HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-21-495-2017Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring networkMartiniEdoardoedoardo.martini@ufz.dehttps://orcid.org/0000-0002-4198-6422WerbanUlrikehttps://orcid.org/0000-0003-4700-5258ZachariasSteffenhttps://orcid.org/0000-0002-7825-0072PohleMarcoDietrichPeterWollschlägerUteDept. Monitoring and Exploration Technologies, UFZ – Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, GermanyCentre for Applied Geoscience, University of Tübingen, Hölderlinstraße 12, 72074 Tübingen, GermanyDept. Soil Physics, UFZ – Helmholtz Centre for Environmental Research, Theodor-Lieser-Straße 4, 06120 Halle (Saale), GermanyEdoardo Martini (edoardo.martini@ufz.de)26January201721149551325February20164March20166January20179January2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/21/495/2017/hess-21-495-2017.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/21/495/2017/hess-21-495-2017.pdf
Electromagnetic induction (EMI) measurements are widely used for soil
mapping, as they allow fast and relatively low-cost surveys of soil apparent
electrical conductivity (ECa). Although the use of non-invasive EMI for
imaging spatial soil properties is very attractive, the dependence of ECa on
several factors challenges any interpretation with respect to individual
soil properties or states such as soil moisture (θ). The major aim
of this study was to further investigate the potential of repeated EMI
measurements to map θ, with particular focus on the temporal
variability of the spatial patterns of ECa and θ. To this end, we
compared repeated EMI measurements with high-resolution θ data from
a wireless soil moisture and soil temperature monitoring network for an
extensively managed hillslope area for which soil properties and θ dynamics
are known. For the investigated site, (i) ECa showed small temporal
variations whereas θ varied from very dry to almost saturation,
(ii) temporal changes of the spatial pattern of ECa differed from those of the
spatial pattern of θ, and (iii) the ECa–θ relationship varied
with time. Results suggest that (i) depending upon site characteristics,
stable soil properties can be the major control of ECa measured with EMI,
and (ii) for soils with low clay content, the influence of θ on ECa
may be confounded by changes of the electrical conductivity of the soil
solution. Further, this study discusses the complex interplay between
factors controlling ECa and θ, and the use of EMI-based ECa data
with respect to hydrological applications.
Introduction
Electromagnetic induction (EMI) methods are widely used for soil mapping, as
they allow fast and relatively low-cost surveys of soil apparent electrical
conductivity (ECa) over areas up to several squared kilometers in size (McNeil, 1980).
The main strength of the EMI method is that the induction principle does not
require direct contact with the ground. Consequently, a survey carried out
using EMI sensors can be accomplished faster than an equivalent survey
carried out with other instruments. Normally, surveys can be performed by a
single operator, and a GPS receiver connected to the instrument allows
for collection of georeferenced ECa data. Measurements of ECa using EMI have been in use
since the 1970's, initially having been used for applications related to
soil salinity. Since then, various environmental questions have been
addressed using the EMI method, as discussed in the recent review of
Doolittle and Brevik (2014). Although the use of non-invasive geophysical
techniques for soil mapping is very attractive, the dependence of the
measured ECa on a number of parameters complicates any interpretation to
determine soil properties or states (Robinson et al., 2012). A firm
understanding of the spatial and temporal variability of soil electrical
conductivity (EC) and an appreciation for its highly complex interactions
with static soil properties and dynamic state variables, particularly at
low-salt concentrations, is needed (Sudduth et al., 2001,
2005; McCutcheon et al., 2006), and it is helpful for understanding when EMI
can be applied, as it is not applicable under all circumstances (Robinson et al., 2012).
