Mapping soil salinity in 3-dimensions using an EM38 andEM4Soil inversion modelling at the reconnaissance scale incentral Morocco
H. DAKAK1, J. HUANG
2, A. ZOUAHRI1, A. DOUAIK
1 & J. TRIANTAFILIS2
1National Institute of Agricultural Research, Avenue de la Victoire, Rabat, Morocco, and 2School of Biological, Earth and
Environmental Sciences, UNSW Sydney, Kensington, NSW 2052, Australia
Abstract
Large areas of Morocco require irrigation and although good quality water is available in dams,
farmers augment river water with poorer quality ground water, resulting in salt build-up without a
sufficient leaching fraction. Implementation of management plans requires baseline reconnaissance
maps of salinity. We developed a method to map the distribution of salinity profiles by establishing a
linear regression (LR) between calculated true electrical conductivity (r, mS/m) and electrical
conductivity of the saturated soil-paste extract (ECe, dS/m). Estimates of r were obtained by
inverting the apparent electrical conductivity (ECa, mS/m) collected from a 500-m grid survey using
an EM38. Spherical variograms were developed to interpolate ECa data onto a 100 m grid using
residual maximum likelihood. Inversion was carried out on kriged ECa data using a quasi-3d model
(EM4Soil software), selecting the cumulative function (CF) forward modelling and S2 inversion
algorithm with a damping factor of 3.0. Using a ‘leave-one-out cross-validation’ (LOOCV), of one in
12 of the calibration sites, the use of the q-3d model yielded a high accuracy (RMSE = 0.42 dS/m),
small bias (ME = �0.02 dS/m) and Lin’s concordance (0.91). Slightly worse results were obtained
using individual LR established at each depth increment overall (i.e. RMSE = 0.45 dS/m; ME = 0.00
dS/m; Lin’s = 0.89) with the raw EM38 ECa. Inversion required a single LR (ECe = 0.679 +0.041 9 r), enabling efficiencies in estimating ECe at any depth across the irrigation district. Final
maps of ECe, along with information on water used for irrigation (ECw) and the characterization of
properties of the two main soil types, enabled better understanding of causes of secondary soil
salinity. The approach can be applied to problematic saline areas with saline water tables.
Keywords: Soil mapping, electrical conductivity, soil salinity, baseline data, EM inversion modelling
Introduction
Morocco is located in one of the driest regions of the world
with most of the land classified as arid (~85%). Of the
9 million hectares of arable land, irrigation is used to
supplement the meagre rainfall across 10% of this area. There
are nine irrigation districts, of which Tadla is the largest and
covers over 120 000 ha. As for many irrigated areas, the
quality and availability of water are becoming increasingly
problematic. Although water in storage dams has little
salinity, its quality diminishes downstream (Badraoui et al.,
2002). In addition, farmers augment river water with poorer
quality groundwater (Badraoui et al., 2002). Both factors are
beginning to limit sustainable soil management as salts build-
up in the absence of a sufficient leaching fraction. In addition,
the Intergovernmental Panel on Climate Change (IPCC)
climate models suggest annual rainfall will decrease over the
next few decades (MATUHE, 2001).
Given water authorities are struggling to distribute and
provide potable water for domestic and industrial use,
knowledge about the current status of soil salinity is necessary
to monitor the impacts of applying degraded water to arable
land. Collecting the necessary soil baseline information across
large irrigated areas and throughout the root-zone is costly
and time-consuming (Odeh et al., 1998). However, proximal
sensing electromagnetic (EM) induction instruments have
been used to augment limited laboratory measurements of
extracts of a saturated soil paste (i.e. ECe – dS/m). There is a
growing body of literature showing increasing research inCorrespondence: J. Triantafilis. E-mail: [email protected]
Received September 2016; accepted after revision July 2017
© 2017 British Society of Soil Science 1
Soil Use and Management doi: 10.1111/sum.12370
SoilUseandManagement
developing countries aimed at using such an approach to
measure and map soil salinity. This includes Indonesia
(McLeod et al., 2010), Turkey (Cetin et al., 2012), Uzbekistan
(Akramkhanov et al., 2014) and China (Yao & Yang, 2010;
Guo et al., 2013) where in coastal tidelands susceptibility to
shallow water tables and saline soil conditions after
reclamation requires knowledge for monitoring and
management. In Africa, similar work has also been
undertaken including South Africa (Johnston et al., 1997),
Senegal (Ceuppens & Wopereis, 1999; Barbi�ero et al., 2001)
and Tunisia (Arag€u�es et al., 2011).
Most of the studies have been conducted at the field level. In
addition, as described by Amezketa & del Valle de Lersundi
(2008), the first step is the development of a linear regression
between the measured apparent electrical conductivity (ECa)
and average ECe (i.e. for 0–1.0 m depth). A similar approach
was used by Triantafilis & Buchanan (2010) to map subsurface
saline material (6–12 m) across an irrigated district. The
problem with these approaches is that managing the salinity
requires knowledge of whether the salt distribution is normal
(i.e. increasing salinity with depth), uniform (e.g. nonsaline
[<2 dS/m] or moderately saline [4–8 dS/m]) or inverted (i.e.
moderately saline topsoil and slightly saline subsoil). To
resolve this, various authors have developed linear regression
(LR) models at different depths (e.g. Yao & Yang, 2010).
However, this approach requires different models for mapping
soil ECe at different depths, and it is not able to predict soil
ECe at depths where soil samples are not collected. It can also
introduce uncertainty at different depths due to the use of
different input data (Huang et al., 2015a).
Another approach is first to invert ECa into estimates of true
electrical conductivity (r, mS/m) at depths of interest; whereby
r can be directly related to ECe at the same depths. Hendrickx
et al. (2002) used Tikhonov regularization to invert EM38 ECa
at different depths and to model salinity at individual sites. Li
et al. (2013) used a similar 1-d inversion at each site prior to
mapping ECe. To calibrate r and ECe using a single equation,
Goff et al. (2014) and Huang et al. (2015b) established
calibrations by inverting ECa from a single-frequency and
multiple-coil DUALEM-421 to predict salinity across
estuarine–alluvial and semi-arid gilgai clay plains, respectively.
More recently, using a quasi-3d inversion model, Davies et al.
(2015) showed how saline wedges in a swash-zone (i.e. upper
part of the beach between backbeach and surf zone) can be
discerned with Zare et al. (2015) and Huang et al. (2017a)
mapping ECe in 3-d across, respectively, irrigated and dryland
fields affected by salinity. The aim of our study was to map the
variability of ECe across an irrigated alluvial plain in the Tadla
irrigation district of central Morocco. We approached this by
producing a reconnaissance set of EM38 ECa acquired across
an area of 2000 ha. Using a quasi-3d algorithm we established
a single LR equation between r and ECe and mapped ECe for
any depth within the electromagnetic conductivity image
(EMCI) generated. We compared accuracy, bias and Lin’s
concordance with that of LR relationships established between
EM38 ECa and ECe at three depth increments (i.e. 0–0.3, 0.3–0.6 and 0.6–0.9 m). We evaluated the model uncertainty for the
inversion-based approach, and identified where improvements
in mapping might be achieved. We have discussed the results in
terms of the causes of the minor soil salinization relative to
various soil properties and briefly the implications in terms of
appropriate soil management to mitigate the problem.
