.5 mS m−1, on an orthic Humo-ferric podzol while Pan et al. [59] indicated a low ECa between 1.36 and 3.29 mS m−1 in a sandy soil. Martini et al. [60] also observed a low ECa, between 0 and 24 mS m−1, with a very small range of spatial variation which was predominantly attributed to the low heterogeneity of soil texture (Sand = 6%–28%, Silt = 55%–79%, Clay = 13%–25%). These ECa ranges of previous studies are similar to the results of our study site, classified as an orthic Humo-ferric podzol, with a lower ECa ranging between 0 and 7 mS m−1 and also with a low textural variation (Sand = 80.10%–83.75%, Silt = 10.44%–12.58%, Clay = 5.81%–7.32%). Although the report by Martini et al. [60] has low sand content and variation, the clay content (which is one of the factors that can influence ECa; McNeill [20]) is lower at both sites of this study.
The depth range (0–20 cm) considered in this study, also includes the Podzolic Ae horizon with a texture that is coarser than the adjacent horizons [44]. The known depth-response function of CMD-MINIEXPLORER has been used by various authors to calibrate the sensor, even though not all coil separations exhibit low signal to noise levels [33,61].
Arguably, the multi-frequency GEM-2 sensor measures at a deeper DOI compared to the multi-coil CMD-MINIEXPLORER sensor. The measured ECa from the GEM-2 sensor has lower values compared to the measured ECa from the CMD-MINIEXPLORER sensor with known DOIs of 90 cm and 180 cm for low (ECa-L) and high (ECa-H) coil 3 dipole configurations, respectively. Evaluating the ECa measurements by GEM-2 with the site soil and parent material using the EMI skin depth, Nomogram [52] also confirmed a greater DOI than 180 cm. When the DOI increases, weaker signals indicate a less conductive soil, whereby stronger signals are observed with decreasing DOI [38,39]. Additionally, the CMD-MINIEXPLORER with the coil 3 dipole configuration adopted in this study shows the highest local sensitivity at a depth between 0–35 cm and 0–75 cm, according to the sensitivity function by McNeil [20]. This provides a reasonable match between the sensing volume of EMI and the depth range sampled by the HD2-TDR precision soil moisture probe, considering the DOI from the soil surface as zero. The largest coil separation in VCP mode was also less sensitive to variations in instrument height that inevitably occurred when EMI measurements were carried out.
Warrick and Nielsen [57] proposed the use of CV categories, which have been widely adopted to assess the soil’s spatial variability. This procedure allows for comparisons across samples that employ different units of measurement [62]. However, the geostatistical techniques must be carried out to understand the spatial dependence among the variables [63]. Molin and Faulin [64] found CVs for ECa and SMC to be moderate (43% and 57%). These findings are similar to the results of this study even though CVs are less than 23% (Table 2). The CVs of ECa-L, ECa-H, and ECa-38 kHz measurements and measured SMC (Table 2) suggest that ECa values respond to vertical heterogeneity of soil properties [65] such as SMC variability along the soil depth.
Other researchers also found considerable site-to-site variability in the relationship between ECa and SMC e.g., [25], similar to our study. The R2 and RMSE of validation models are not consistent when compared to those of calibration models (Table 4). For instance, calibration using θv(0–16) produced an R2 of 0.74 and RMSE of 0.018 m3 m−3 while validation produced R2 of 0.54 and RMSEP of 0.031 m3 m−3. The R2 generated when the detailed field study regression models were applied to the grass plot showed a need for site-specific calibration to establish the relationship between ECa and SMC (Table 5). Also, the R2 and the RMSE values for SMC presented in Figure 6 for ECa-L, ECa-H, and ECa-38 kHz measurements vary by 0.031 m3 m−3 and 0.040 m3 m−3. This implies that the variation in SMC can be attributed to the maximum sensitivity of the ECa.
Martini et al. [60] observed that SMC monitoring using ECa requires the determination of the temporal variations of all other variables that can induce ECa (e.g., temperature and ECw) while Altdorff et al. [66] reported that EMI has the potential to account for a strong influence of SMC on ECa. Even though our study did not account for all variables, the data set used in this study gave a reasonably accurate site-specific calibration of SMC at the study site. However, spatial statistics techniques such as variogram modelling are needed to confirm the number of required sampling points and capture the spatial variability more accurately.
This study confirms the relationship between ECa and SMC through the correlation between the spatial pattern of ECa (Figure 7) and SMC (Figure 8). Regions of low ECa correspond to regions of low SMC and vice versa. For instance, the region with the ECa > 5 mS m−1 corresponds to the SMC region > 0.28 m3 m−3 and the region with ECa < 4 mS m−1 corresponds to the SMC region < 0.23 m3 m−3. The spatial variability of geo-referenced SMC is lower than ECa-L predicted SMC (Figure 8), as expected. This may indicate the need for more sampling locations to fully capture the spatial variability of SMC and its effects on the map interpolation.