The theory and basic principle of EMI are based on the soil equivalent
resistance model (Sauer et al., 1955). Soil EC is assumed to arise from
three conductance pathways through the soil: (i) a conductance pathway
traveling through a continuous soil solution, (ii) a conductance pathway
traveling through the solid particles, and (iii) an alternating solid–liquid
pathway (Rhoades et al., 1989). In this formulation, the total soil water
content is separated into the fraction of water content in the fine pores
(mostly adsorbed by the clay minerals, contributing to the alternating
solid–liquid pathway) and the water content in the large pores (which
contributes to the continuous liquid pathway). The soil EC is influenced by
the volumetric water content (θ), the EC of the fractions of soil
solution (ECw), as well as by the volume of the solid particles and
their EC (ECs). As a consequence, several factors influence EC
(Friedman, 2005). A higher clay content and/or higher organic matter content
usually correspond to a higher content of adsorbed water (i.e., higher θ),
higher ECs, and higher cation exchange capacity (CEC), thus
potentially leading to higher EC (e.g., Hudson, 1994; Dingman, 2002; Lal and
Shukla, 2004; Roth, 2012). Moreover, of particular importance is ECw,
which often increases with higher CEC: as soil water interacts with the soil
minerals (especially clay) and with soil organic matter (SOM), ions from the
soil minerals can be released into the soil solution and, conversely, free
ions can be adsorbed to equilibrate the mineral surface charges. In this
respect, the initial EC of rain water and its residence time in the soil may
play a key role along with the mineralogical composition of the soil. Soil
compaction affects EC due to the reduced porosity and increased soil
particle-to-particle contact (Corwin et al., 2008; Brevik and Fenton, 2004).
Soil temperature also affects EC, which increases approximately 1.9 % per
degree centigrade (U.S.D.A., 1954; Corwin and Lesch, 2005). All these mechanisms
contribute to the complexity of EC and soil property relationships. Corwin
et al. (2008) and Farahani et al. (2005) highlighted that the EC versus soil
property functions are expected to be temporally variable unless ECw
and θ remain relatively unchanged, assuming ECs to be stable at
the temporal scale of observation.
The parameter measured by EMI sensors refers to a certain volume of soil
material according to a complex depth-weighting, thus it is indicated as
apparent electrical conductivity ECa. Callegary et al. (2007, 2012)
discussed this concept using a forward model of electromagnetic field
propagation, and found that the sensitivity of any EMI sensor, as well as the
depth of investigation, can differ significantly from those suggested by
McNeil (1980). Nevertheless, it appears clear that the final ECa reading of
any EMI device is a complex physicochemical measurement which results from
the propagation of the EM field within the volume of investigation and its
interaction with stable and transient soil properties and/or states, and that the
effective measurement depth and volume of investigation of an EMI sensor may
vary at different times and locations (Sudduth et al., 2001; Corwin and
Lesch, 2005; Farahani et al., 2005; Callegary et al., 2007; Corwin et al.,
2008; Werban et al., 2009; Zhu et al., 2010; Robinson et al., 2012; Callegary et al., 2012).
The fact that ECa measured with EMI responds to variations of several soil
properties encouraged its use for a broad range of scopes. Examples of the
application of EMI-based ECa measurements include soil salinity (e.g.,
Doolittle et al., 2001; Heilig et al., 2011), spatial patterns of soil
texture (e.g., Abdu et al., 2008; Heil and Schmidhalter, 2012), lateral
boundaries between soil types (e.g., Anderson-Cook et al., 2002; James et
al., 2003), depth of clay-rich layers (e.g., Saey et al., 2009; Doolittle at
al., 1994), clay content (e.g., King et al., 2005; Weller et al., 2007),
soil compaction (e.g., Al-Gaadi, 2012; Islam et al., 2014), soil CEC (e.g.,
Headley et al., 2004; Triantafilis et al., 2009), soil organic carbon (e.g.,
Martinez et al., 2009; Altdorff et al., 2016), assessment of soil quality
(e.g., Johnson et al., 2001; Corwin and Lesch, 2005), detection of buried
services (e.g., Won and Huang, 2004; El-Quady et al., 2014), and mapping of
active layer thickness in permafrost areas (e.g., Hauck and Kneisel, 2008;
Dafflon et al., 2013). ECa measurements are widely used in the context of
precision agriculture for, e.g., refining existing soil maps (e.g.,
Doolittle et al., 2008; Martini et al., 2013), precision farming (e.g.,
Lück et al., 2009; Scudiero et al., 2015), and harvest zoning (e.g.,
Frogbrook and Oliver, 2007; Priori et al., 2013).