Materials and methods
Study area
The study area, located ~260 km south-east of Rabat in the
Tadla irrigation district (Figure 1), consisted of two
subdistricts straddling the Oum Rabia River, including the
Beni Amir to the north and Beni Moussa to the south. The
study area was located in the northern part of the Ben Amir in
a semi-arid zone, with an annual rainfall ~250 mm, varying
between a minimum of 140 and maximum of 400 mm. The
potential evaporation was large (1800 mm). Daily temperature
(18 °C annual average) varied between a maximum of 40 °Cin August (summer) and a minimum of 3 °C in January
(winter). The study area covered 2066 hectares with the
predominant crops being wheat (40%), alfalfa (34%), olive
groves (15%), vegetables (7%) and sugar beet (4%).
The soil was predominantly of two different types
(Figure 2a). The largest area was an Isohumic (Chernozem,
FAO 2006) soil, containing medium brown subtropical soils,
saline and saline-sodic brown subtropical soils and medium
chestnut soils. This soil class, characterized by clayey and
clayey-silt textures, is deep to moderately deep with
limestone present throughout. The other class was the
Fersialitic (Chromic Luvisol, FAO 2006). A small part of the
area was characterized by calcimagnesic soil, which includes
brown limestones and ‘rendziniform’ soils. These highly
calcareous soils are generally shallow (i.e. 0.2–0.4 m).
There are two sources of water for irrigation: the primary
source of water originates from the El-Hansali dam (storage
capacity 800 million m3) and reaches the study area by canals
from the river Oum Rabia. Water quality is fair (Dakak, 2015)
with an average electrical conductivity (ECw) of 1.9 dS/m)
having slight to moderate (0.7–3.0 dS/m) degree of restriction
(Ayers & Westcot, 1985). Irrigation is gravity fed. Groundwater
also augments river water. In the northern area, ECw varies
from 0.73 to 3.8 dS/m and between 0.64 and 4.87 dS/m in the
southern half. Values of ECw >3.0 dS/m may be problematic.
EM38 data collection and interpolation
The ECa data were acquired using an EM38 instrument
(Geonics Limited, Mississauga, ON, Canada). The instrument
operates at a low frequency (14.8 kHz). The transmitter (Tx) is
located at one end with the receiver located at the other end and
© 2017 British Society of Soil Science, Soil Use and Management
2 H. Dakak et al.
1 m away. Depth of exploration (DOE) of ECa by EM38 is a
function of coil spacing (s) and array orientation. When placed
on the ground, DOE for an EM38 is 0.59s and 1.69s for
measurements made in the vertical (EM38v) and horizontal
(EM38h) positions, respectively. DOE, determined as the depth
of an array, accumulates 70% of its total sensitivity when
operating at low induction numbers (LIN) (McNeill, 1980).
The EM38 survey involved traversing the area in a
pseudo-regular grid, where sites were spaced 500 9 500 m
apart. The EM38 was placed on the ground. A total of 92
ECa measurement locations were visited (Figure 2b) with the
horizontal and vertical modes and with the data
geo-referenced in latitude and longitude using a Garmin
Dakota 20 Global Positioning System (GPS) (Garmin Ltd.,
Kansas, USA), with an accuracy of <10 m.
To understand the spatial variability of ECa across the
whole area, we fitted two variograms for EM38h and EM38v
using the residual maximum-likelihood (REML) method.
This was because when the sample size is small, REML has
been reported as a more robust approach than the
traditional ‘method-of-moments’ approach (Lark & Cullis,
2004; Lark et al., 2006; Kerry & Oliver, 2007). The fitting
was carried out using the ‘likfit’ function of the geoR
package (Ribeiro & Diggle, 2001) in R software (R Core
Team, 2016).
Once the variograms were calculated, EM38h and EM38v
ECa values were kriged onto a 100 m grid across the study
area using ordinary kriging with a maximum neighbour of 5
points. This was carried out using the gstat package
(Pebesma, 2004) available in R software. We used 5 points
as the maximum neighbour for kriging because we only had
92 ECa measurements and we did not want to over smooth
the ECa data.
Soil sampling and laboratory analysis
To calibrate the calculated estimates of ECa, a total of 36
samples were collected from 12 locations. To cover the spatial
extent, the sampling points were taken on an ~1 km grid in
the east-west orientation and on transects spaced ~2 km apart
from north to south (Figure 2c). In addition, to account for
the maximum and minimum ECa and the range, sampling
7°10’W
32°40’N
32°30’N
32°20’N
32°10’N
32°40’N
32°30’N
32°20’N
32°10’N
7°W 6°50’W 6°40’W 6°30’W 6°20’W 6°10’W
7°10’W 7°W 6°50’W 6°40’W 6°30’W 6°20’W 6°10’W
Situation map of the study area
Study zone
Tadla irrigated perimeter
Figure 1 Location of the study area in Beni Amir section of the Tadla Irrigation District in central Morocco.
© 2017 British Society of Soil Science, Soil Use and Management
3D soil salinity mapping in Morocco 3
Northing (m)217 000
N
(a)
214 000
212 000
210 000
208 000
Northing (m)217 000
214 000
212 000
210 000
208 000
370 000 372 000
Easting (m) Easting (m)
375 000 370 000 372 000 375 000
370 000
Boundary ofstudy area
Boundary ofstudy area
Fersialitic
Undefined
Complexes
Isohumic
Calcimagnesic
Boundary ofstudy area
EM38 surveylocation
500 m
372 000
Easting (m) Easting (m)
375 000 370 000 372 000 375 000
Elevation (m)
<420
<425
<430
<435
≥435
N
N N
90
72
6084
53 46
31 10
2115
Soil samplinglocation
28
49
500 m
500 m 500 m
(b)
(c) (d)
Figure 2 (a) Map of soil types in the Ben Amir section of the Tadla Irrigation District and spatial distributions of the (b) EM38 survey
locations, (c) soil sampling points and (d) contour plot of elevation (m).
© 2017 British Society of Soil Science, Soil Use and Management
4 H. Dakak et al.
points representative of every 10 percentile in the distribution
of ECa were selected.
At each of the 12 locations, soil samples were collected at
three depths, including the topsoil (0–0.3 m), subsurface
(0.3–0.6 m) and subsoil (0.6–0.9 m). The samples were
oven-dried and ground to pass through a 2-mm sieve. A
saturated soil-paste extract was prepared, and the electrical
conductivity of the soil-paste extract (ECe) was measured
using an Orion (Model 162) conductivity instrument
(Thermo Electron Corporation, Beverly, MA, USA).
Quasi-3d inversion of EM38 data
The EM4Soil V2.02 software package (EMTOMO, 2014)
was used to invert the EM38 ECa data. EM4Soil is able to
invert 1-dimensional (1d), 2d and 3d ECa data. The quasi-3d
(q-3d) inversion was used. It assumes that below each
measured location, 1-dimensional variation of calculated soil
conductivity (r, mS/m) is constrained by variation under
neighbouring locations.
The inversion algorithm is based on Occam’s
regularization (Sasaki, 1989), with the best q-3d model of r,compared to measured soil ECe. This was achieved by
varying parameters used by EM4Soil, including; choice of
forward modelling (cumulative function [CF] and full
solution [FS]), choice of inversion algorithm (S1 or S2) and
the damping factor (k) (Triantafilis & Monteiro Santos,
2013; Triantafilis et al., 2013a,b).