5. Conclusions

Analysis of the relationships between ECa measurements using two EMI sensors (CMD-MINIEXPLORER and GEM-2), and SMC using oven drying and HD2-TDR methods were carried out on a podzolic soil at an experimental site in western Newfoundland, Canada. Linear regression analysis used to estimate SMC from the two EMI sensors using ECa data at the study site provided the best prediction for SMC at 0–11 cm and 0–16 cm depth ranges.
The validation results show that to derive reasonably accurate regression models for predicting SMC from EMI measurements for field scale mapping of SMC, sitespecific calibration is required. The site-specific calibration of ECa-SMC can be determined using linear regression models. This can be attributed to the potential of CMD-MINIEXPLORER and GEM-2 to measure the strong influence of SMC on ECa implying that the SMC is a major driver of ECa measurement at the study site.
A good relationship was found between the measured ECa from CMD-MINIEXPLORER and GEM-2 at the study site. The CMD-MINIEXPLORER and GEM-2 were observed to have similar values for the selected coil orientation and frequency used in this study. Though the temperature effect is minimal, it is important to conduct the direct measurements and EMI measurements from the two EMI sensors within a short period of time as there will be minor changes of SMC.
Further research on the prediction of profile depth and sampling volume at the field needs to be conducted to confirm if SMC is the basic driver of CMD-MINIEXPLORER and GEM-2 response along the depth and horizontal variation at a large scale.

Author Contributions

Conceptualization, L.G.; Data curation, E.B. and L.G.; Formal analysis, E.B.; Funding acquisition, L.G.; Investigation, E.B.; Methodology, E.B. and L.G.; Project administration, L.G.; Resources, M.C., V.K. and L.G.; Supervision, A.U. and L.G.; Writing—original draft, E.B.; Writing—review & editing, A.U., M.C., V.K. and L.G.

Funding

This research was funded by Research and Development Corporation of Newfoundland and Labrador through Ignite R&D program 5404-1962-101 and the Research Office of Grenfell Campus, Memorial University of Newfoundland through stat-up fund 20160160.