EMI has become widely used to determine soil water content or to study
hydrological processes within the field of hydrogeophysics (Binley et al.,
2015; Huang et al., 2016, 2017). In a recent work, Calamita et
al. (2015) listed 20 of the papers which address the use of EMI sensors for
the determination of spatial and temporal patterns of θ. This
summary provides a clear illustration of the differences among ECa–θ
studies: estimation of θ was attempted for different soils and under
varying climatic conditions, from the plot to the small catchment scale,
with different temporal resolutions, and with different measurement schemes.
The temporal resolution varied between one measurement date up to several
days or years. Soil water content was estimated with a variety of probes
down to different depths of the soil profiles and sometimes total water
storage down to a certain soil depth was inferred. However, discrepancies
exist between the depth of soil moisture measurements and the theoretical
investigation depth of the EMI sensor in use.
Because factors affecting ECa readings are complex and often interrelated,
accurate interpretations have been a challenge (Zhu et al., 2010). In
particular, the transient nature of soil water content and soil temperature
was found to complicate the characterization of ECa variability by altering
its response to a given soil property during a given mapping event
(McCutcheon et al., 2006), such that the spatial and temporal variance of θ
explained by EMI-ECa data is strongly unstable (Calamita et al.,
2015). Repeated EMI measurements at one site (which require accounting for
temperature changes between different dates) allow for inference of the dynamic
component of the signal, based on the assumption that changes of ECa are
related to changes in the volume of water in the soil pores and/or changes
in the concentration of ions in the soil solutions.
Zhu et al. (2010) conducted repeated EMI measurements under varying moisture
conditions on a 19.5 ha agricultural field in central Pennsylvania, and
found that the spatial pattern of standardized ECa remained relatively
stable over time. In their study, the R2 values between ECa and
θ measured at different depths varied between 0.24 and 0.47. The authors
argued that, because of the spatial heterogeneity of soil and hydrologic
properties across the landscape, “the effect of soil moisture on ECa could
have been masked by other variations of soil properties and terrain
attributes”. Soil ECa was strongly influenced by soil moisture during
wetter periods and at wetter locations, whilst other factors masked the
effect of soil moisture on ECa variations during drier periods and at drier
locations. They also remarked that the relationship between temporal
variations of the soil ECa and soil water dynamics has not been thoroughly
investigated for different soil moisture conditions and drying–wetting
cycles, because simultaneous soil moisture measurements and EMI surveys were
conducted only three times in this study.
Martinez et al. (2010) measured soil ECa during 13 occasions over 3
years in Vertisols to map temporal changes of the spatial pattern of θ.
They used a principal component analysis to detect the main sources of
variation of ECa, and found that the EM38-DD could successfully identify
changes in soil properties due to tillage (i.e., changes of soil porosity)
and formation of cracks within the soil profile. In fact, the first three
components (90 % of the ECa variability explained) were related to soil
heterogeneity, soil management, and topography. Soil water dynamics
reflected temporal variations of the above-mentioned factors, and could
therefore be identified only as a less important signal.
Robinson et al. (2012) conducted EMI measurements on 9 occasions within 5
months in a small forested catchment with contrasting soil textures. Similar
to the finding of Western et al. (2003), they found that two distinct
patterns are present in the ECa and modeled θ maps: in the wet
state, the spatial pattern of ECa correlated well with the spatial pattern
of clay content, which, in turn, correlated well with θ, whilst the
pattern in the dry state shows a smaller degree of organization and
reasonable uniformity in θ across the catchment. They proposed a
differencing approach to estimate θ from ECa, which improved the
correlation from R2= 0.28 to R2= 0.48.