With respect to the inversion algorithm to be used, there is a
choice of S1 and S2; whereby S2 (see Sasaki, 2001) has more
constraints than S1 (see Sasaki, 1989) and therefore would
produce smoother results than S1. Under low induction number
conditions, the CF model is based on the ECa cumulative
response and is used to convert depth-profile r to ECa (McNeill,
1980). FS considers the Maxwell equations of the propagation
of EM fields (Kaufman & Keller, 1983) and is not limited to the
low induction number condition (i.e. conductivity <100 mS/m).
As a result the FS can improve models calculated from ECa data
acquired over highly conductive soil.
Here, the quasi-3d inversion was carried out on the 92 ECa
measurements and on the kriged ECa locations across the
whole study area. A three-layer model with depths of layers of
0.3, 0.6 and 0.9 m and initial r of 100 mS/m was used to
estimate r, with k set from 0.07, 0.3, 0.6 and at 0.6 increments
thereafter to a maximum value of 3.0. In this study, 20
iterations were used to determine the best inversion
parameters, based on our previous experience. Generally, too
few iterations produce inversion results with abrupt changes in
r with depth while too many iterations are time-consuming.
A comparison was made between estimated r and ECe at
all depths considering the coefficient of determination (R2).
The set of parameters (CF or FS, S1 or S2 and k), whichgave the largest R2, was used to establish a linear regression
(LR) between r and ECe. Note that the process of varying
inversion parameters is equivalent to the parameters
optimization or tuning in machine learning algorithms such
as the artificial neural network (Taghizadeh-Mehrjardi et al.,
2015), support vector machine (Ji et al., 2014) and ensemble
Kalman filter (Huang et al., 2017b).
Validation and comparison with LR established using ECa
Once the optimal inversion parameters were determined, we
used the corresponding set of r generated by inverting 92 ECa
points to produce a linear regression (LR) model to predict
ECe at different depths. The LR model was also applied to the
set of r generated by inverting the kriged ECa points to obtain
the predicted ECe across the whole study area and on a 100 m
grid. To determine the robustness of the LR, a cross-
validation procedure was conducted. Here, a single sample
point was removed, and a calibration was developed from the
remaining points (i.e. 11). This leave-one-out cross-validation
(LOOCV) procedure was carried out 12 times with each one of
the sampling points excluded in turn.
This approach was compared with a traditional LR
established directly between ECe and the raw ECa data following
Yao & Yang (2010). Here, we fitted three LR models for
predicting ECe at each depth, including the topsoil (0–0.3 m),
subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m), but using only
the most highly correlated EM38 ECa. The same LOOCV
procedure was used to evaluate the LR model predictions.
The accuracy of the predictions was assessed using the root
mean square error (RMSE) of prediction; whereby the closer
the value to zero the more accurate the prediction. Prediction
bias was calculated using the mean error (ME). Again the
closer to zero then the less biased is the prediction. The Lin’s
concordance correlation coefficient (qc) was calculated to
determine how close the model was to the 1:1 relationship for
the various depth increments (i.e. 0–0.3, 0.3–0.6 m and 0.6–0.9 m). We chose this because the Lin’s coefficient (Lin, 1989)
measures agreement between two variables (here the measured
and predicted ECe) and was calculated:
qc ¼2SXY
S2X þ S2Y þ ð�X � �YÞ2 ð1Þ
where �X and �Y are the means for the two variables, S2X and
S2Y are the corresponding variances, and SXY is the
covariance between the two variables:
SXY ¼ 1n
Xn
i�1Xi � �Xð Þ ðYi � �YÞ ð2Þ
Evaluation of the model uncertainty
To evaluate the model uncertainty for the inversion-based
approach, we used a LOOCV approach. That was, for each
of the 12 iterations of LOOCV process introduced in the
© 2017 British Society of Soil Science, Soil Use and Management
3D soil salinity mapping in Morocco 5
previous section, we predicted soil ECe onto the 100 m grid
using the LR model established with the 11 soil samples.
This process was carried out 12 times. The standard
deviation of the predicted soil ECe was calculated as the
model uncertainty.
Results and discussion
Preliminary data analysis: soil properties
Table 1 shows the average soil properties of the soil samples
obtained from the calibration sites. It is evident that the
Fersialitic profiles in the north had slightly alkaline pH
(~7.5) with increasing bulk density and what appeared to be
a texture change at a depth of 0.3 m; whereby clay increased
from the topsoil (28.1%) to subsurface (57.6%). By
comparison, the Isohumic profiles in the south had a more
alkaline pH (~8.45), which was a function of the large
amounts of calcium carbonate present, particularly in the
subsoil (i.e. 25.35%). The clay was uniform throughout
subsoil (33.41%) clay almost half of that present in the
north.
Table 2 shows that the mean EM38h ECa (87.6 mS/m)
was only slightly smaller than the average EM38v (88.4 mS/
m). This was also the case with the maximum values (i.e.
130.1 and 132.2 mS/m, respectively) with the minimum
values reversed (39.2 and 33.1 mS/m, respectively). Given
that the median for the EM38h (89.3 mS/m) and EM38v
(91.5 mS/m) was equivalent to the mean, we conclude the
EM38h and EM38v data were normally distributed. This
was reinforced by the small skewness in the EM38h (�0.4)
and EM38v (�0.8).
Table 2 also shows the summary statistics for the ECa
measured at the 12 calibration points shown in Figure 2c.
The mean EM38h (82.9 mS/m) was slightly larger than
EM38v (81.2 mS/m). This was also the case with the
maximum (i.e. 130.1 and 113.1 mS/m, respectively), with the
minimum values showing the EM38h (41.1 mS/m) was
slightly larger compared with the EM38v (33.1 mS/m). The
median values were similar with the mean with the EM38h
(81.9 mS/m), while the median of EM38v (84.3 mS/m) was
slightly larger than the mean. Again the data were
approximately normally distributed. We surmise there was a
good agreement between the sample sites selected for
calibration and the survey data.
Table 3a shows the mean ECe of the topsoil (0–0.3 m)
samples was largest (4.2 dS/m), with the largest individual
ECe (6.5 dS/m) measured in the topsoil. The salinity in the
topsoil was on average nominally of moderate (4–8 dS/m)
salinity but the largest value suggested that it did not
approach concentrations where germination might be
problematic in legumes (i.e. 2 dS/m) and some wheat
cultivars (i.e. 6 dS/m). The average ECe decreased ever so
slightly with depth with the smallest being the subsoil
(4.0 dS/m) where the minimum ECe (1.6 dS/m) was also
recorded. At these levels, which would be considered
nonsaline (<2 dS/m), most arable crops including legumes
would not be susceptible to any perceptible problems with
salinity. The decreasing ECe with depth was consistent with
the slightly larger EM38h compared with EM38v measured
at the calibration points, because DOEs were 0.75 and 1.5 m
for EM38h and EM38v, respectively.