Acknowledgments

We acknowledge an MSc—BEAS graduate fellowship from Memorial University, E. Badewa, and data-collection support by Marli Vermooten, Dinushika Wanniarachchi and Kamaleswaran Sadatcharam.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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Figure 1. The location of Pynn’s Brook Research Station (PBRS), Pasadena (49°04′20″ N, 57°33′35″ W) in Newfoundland, Canada and the study site.
Figure 1. The location of Pynn’s Brook Research Station (PBRS), Pasadena (49°04′20″ N, 57°33′35″ W) in Newfoundland, Canada and the study site.
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Figure 2. Comparison of the measured θv using the HD2-TDR and calculated θv by using the measured θg and bulk density at Pynn’s Brook Research Station.
Figure 2. Comparison of the measured θv using the HD2-TDR and calculated θv by using the measured θg and bulk density at Pynn’s Brook Research Station.
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Figure 3. Results of small field EMI surveys on 30 September and 6 October 2016 for ECa-L (a,d), ECa-H (b,e) and ECa-38 kHz (c,f).
Figure 3. Results of small field EMI surveys on 30 September and 6 October 2016 for ECa-L (a,d), ECa-H (b,e) and ECa-38 kHz (c,f).
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Figure 4. Variability of the measured ECa by the two EMI sensors on a 45 m linear transect in the small field.
Figure 4. Variability of the measured ECa by the two EMI sensors on a 45 m linear transect in the small field.
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Figure 5. Scatter-plot of ECa measured using CMD-MINIEXPLORER and GEM-2.
Figure 5. Scatter-plot of ECa measured using CMD-MINIEXPLORER and GEM-2.
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Figure 6. Plots of predicted θv (m3 m−3) using ECa data versus TDR measured θv (m3 m−3) for the linear regressions given in Table 4 for ECa-L, ECa-H and ECa-38 kHz.
Figure 6. Plots of predicted θv (m3 m−3) using ECa data versus TDR measured θv (m3 m−3) for the linear regressions given in Table 4 for ECa-L, ECa-H and ECa-38 kHz.
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Figure 7. Spatial variability maps of ECa for the large field study (a) ECa-L (b) ECa-H (c) ECa-38 kHz.
Figure 7. Spatial variability maps of ECa for the large field study (a) ECa-L (b) ECa-H (c) ECa-38 kHz.
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Figure 8. Spatial variability maps of SMC for the large field study estimated using ECa-L measurements (a) and 27 geo-referenced point measurements using the HD2-TDR (b).
Figure 8. Spatial variability maps of SMC for the large field study estimated using ECa-L measurements (a) and 27 geo-referenced point measurements using the HD2-TDR (b).
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Table
Table 1. Linear regression, coefficient of determination (R2) and root mean square error (RMSE) for HD2-TDR calibration at Pynn’s Brook Research Station using the calculated θv from θg (n = 10).
Table 1. Linear regression, coefficient of determination (R2) and root mean square error (RMSE) for HD2-TDR calibration at Pynn’s Brook Research Station using the calculated θv from θg (n = 10).
SMCRegression EquationR2RMSE
θv(0–11)0.8646 (θv) + 0.07080.890.040
θv(0–16)0.9330 (θv) + 0.01930.880.013
θv(0–30)1.2137 (θv) − 0.04620.900.018
Table
Table 2. Descriptive statistics of the ECa (mS m−1) measurements using CMD-MINIEXPLORER and GEM-2 and TDR measured SMC (m3 m−3) at the study site (n = 20).
Table 2. Descriptive statistics of the ECa (mS m−1) measurements using CMD-MINIEXPLORER and GEM-2 and TDR measured SMC (m3 m−3) at the study site (n = 20).
DepthMinMaxMeanMedianSkewnessKurtosisCV
ECa-L2.793.993.58 a3.68−0.90.59.0
ECa-H3.454.884.14 a4.14−0.1−1.011.3
ECa-38 kHz2.154.583.21 b3.20.2−0.922.4
θv(0–11)0.230.340.29 c0.30−0.5−0.611.3
θv(0–16)0.160.310.25 d0.26−0.70.214.6
θv(0–30)0.160.350.25 d0.260.1−0.420.5
Means that do not share a letter are significantly different at 5% probability.
Table
Table 3. Pearson’s correlation coefficients of the ECa measurements of CMD-MINIEXPLORER and GEM-2 and TDR measured SMC at the study site (n = 20). Significance is reported at the 0.1 (*), 0.05 (**), and 0.001 (***) p-values.
Table 3. Pearson’s correlation coefficients of the ECa measurements of CMD-MINIEXPLORER and GEM-2 and TDR measured SMC at the study site (n = 20). Significance is reported at the 0.1 (*), 0.05 (**), and 0.001 (***) p-values.
ECa-LECa-HECa-38 kHzθv(0–11)θv(0–16)θv(0–30)
ECa-L1
ECa-H0.88 ***1
ECa-38 kHz0.63 **0.84 ***1
θv(0–11)0.89 ***0.74 ***0.54 **1
θv(0–16)0.86 ***0.68 ***0.50 **0.95 ***1
θv(0–30)0.59 **0.42 *0.41 *0.75 ***0.79 ***1
Table
Table 4. Linear regressions between ECa data from CMD-MINIEXPLORER and GEM-2 with TDR measured SMC for different integral depths (n = 20).
Table 4. Linear regressions between ECa data from CMD-MINIEXPLORER and GEM-2 with TDR measured SMC for different integral depths (n = 20).
ECaSMCRegression EquationCalibrationValidation
R2RMSER2pRMSEP
ECa-Lθv(0–11)0.0888 ECa-L − 0.03010.790.0150.380.033
θv(0–16)0.0983 ECa-L − 0.09880.740.0180.540.031
θv(0–30)0.0925 ECa-L − 0.08360.350.040--
ECa-Hθv(0–11)0.0515 ECa-H + 0.07430.550.0210.150.032
θv(0–16)0.0542 ECa-H + 0.02840.470.0260.320.031
θv(0–30)0.0462 ECa-H + 0.0560.180.045--
ECa-38 kHzθv(0–11)0.0243 ECa-38 kHz + 0.20950.290.0270.010.036
θv(0–16)0.0257 ECa-38 kHz + 0.17010.250.0310.050.040
θv(0–30)0.0292 ECa-38 kHz + 0.15330.170.045--
Table
Table 5. Validation of the fitted linear regressions summarised in Table 4, using ECa data from CMD-MINIEXPLORER and GEM-2 with TDR measured SMC on a 30 m transect (n = 11).
Table 5. Validation of the fitted linear regressions summarised in Table 4, using ECa data from CMD-MINIEXPLORER and GEM-2 with TDR measured SMC on a 30 m transect (n = 11).
SMCECaSilage Corn PlotGrass Plot
R2pRMSEPR2pRMSEP
θv(0–11)ECa-L0.300.0460.130.066
ECa-H0.350.0540.320.062
ECa-38 kHz0.300.0410.300.074
θv(0–16)ECa-L0.550.0700.070.071
ECa-H0.580.0440.260.053
ECa-38 kHz0.590.0720.230.061
θv(0–30)ECa-L--0.070.062
ECa-H--0.180.039
ECa-38 kHz--0.140.040

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Badewa, E.; Unc, A.; Cheema, M.; Kavanagh, V.; Galagedara, L. Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols. Agronomy 2018, 8, 224. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy8100224

AMA Style

Badewa E, Unc A, Cheema M, Kavanagh V, Galagedara L. Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols. Agronomy. 2018; 8(10):224. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy8100224

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Badewa, Emmanuel, Adrian Unc, Mumtaz Cheema, Vanessa Kavanagh, and Lakshman Galagedara. 2018. "Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols" Agronomy 8, no. 10: 224. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy8100224

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