Recently, Shanahan et al. (2015) used repeated EMI measurements and
electrical resistivity tomography to model soil EC, combined with laboratory
estimates of gravimetric soil water content (θg), to
investigate more specifically the effects of θ on EC in soils with
contrasting texture and under different wheat genotypes. They documented
difficulties of relating soil EC to θg; in fact, they observed
that the correlation between changes in soil EC and changes in θg
varied with time and that the correlation was better for the
investigated loamy sand soil than for the clay loam. The authors concluded
that in soils where the effect of ECw appears to be larger, “changes
in bulk EC, measured by EMI, may be confounded by increased pore water
conductivity and less closely associated with changes in θg”.
Findings of the studies summarized above clearly show the need for a more
in-depth examination of the ECa–θ relationship for soils under field
conditions, with specific attention to the suite of physicochemical
properties and states controlling the EMI measurements. The complexity of
EMI data is too often ignored and the numerous issues related to the use of
EMI for mapping of soil moisture are not always illustrated clearly. This
may generate confusion due to the fact that proximal soil sensing techniques
are used for a variety of scopes in several disciplines and there is a risk
of interpreting ECa data beyond the limits of its applicability, resulting in misinterpretation.
This study aims to further investigate the potential of repeated EMI
measurements with wide spatial coverage to capture field-scale soil water
dynamics. To this end, we compare a time series of EMI measurements with
high-resolution data from a wireless soil moisture and soil temperature
monitoring network for a hillslope area in the Schäfertal catchment
(Harz Mountains, central Germany), for which spatial soil properties and soil
moisture dynamics are known in detail. This gives us the opportunity to
discuss the complex interplay between factors controlling ECa and θ,
and the use of EMI-based ECa data with respect to hydrological applications.
Material and methodsSite description
The study was carried out on a hillslope at the Schäfertal experimental
site, a small headwater catchment (1.44 km2) located in the Lower Harz
Mountains, central Germany (51∘39′ N, 11∘03′ E)
(Borchardt, 1982; Reinstorf et al., 2010; Martini et al., 2015). The
Schäfertal is a highly instrumented intensive research catchment within
the TERENO “Harz/Central German Lowland observatory” (Zacharias et al.,
2011; Wollschläger et al., 2017).
The catchment receives an average precipitation of 630 mm per year (of which
a large fraction may be falling as snow, according to the annual winter
conditions) and has an average annual air temperature of 6.9∘.
The slopes of the Schäfertal catchment are formed by Devonian
argillaceous shales and greywackes of the so-called “Tanner Zone”, which are
covered by periglacial sediments (Borchardt, 1982). Cambisols and Luvisols
are the dominant soil types on the slopes of the catchment, and Gleysols
occupy the valley bottom (e.g., Ollesch et al., 2005). Interflow is known to
play a relevant role within the runoff processes (Borchardt, 1982), and part
of it can result in return flow. The slopes of the catchment are intensively
used for agriculture, whilst meadow occupies the valley bottom (Schröter et al., 2015).
The hillslope site investigated for this study is a grassland transect
beside the agricultural fields at the outlet of the catchment and consists
of a north- and a south-exposed slope and a valley bottom where the creek
Schäferbach lies (Fig. 1). The spatial extent is ca. 250 m by 80 m.
The detailed soil mapping for the Schäfertal hillslope site is described
in Martini et al. (2015) and revealed low textural variations. The site is
extensively managed, i.e., neither irrigation nor fertilizers are applied,
which might alter ECw and effect the measured ECa. Overall, the site
characteristics (low textural heterogeneity, extensive land use) and the
experimental setup of the Schäfertal hillslope site provide a rare
opportunity to assess the suitability of repeated EMI surveys for mapping
soil moisture at the field scale.