Table 1 Soil properties for the two soil types at three depths
Soil property
Soil type
Fertialitic Isohumic
Soil depth (m) 0–0.3 0.3–0.6 0.6–0.9 0–0.3 0.3–0.6 0.6–0.9
Bulk density
(g/cm3)
1.3 1.43 1.76 1.2 1.37 1.46
Sand content
(%)
36.3 30.7 26.8 22.7 22.7 21.2
Silt content
(%)
35.6 26.9 27.9 48.3 46.82 45.4
Clay content
(%)
28.1 57.6 54.7 29.1 30.5 33.4
pH 7.6 7.4 7.6 8.4 8.4 8.5
Organic
carbon (%)
2.2 1.4 0.4 2.3 1.3 0.3
Calcium
carbonates
(%)
1.6 1.5 1.6 16.5 21.1 25.4
N-NO3
content (g/t)
22.6 15.7 11.9 19.4 16.9 14.0
Field capacity
(m3/m3)
14.8 12.2 10.9 14.1 15.0 16.6
Wilting
point (m3/m3)
18.7 19.6 20.7 22.9 23.5 21.7
Table 2 Summary statistics of apparent electrical conductivity (ECa
– mS/m) measured by an EM38 for the entire survey area and at the
12 calibration sites
N Min Mean Median Max Skewness CV
Survey data
EM38h
(mS/m)
92 39.2 87.6 89.3 130.1 �0.4 23.2%
EM38v
(mS/m)
92 33.1 88.4 91.5 132.2 �0.8 23.8%
Calibration data
EM38h
(mS/m)
12 41.1 82.9 81.9 130.1 0.22 27.0%
EM38v
(mS/m)
12 33.1 81.2 84.3 113.1 �0.9 25.6%
© 2017 British Society of Soil Science, Soil Use and Management
6 H. Dakak et al.
The constant trend in ECe with depth indicated on
average the salinity profiles was uniform at the time of
sampling. Whereas the levels of salinity varied from
nonsaline, to slightly saline and moderately saline, the
uniformity suggested accumulation of salts was occurring as
a function of irrigation with poorer quality water and that
the leaching fraction was insufficient to facilitate the removal
of the salts added.
Table 3b also shows the correlation coefficient (r) between
soil ECe and the EM38 ECa. In general, all ECa
measurements were statistically correlated with soil ECe
(P < 0.001). The most statistically significant (P < 0.001) and
largest correlation was between the EM38h ECa and topsoil
(r = 0.92) and subsurface (r = 0.93) ECe. The EM38v ECa
was statistically significant (P < 0.0001) with subsoil (0.91)
ECe.
Preliminary data analysis: ancillary data
A contour plot of elevation shows the land surface was
higher (i.e. >435 m) at the northern end with elevation
generally decreasing gradually towards the south (<420 m)
(Figure 2d). The spatial distribution of kriged EM38h shows
that small (<60 mS/m) and intermediate-small values (60–75 mS/m) of ECa characterized the southern end (Figure 3a).
At a few sites, ECa was similarly intermediate–small in the
central areas, where ECa was generally intermediate (70–90 mS/m). Intermediate–large (90–105 mS/m) and large
(>105 mS/m) values of ECa were generally found along the
western flank and north-eastern corner of the study area.
Spatial distribution of kriged EM38v shows that patterns of
ECa values were similar to those for EM38h (Figure 3b).
The spherical model parameters of the variograms, fitted
for EM38h and EM38v (Table 4), were selected according to
the log-likelihood values (�399.7 and �404.8, respectively).
The EM38h had a smaller nugget:sill ratio (74.3%)
compared with EM38v (84.7%). Within the range 1627 and
2000 m for EM38h and EM38v, respectively, the spatial
dependence values for horizontal and vertical configurations
were not strong and more ECa data need to be collected to
characterize the short-scale variation.
Figure 3c,d show the kriging variance for EM38h and
EM38v, respectively. The patterns were similar. Here, large
error occurred in the central and north-eastern margins of
the study area, where no samples were collected. However,
EM38h had smaller kriging variance (460–490 mS2/m2)
compared with EM38v (380–440 mS2/m2). This was
consistent with the larger nugget:sill ratio of EM38v.
Determination of optimal quasi-3d inversion parameters
The coefficient of determination (R2) between the calculated
true electrical conductivity (r) from the q-3d inversion and
ECe at all depths is shown in Figure 4a and when different
sets of inversion parameters were considered. This includes
forward modelling (i.e. CF and FS) and inversion (i.e. S1
and S2) algorithms versus k.The S2 performs better than the S1 given the consistently
larger R2 produced. In addition, the CF for the S2 algorithm
produces slightly larger R2 than S1. The best R2 (0.87) was
achieved when the S2 was used in concert with the CF and
when the k value of 3.0. As an increase in k after 3.0 did not
significantly increase R2 (data not shown), we therefore
selected these parameters to estimate r from a linear
regression relationship between r and ECe and to map the
latter across the field at various 0.3 m depth increments.
Model fitting was conducted using the bivariate fit tool
(JMP software, SAS Institute Inc., 2014). Figure 4b shows
the fitted lines and confidence curves (confidence limits for
the mean value) and individual confidence curves (confidence
limits for individual predicted values) between ECe and r.The significant level (a) was set to 0.05. As shown in
Table 5, the regression equation was ECe = 0.679 +0.041 9 r. Given the large coefficient of determination
Table 3 (a) Summary statistics of measured electrical conductivity of
the saturated soil-paste extract (ECe – dS/m) at the calibration sites,
(b) correlation coefficient (r) between soil ECe and the measured
apparent electrical conductivity (ECa) of each of the EM38 arrays
and (c) the calibration linear regression models with their coefficient
of determination (R2) used to predict ECe from the EM38 which was
most highly correlated with ECe and for each depth increment.
Note: *, P < 0.05; **, P < 0.001; ***, P < 0.0001
(a)
N Min Mean Median Max Skewness CV
ECe
(0–0.3 m)
12 2.3 4.2 4.3 6.5 0.4 26.7%
ECe
(0.3–0.6 m)
12 2.1 4.1 4.1 6.2 0.2 24.5%
ECe
(0.6–0.9 m)
12 1.6 4.0 4.2 5.6 �0.8 26.7%
(b)
r EM38h EM38v
ECe (0–0.3 m) 0.92*** 0.78*
ECe (0.3–0.6 m) 0.93*** 0.84**
ECe (0.6–0.9 m) 0.90** 0.91**
(c)
Equation R2
ECe (0–0.3 m) 0.4680 + 0.0447 9 EM38h 0.85***
ECe (0.3–0.6 m) 0.7153 + 0.0404 9 EM38h 0.87***
ECe (0.6–0.9 m) 0.3360 + 0.0449 9 EM38v 0.82**
© 2017 British Society of Soil Science, Soil Use and Management
3D soil salinity mapping in Morocco 7
Northing (m)217 000
N
214 000
212 000
210 000
208 000
500 m
370 000 372 000
Easting (m) Easting (m)
375 000 370 000 372 000 375 000
Northing (m)217 000
214 000
212 000
210 000
208 000
370 000 372 000
Easting (m) Easting (m)
375 000 370 000 372 000 375 000
N
N N
EM38hECa (mS/m)
<60
<75
<90
<105
≥105
EM38vECa (mS/m)
<60
<75
<90
<105
≥105
Kriging variance(mS/m)2
<460
<470
<480
<490
≥490
EM38v
Kriging variance(mS/m)2
<380
<400
<420
<440
≥440
EM38h
500 m
500 m 500 m
(a) (b)
(c) (d)
Figure 3 Contour plot of kriged apparent electrical conductivity (ECa, mS/m) collected using an EM38 in the (a) horizontal (EM38h) and
(b) vertical (EM38v) modes and the kriging variance (mS2s/m2) of (c) EM38h and (d) EM38v across the Ben Amir section of the Tadla
Irrigation District. Note: EM38 survey points were marked in black dots.
© 2017 British Society of Soil Science, Soil Use and Management
8 H. Dakak et al.
(i.e. R2 = 0.87), the use of r should yield good estimates of
ECe at all depths from this single equation.
Table 3c shows the summary statistics of the LR models
established between the raw EM38 ECa. It was evident that
the best coefficient of determination (R2 = 0.85) was
achieved for the topsoil (0–0.3 m) and using the EM38h.
This was also the case for the subsurface (0.3–0.6 m) ECe
where the correlation was a little stronger (R2 = 0.87).
Subsoil (0.6–0.9 m) ECe was best predicted using only the
EM38v ECa (R2 = 0.82), however.
Evaluation of soil salinity prediction
A leave-one-out cross-validation (LOOCV) approach was
applied on each calibration location. In the first instance, we
describe the results achieved using the q-3d calibration
approach and to predict ECe from r. Figure 4c shows that
predicted ECe at each location was in general accurate and
unbiased given the RMSE (0.42 dS/m) and small ME (-
0.02 dS/m), respectively. This was also reflected in the Lin’s
concordance (0.91), which was very large and which for the
most part showed excellent agreement between measured and
predicted ECe.
Figure 4d shows similar results from the LOOCV obtained
by establishing a LR relationship at each depth. The Lin’s
concordance was the same (0.89), RMSE higher (0.45 dS/m)
with predictions unbiased (0.00 dS/m) overall. One of the
differences between the methods was the q-3d modelling
approach better accounted for and predicted the largest
measured ECe and produced predictions with short ranges
where ECe was near the average (i.e. 4 dS/m).
In addition, the LR approach requires three models to be
established and cannot predict soil ECe at the depths below
0.75 m. This was not the case for the inversion approach as
it was designed to predict soil ECe at any depths within the
DOE of the EM38 (~1.5 m) without using any depth
functions for interpolation (Malone et al., 2009).
Nevertheless, the results achieved with the q-3d modelling
were comparable to recent investigations conducted at the
field level using more densely collected ECa data. For
example, at the field scale and in a highly saline area of
Bourke in northwest New South Wales, Australia, Zare
et al. (2015) achieved a larger Lin’s concordance (0.93)
between measured and predicted ECe; however, their q-3d
model was based on DUALEM-421 data collected along
transects spaced 50 m apart. Moghadas et al. (2016) carried
out an equivalent modelling approach but across a much
larger area with EM38 data across the Ardakan area of
central Iran. They achieved progressively smaller Lin’s
concordance with increasing depth from the topsoil (0.75) to
the subsurface (0.35). However, the reduced ability to predict
ECe was a function of the EM38 survey spacing with the
requirement to collect EM38 data on a closely spaced and
denser survey grid of <500 m.
Comparison between measured and predicted ECe profiles
To better understand the predicted ECe achieved using the q-
3d modelling approach and against the measured ECe,
Figure 5a shows the measured ECe for each of the calibration
sites with predicted ECe shown in Figure 5b. With respect to
sampling site 10, the measured ECe (2.3 dS/m) in the topsoil
was only slightly saline (2–4 dS/m) and decreased to
nonsaline levels in the subsurface (2.1 dS/m) and subsoil
(1.6 dS/m). In general, the predicted ECe at this site was
satisfactory for subsurface equivalent to measured ECe
(2.2 dS/m), with topsoil (2.7 dS/m) and subsoil (1.9 dS/m)
ECe slightly overestimated.
With respect to the slightly saline measured ECe profiles
(i.e. sites 15, 21, 49, 53 and 72) the predicted ECe was also
satisfactory. Issues arise in prediction where sites were
located near areas where ECa changes rapidly. This was the
case around site 53, where predicted subsoil ECe (4.3 dS/m)
was slightly saline, which was much greater than measured
(3.4 dS/m) ECe that was nonsaline. Figure 3b shows this
clearly and specifically where EM38v ECa increased from
intermediate (75–90 mS/m) to large (>105 mS/m) values over
a distance of <500 m either side of site 53. This was the scale
of the ECa surveying interval.
With regard to the moderately saline profiles (i.e. sites 28,
46, 60, 84 and 90), similarly good predictions were also
achieved. There was, however, an issue with underestimation
of topsoil ECe (3.9 dS/m) at site 90 which was predicted to
be slightly saline when it was moderately saline (4.6 dS/m).
Here, as with the predictions at site 53, the prediction error
was a function of site 90 being close to an area of rapid
change in ECa. This was evidenced in Figure 3b in the
northeast corner of the study area.
In terms of the most saline and inverted salinity profile of
site 31, the predicted ECe was generally satisfactorily
resolved in the topsoil and subsoil depths. However, the
measured subsurface ECe (6.2 dS/m) was slightly
under-predicted (5.7 dS/m) in terms of the salinity class.
Again, as with the other sites cited and for that matter most
Table 4 Summary statistics of the variograms fitted for apparent
electrical conductivity (ECa) data measured by the EM38 in the
horizontal (EM38h) and vertical (EM38v) modes using residual
maximum-likelihood method
Mode EM38h EM38v
Variogram model Spherical Spherical
Log-likelihood �399.7 �404.8
Nugget 302 375.4
Partial sill 104.6 68.0
Nugget/sill 74.3% 84.7%
Range (m) 1627 2000
© 2017 British Society of Soil Science, Soil Use and Management
3D soil salinity mapping in Morocco 9
sites where errors in salinity classification occurred, the
short-range spatial variation at less than the survey interval
of 500 m was problematic.
Another reason for the slight errors in prediction was a
function of the 100 m grid, which the ECa data needed to be
gridded onto to enable the q-3d inversion of the ECa data.
R2
0.90
0.88
0.86
0.84
0.82
0.800.07
Predicted ECe (dS/m) Predicted ECe (dS/m)
Calculated true electrical conductivity ( ) (mS/m)σ
Lin’s concordance = 0.91
RMSE = 0.45 dS/mME = 0.00 dS/mLin’s concordance = 0.89
ME = –0.02 dS/mRMSE = 0.42 dS/m
0.5 1.0 1.5 2.0 2.5 3.0
8Measured ECe (dS/m)
confidence curves fit
R 2 = 0.872confidence curves individual
fitted line
6
4
2
0
8
6
4
2
00 2 4 6 8 0 2 4 6 8
8
6
4
2
0
0 30 60 90 120 150
0.6 - 0.9 m
0.3 - 0.6 m0.0 - 0.3 m
0.6 - 0.9 m
Measured ECe (dS/m)
0.3 - 0.6 m0.0 - 0.3 m
0.6 - 0.9 m
Measured ECe (dS/m)
0.6 - 0.9 m: ECe = 0.336 + 0.045 × EM38v0.3 - 0.6 m: ECe = 0.715 + 0.040 × EM38h0.0 - 0.3 m: ECe = 0.468 + 0.045 × EM38h
0.3 - 0.6 m0.0 - 0.3 m
S1, FSS1, CF
S2, FS
λ
S2, CF
ECe = 0.679 + 0.041 × σ
ECe = 0.679 + 0.041 × σ
(a) (b)
(c) (d)
Figure 4 Plot of (a) coefficient of determination (R2) achieved between the electrical conductivity of the saturated soil-paste extract (ECe – dS/m)
and calculated true electrical conductivity (r, mS/m) generated by inverting EM38 ECa using EM4Soil quasi-3d (q-3d) inversion algorithm,
cumulative function (CF) and full solution (FS), inversion algorithm S1 or S2 versus damping factor (k), (b) measured ECe versus r when CF,
S2 were used with the k value of 3.0, and measured ECe versus predicted ECe generated by leave-one-out cross-validation at the calibration
points using (c) quasi-3d (q-3d) inversion model of the EM38 ECa (mS/m) and (d) linear regression of individual EM38 ECa at different depth
intervals.
© 2017 British Society of Soil Science, Soil Use and Management
10 H. Dakak et al.
In this regard, the collection of ECa data on a 100 m grid
would lead to less smoothing. This was not a problem with
the LR method, because the EM38 ECa data collected above
each calibration site was directly correlated with ECe at each
depth. To improve the results in q-3d modelling, we
recommend in future research that EM38 ECa data should
be collected at either 250 or 100 m apart or smaller, because
the gridded data would be less smoothed.
Interpreting ECe maps at the three depths
Figure 6 shows predicted ECe spatially distributed using the
relationship established between r and ECe with the q-3d
inversion. With respect to topsoil ECe, Figure 6a shows that
there were only a few isolated areas where soil ECe was
nonsaline (<2 dS/m). These occurred in the southern part of
the study area, associated with the sands of Isohumic soil
profiles in the lower landscape positions. Interestingly, ECe
gradually increased from slightly saline (2–4 dS/m) to
moderately saline (4–8 dS/m) in areas immediately to the
north and similarly characterized by Isohumic profiles. In
the areas to the north, soil became mostly slightly saline (2–4
dS/m) as characterised by Fersialitic profiles.
Figure 6b,c show the spatial distribution of predicted ECe
at depths of 0.3–0.6 and 0.6–0.9 m, respectively. For the
most part, the patterns in ECe distribution were equivalent.
We note, however, that the areas where predicted salinity in
subsurface and subsoil ECe was smaller in terms of being
moderately saline. This was particularly the case in the
contiguous area in the central southern part of the study
area characterized by the Isohumic profiles. Conversely, the
area to the north as characterized by the Fersialitic profiles
was predicted to have larger and moderately saline ECe (i.e.
>4 dS/m) in the subsoil in particular.
The reasons for the differences in ECe appear to be a
function of the ECw and the soil properties. In the south-
eastern half, the generally smaller ECe was most likely a
function of the uniformly siltier nature of the soil (~46%)
Table 5 Summary statistics of the linear regression model established between calculated true electrical conductivity (r) and measured electrical
conductivity of the saturated soil-paste extract (ECe)
Parameter estimates Estimate Standard error t Ratio Probability>|t| Coefficient of determination (R2)
Intercept 0.679 0.232 2.93 0.0060 0.87
r 0.041 0.003 15.23 <0.0001
Depth (m)
10 5321 7215 90604649
10 532172 15 9060 4649
Depth (m)
0(a)
0.3
0.6
0.90 1 2 3 4
2884
2884
Measured ECe (dS/m)
Predicted ECe (dS/m)
5 6
31
7 8
0 1 2 3 4 5 6 7 8
0
0.3
0.6
0.9
31(b)
Figure 5 Plot of (a) measured and (b)
predicted electrical conductivity of the
saturated soil-paste extract (ECe – dS/m)
with depth (m) at the soil sampling locations
generated using quasi-3d (q-3d) inversion
model of the EM38 ECa (mS/m), cumulative
function (CF) forward modelling and S2
inversion algorithm with a damping factor
(k) of 3.0.
© 2017 British Society of Soil Science, Soil Use and Management
3D soil salinity mapping in Morocco 11
and smaller bulk density (e.g. average subsoil = 1.46 g/cm3)
of the Isohumic profiles. This was despite the generally
larger ECw of ground water (i.e. 0.64 and 4.87 dS/m), which
in some cases seemed to produce moderately saline
conditions in the central and western parts of the area.
Conversely, in the areas associated with the Fersialitic
profiles to the north, whilst the ECw was slightly smaller
(0.73–3.8 dS/m) the larger subsurface (57.60%) and subsoil
(54.70%) clay and bulk density appeared to favour the
accumulation of more salts, particularly in the subsoil.
Overall, the maps of predicted ECe were generally in
accord with the measured ECe profiles (Figure 6a). The
unity of the results was therefore informative in terms of
soil use and management and as a function of the salinity
tolerance of the main agricultural crops used within the
Tadla irrigation district. In terms of olives (Olea sylvestris)
which are grown in 15% of the area, the ECe at current
levels is generally satisfactory given olives are moderately
tolerant; albeit they prefer conditions where ECe was
<4.5 dS/m.
With respect to wheat (T. turgidum L. var. durumDesf.),
which is commonly grown across 40% of the area, the levels
of ECe currently prevailing should not be overly problematic
given the threshold ECe is 5.9 dS/m (Francois et al., 1986).
This was because this was larger than most predicted topsoil
ECe; albeit that at site 31 and surrounds this may not be the
case given measured ECe (6.5 dS/m) exceeded this value
(Table 3a). However, this was not the case for alfalfa
(Medicagosativa L.), which is grown in 34% of the area and
usually in rotation with wheat. In this regard, the ECe
tolerance is 1/3 that of wheat and at 2 dS/m (Bower et al.,
1969; Bernstein & Francois, 1973) this value was exceeded
across most of the area and at all soil depths investigated.
This was therefore problematic given the role of alfalfa was
to provide carbon and nitrogen to the soil and for the
benefit of wheat cropping.
Evaluation of the model uncertainty
Figure 7 shows the standard deviation of predicted ECe at
different depths using the LOOCV approach. We note that
the model uncertainty illustrated by the standard deviation
was small (<1.2 9 10�8 dS/m). This was most likely due to
the limited soil samples (i.e. 12) used in the LOOCV
Northing (m)217 000
N
(a)
90
7260
84
46 49
31 28 10
1521
53
90
7260
84
46 49
31 28 10
1521
53
90
7260
84
46 49
31 28 10
1521
53
214 000
212 000
210 000
208 000
370 000
500 m
372 000 375 000
ECe (dS/m)<2
<3
<4
<5
≥5
ECe (dS/m)<2
<3
<4
<5
≥5
ECe (dS/m)<2
<3
<4
<5
≥5
370 000 372 000 375 000 370 000 372 000 375 000Easting (m) Easting (m) Easting (m)
500 m 500 m
N N
(b) (c)
Figure 6 Spatial distributions of predicted electrical conductivity of the saturated soil-paste extract (ECe–dS/m) at depths of (a) 0–0.3 (topsoil),
(b) 0.3–0.6 (subsurface) and (c) 0.6–0.9 m (subsoil) across the Ben Amir section of the Tadla Irrigation District using the quasi-3d (q-3d)
inversion algorithm with cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor (k) of 3.0.
© 2017 British Society of Soil Science, Soil Use and Management
12 H. Dakak et al.
approach, whereby all the iterations with the remaining 11
samples produced similar linear models. To better account
for the model uncertainty, a conditional simulation (Nelson
et al., 2011) could be carried out with more soil samples
collected.
Conclusions
A quasi-3d (q-3d) inversion algorithm was used to invert
kriged EM38 ECa data across a small part of the Beni Amir
irrigated district, in the Tadla area, central Morocco. The
true electrical conductivity (r) was strongly correlated with
ECe, when we used the q-3d and CF, S2 algorithm and
k = 3.0. We also achieved equivalent results by developing
individual LR equations at each depth increment with the
raw EM38 ECa. We conclude that the inversion approach
was more efficient as a single LR equation was needed and
to apply it to a quasi-3d electromagnetic conductivity image
(EMCI). This approach also allowed prediction of ECe at
not only the depths of sampling but at any depth within a
quasi-3d EMCI.
The final maps of the estimated ECe at various depths
allowed the causes and potential management of secondary
soil salinity to be understood and appropriate management
strategies to be initiated and with respect to the application of
poorer quality groundwater for irrigation. This was
particularly the case with respect to the larger amounts of ECe
accumulating in the northern part of the study area associated
with the Fersialitic profile, which was characterized by larger
subsoil clay and bulk density, and with Isohumic profiles in
the central and western parts of the study.
In either case, the methodology provides for baseline data,
which can be used to monitor the effect of continued use of
poorer quality groundwater for irrigation. The methods
developed also have application for extending the area
mapped to other areas where salinity is more problematic
and in areas where saline ground waters are known to exist
to the south and in other parts of the Tadla irrigation
district as well as in other parts of Morocco such as the
Skhirat region (Zouahri et al., 2015). The results also point
to where more detailed studies are needed and to better
understand the cause of the uniform and moderately saline
profiles. In this regard, the approach of Zare et al. (2015)
might be useful to measure, map, manage and monitor
salinity at the field scale and using q-3d inversion modelling
of DUALEM-421 ECa data.
Northing (m)
217 000
90
72
6084
46 49
31 28 10
15
ECe (dS/m)
<3×10–9
<6×10–9
<9×10–9
<1.2×10–8
≥1.2×10–8
<3×10–9
<6×10–9
<9×10–9
<1.2×10–8
≥1.2×10–8
<3×10–9
<6×10–9
<9×10–9
<1.2×10–8
≥1.2×10–8
21
500 m
53
90
72
6084
46 49
31 28 10
1521
53
90
72
6084
46 49
31 28 10
1521
53
214 000
212 000
210 000
208 000
370 000 372 000 375 000 370 000 372 000 375 000 370 000 372 000 375 000
Easting (m) Easting (m) Easting (m)
N N N
(a) (b) (c)
ECe (dS/m) ECe (dS/m)
500 m 500 m
Figure 7 Spatial distributions of the standard deviation of the predicted electrical conductivity of the saturated soil-paste extract (ECe–dS/m) at
depths of (a) 0–0.3 (topsoil), (b) 0.3–0.6 (subsurface) and (c) 0.6–0.9 m (subsoil) across the Ben Amir section of the Tadla Irrigation District
using the 12 iterations of leave-one-out cross-validation and the quasi-3d (q-3d) inversion results.
© 2017 British Society of Soil Science, Soil Use and Management
3D soil salinity mapping in Morocco 13
References
Akramkhanov, A., Brus, D.J. & Walvoort, D.J.J. 2014.
Geostatistical monitoring of soil salinity in Uzbekistan by
repeated EMI surveys. Geoderma, 213, 600–607.
Amezketa, E. & del Valle de Lersundi, J. 2008. Soil classification
and salinity mapping for determining restoration potential of
cropped riparian areas. Land Degradation Development, 19, 153–
164.
Arag€u�es, R., Urdanoz, V., Cetin, M., Kirda, C., Daghari, H., Ltifi,
W., Lahlou, M. & Douaik, A. 2011. Soil salinity related to
physical soil characteristics and irrigation management in four
Mediterranean irrigation districts. Agricultural Water
Management, 98, 959–966.
Ayers, R.S. & Westcot, D.W. 1985. Water for agriculture. FAO
Irrigation and Drainage Paper No. 29. Revision 1. Food and
Agriculture Organisation of the United Nations, Rome, 1985.
Badraoui, M., Esssafi, B., Soudi, B., Bouazzama, B. & Bouyahyaoui,
A. 2002. Impact de l’irrigation sur la qualit�e des sols et des eaux
dans le Tadla : Salinisation. Rapport de diagnostic: prospection,
mesures sur le terrain et analyses des sols et des eaux. Projet
PGRE, IAV Hassan II/ORMVAT/SEEN, Rabat, Maroc.
Barbi�ero, L., Cunnac, S., Man�e, L., Laperrousaz, C., Hammecker,
C. & Maeght, J.L. 2001. Salt distribution in the Senegal middle
valley analysis of a saline structure on planned irrigation schemes
from N’Galenka creek. Agricultural Water Management, 46, 201–
213.
Bernstein, L. & Francois, L.E. 1973. Leaching requirement studies:
sensitivity of alfalfa to salinity of irrigation and drainage waters.
Proceedings – Soil Science Society of America, 37, 931–943.
Bower, C.A., Ogata, G. & Tucker, J.M. 1969. Rootzone salt profiles
and alfalfa growth as influenced by irrigation water salinity and
leaching fraction. Agronomy Journal, 61, 783–785.
Cetin, M., Ibrikci, H., Kirda, C., Kaman, H., Karnez, E., Ryan, J.,
Topcu, S., Oztekin, M.E., Dingil, M. & Sesveren, S. 2012. Using
an electromagnetic sensor combined with geographic information
systems to monitor soil salinity in an area of southern Turkey
irrigated with drainage water. Fresenius Environmental Bulletin, 21,
1133–1145.
Ceuppens, J. & Wopereis, M.C.S. 1999. Impact of non-drained
irrigated rice cropping on soil salinization in the Senegal River
Delta. Geoderma, 92, 125–140.
Dakak, H. 2015. Diagnostic et controle de la salinit�e et des nitrates
des eaux et des sols du p�erim�etre irrigu�e de Tadla: �Elaboration du
bilan hydrologique et du bilan de masse d’azote - Exp�erimentation
et mod�elisation. PhD thesis, Ibn Tofail University, K�enitra,
Morocco.
Davies, G., Huang, J., Monteiro Santos, F.A. & Triantafilis, J. 2015.
Modeling coastal salinity in quasi 2D and 3D using a DUALEM-
421 and inversion software. Groundwater, 53, 424–431.
EMTOMO. 2014. EM4Soil Version 2. EMTOMO, R. Alice Cruz 4,
Odivelas, Lisboa, Portugal.
FAO. 2006. World reference base for soil resources, p. 128. FAO:
Roma, Italy. ISBN: 92-5-105511-4.
Francois, L.E., Maas, E.V., Donovan, T.J. & Youngs, V.L. 1986.
Effect of salinity on grain yield and quality, vegetative growth,
and germination of semi-dwarf and durum wheat. Agronomy
Journal, 78, 1053–1058.
Goff, A., Huang, J., Wong, V.N.L., Monteiro Santos, F.A., Wege,
R. & Triantafilis, J. 2014. Electromagnetic conductivity imaging of
soil salinity in an estuarine–alluvial landscape. Soil Science Society
of America Journal, 78, 1686–1693.
Guo, Y., Shi, Z., Li, H.Y. & Triantafilis, J. 2013. Application of
digital soil mapping methods for identifying salinity management
classes based on a study on coastal central China. Soil Use and
Management, 29, 445–456.
Hendrickx, J.M.H., Borchers, B., Corwin, D.L., Lesch, S.M.,
Hilgendorf, A.C. & Schlue, J. 2002. Inversion of soil conductivity
profiles from electromagnetic induction measurements. Soil
Science Society of America Journal, 66, 673–685.
Huang, J., Barrett-Lennard, E., Kilminster, T., Sinott, A. &
Triantafilis, J. 2015a. An error budget for mapping field-scale soil
salinity at various depths using different sources of ancillary data.
Soil Science Society of America Journal, 79, 1717–1728.
Huang, J., Mokhtari, A.R., Cohen, D.R., Monteiro Santos, F.A. &
Triantafilis, J. 2015b. Modelling soil salinity across a gilgai
landscape by inversion of EM38 and EM31 data. European
Journal of Soil Science, 66, 951–960.
Huang, J., Kilminster, T., Barrett-Lennard, E. G. & Triantafilis, J.
2017. Characterization of field-scale dryland salinity with depth by
quasi-3d inversion of DUALEM-1 data. Soil Use and
Management, in press. DOI:10.1111/sum.12345
Huang, J., Minasny, B., McBratney, A.B. & Triantafilis, J. 2017b.
Monitoring and modelling soil water dynamics using
electromagnetic conductivity imaging and the ensemble Kalman
filter. Geoderma, 285, 76–93.
Ji, W., Shi, Z., Huang, J. & Li, S. 2014. In situ measurement of
some soil properties in paddy soil using visible and near-infrared
spectroscopy. PLoS One, 9, e105708.
Johnston, M.A., Savage, M.J., Moolman, J.H. & Du Plessis, H.M.
1997. Evaluation of calibration methods for interpreting soil
salinity from electromagnetic induction measurements. Soil
Science Society of America Journal, 61, 1627–1633.
Kaufman, A.A. & Keller, G.V. 1983. Frequency and transient
soundings. Meth. Geochem. Geophys. 16. Elsevier, New York, NY.
Kerry, R. & Oliver, M.A. 2007. Comparing sampling needs for
variograms of soil properties computed by the method of
moments and residual maximum likelihood. Geoderma, 140, 383–
396.
Lark, R.M. & Cullis, B.R. 2004. Model-based analysis using REML
for inference from systematically sampled data on soil. European
Journal of Soil Science, 55, 799–813.
Lark, R.M., Cullis, B.R. & Welham, S.J. 2006. On spatial prediction
of soil properties in the presence of a spatial trend: the empirical
best linear unbiased predictor (E-BLUP) with REML. European
Journal of Soil Science, 57, 787–799.
Li, H.Y., Shi, Z., Webster, R. & Triantafilis, J. 2013. Mapping the
threedimensional variation of soil salinity in a rice-paddy soil.
Geoderma, 195–196, 31–41.
Lin, L.I.K. 1989. A concordance correlation coefficient to evaluate
reproducibility. Biometrics, 45, 255–268.
Malone, B., McBratney, A.B., Minasny, B. & Laslett, G. 2009.
Mapping continuous depth functions of soil carbon storage and
available water capacity. Geoderma, 154, 138–152.
MATUHE (Minist�ere de l’Am�enagement du Territoire, de
l’Urbanisme et de l’Environnement). 2001. Communication
© 2017 British Society of Soil Science, Soil Use and Management
14 H. Dakak et al.
nationale initiale �a la convention cadre des Nations Unies sur les
changements climatiques. Rabat, Maroc.
McLeod, M.K., Slavich, P.G., Irhas, Y., Moore, N., Rachman, A.,
Ali, N., Iskandarb, T., Hunta, C. & Caniago, C. 2010. Soil
salinity in Aceh after the December 2004 Indian Ocean tsunami.
Agricultural Water Management, 97, 605–613.
McNeill, J.D. 1980. Electrical conductivity of soils and rock. Geonics
Ltd., Mississauga, ON.
Moghadas, D., Taghizadeh-Mehrjardi, R. & Triantafilis, J. 2016.
Probabilistic inversion of EM38 data for 3D soil mapping in
central Iran. Geoderma Regional, 7, 230–238.
Nelson, M.A., Bishop, T.F.A., Triantafilis, J. & Odeh, I.O.A. 2011.
An error budget for different sources of error in digital soil
mapping. European Journal of Soil Science, 62, 417–430.
Odeh, I.O.A., Todd, A.J., Triantafilis, J. & McBratney, A.B. 1998.
Status and trends of soil salinity at different scales: the case of the
irrigated cotton growing region of eastern Australia. Nutrient
Cycling in Agroecosystems, 5, 99–107.
Pebesma, E.J. 2004. Multivariable geostatistics in S: the gstat
package. Computers and Geosciences, 30, 683–691.
R Core Team. 2016. R: a language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna,
Austria. URL: http.www.R-project.org.
Ribeiro, P.J. Jr & Diggle, P.J. 2001. geoR: a package for
geostatistical analysis. R News, 1, 14–18.
SAS Institute. 2014. JMP Version 10. SAS Institute Inc., Cary,
North Carolina, USA.
Sasaki, Y. 1989. Two-dimensional joint inversion of magnetotelluric
and dipole–dipole resistivity data. Geophysics, 54, 254–262.
Sasaki, Y. 2001. Full 3-D inversion of electromagnetic data on PC.
Journal of Applied Geophysics, 46, 45–54.
Taghizadeh-Mehrjardi, R., Nabiollahi, K., Minasny, B. &
Triantafilis, J. 2015. Comparing data mining classifiers to predict
spatial distribution of USDA-family soil groups in Baneh region,
Iran. Geoderma, 253, 67–77.
Triantafilis, J. & Buchanan, S.M. 2010. Mapping the spatial
distribution of saline sub-surface material in the Darling River
valley. Journal of Applied Geophysics, 70, 144–160.
Triantafilis, J. & Monteiro Santos, F.A. 2013. Electromagnetic
conductivity imaging (EMCI) of soil using a DUALEM-421 and
inversion modelling software (EM4Soil). Geoderma, 211–212, 28–38.
Triantafilis, J., Ribeiro, J., Page, D. & Monteiro Santos, F.A. 2013a.
Inferring the location of preferential flow paths of a leachate
plume using a DUALEM-421 and a quasi-three-dimensional
inversion model. Vadose Zone Journal, 12, DOI: 10.2136/vzj2012.
0086
Triantafilis, J., Terhune, C.H. & Monteiro Santos, F.A. 2013b. An
inversion approach to generate electromagnetic conductivity
images from signal data. Environment Modelling and Software, 43,
88–95.
Yao, R. & Yang, J. 2010. Quantitative evaluation of soil salinity and
its spatial distribution using electromagnetic induction method.
Agricultural Water Management, 97, 1961–1970.
Zare, E., Huang, J., Monteiro Santos, F.A. & Triantafilis, J. 2015.
Mapping salinity in three-dimensions using a DUALEM-421 and
electromagnetic inversion software. Soil Science Society of
America Journal, 79, 1729–1740.
Zouahri, A., Dakak, H., Douaik, A., El Khadir, M. & Moussadek,
R. 2015. Evaluation of groundwater suitability for irrigation in the
Skhirat region, Northwest of Morocco. Environmental Monitoring
and Assessment, 187, 4184.
© 2017 British Society of Soil Science, Soil Use and Management
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