Hydropedological site characterization
Intensive investigation of the vadose zone water dynamics on the hillslope
(Martini et al., 2015) was recently conducted with the aid of a wireless
soil moisture and soil temperature monitoring network (SoilNet; Bogena et al.,
2010). The positions of the 40 measurement nodes of the network (Fig. 1)
were determined by weighted Latin hypercube sampling with extremes (wecLHS,
Schmidt et al., 2014) using information from geophysical surveys (EMI and
gamma-ray spectroscopy) and topographic data. More detailed information can
be found in Martini et al. (2015). For each of the network nodes, six
sensors were permanently installed in the soil, with two repetitions at
three depths (5, 25, and 50 cm), measuring soil moisture and soil temperature
with hourly resolution. The sensors in use (SPADE, sceme.de GmbH i.G., Horn-Bad Meinberg,
Germany; Hübner et al., 2009) are based on a ring oscillator. A
sensor-specific seven-point-calibration in reference media with well-known
dielectric permittivity (Kögler et al., 2013) was conducted to improve
the θ measurement accuracy. Additionally, a sensor-specific
calibration was performed for the soil temperature sensors. Volumetric soil
moisture content was estimated based on the CRIM formula according to Roth
et al. (1990), where the dielectric permittivity of soil and air were
assumed to be 4.6 and 1, respectively, and the dielectric permittivity of
water was calculated based on the measured soil temperature (Kaatze, 2007).
Porosity was estimated using volumetric soil samples and ranged between 0.45
and 0.80. More details can be found in Martini et al. (2015).
Schematic map of the Schäfertal hillslope site (Martini et al.,
2015, modified): the position of the 40 nodes of the wireless soil moisture and
soil temperature monitoring network is indicated, as well as the spatial extent
of the four soil topographic units (STUs). EMI calibration point (grey dot) and
reference profile (dashed line) are indicated in the eastern part of the hillslope.
At each of the 40 sampling locations (Fig. 1), the soil profile was
described down to the depth of ca. 0.60 m (at the ridgetop, stoniness of the
soil due to shallow bedrock limited the investigation to ca. 0.50 m), and
the grain size distribution was determined for each node position and each
soil horizon. Four soil topographic units (STUs) were identified: silty loam
Cambisols were found on the slopes (namely STU 1, STU 2 and STU 4), with
few textural and morphological differences according to the topographic
positions; characteristic hydromorphic features were identified in the
valley bottom as indicators of the distinct wet state of the loam and silty loam
stagnic Gleysols (STU 3), where soils are frequently water-saturated in
winter and spring seasons. A summary of soil textural data relevant for the
present work is provided in Table 1; additional details of the soil
characteristics can be found in Martini et al. (2015).
Time series of (a) daily potential evapotranspiration (PET),
(b) daily cumulative precipitation (P), and (c) daily average
soil moisture at the three depths of observation (θd,05,
θd,25, and θd,50, respectively). Vertical dotted
lines indicate the dates of the EMI measurements: 19 September 2012, 18 October 2012,
20 November 2012, 18 April 2013, 28 May 2013, 31 July 2013, and 29 August 2013
(Martini et al., 2015, modified).
The hydrological behavior of the Schäfertal hillslope site was
characterized by Martini et al. (2015) using the daily average soil moisture
values for each measurement point of the monitoring network at the depths
of 5, 25, and 50 cm (θd,05, θd,25, and θd,50,
respectively, also named topsoil, intermediate soil horizon, and
deep soil horizon, as they refer to three distinct soil layers). The
monitoring period (from 15 September 2012 to 14 November 2013) comprises
different states of soil moisture in response to varying atmospheric
conditions (Fig. 2). Soil moisture increased during the fall of 2012, when
rainfall events were frequent and evapotranspiration (ET) decreased. The
winter season was characterized by low precipitation (P) and low ET,
followed by the spring season (April to June 2013), dominated by strong
dynamics of soil moisture in response to increasing ET and extreme rainfalls
up to 49 mm day-1. Large areas of central Europe were flooded at that time, and
soils at the Schäfertal site were observed to be saturated in swales and
depressions. During the summer period, ET exceeded P and the soil remained
drier than the annual mean. The wetting transition started at the beginning
of September 2013, with intense rainfalls and decreasing ET.
Soil texture (median values) of the Schäfertal hillslope site for
the topsoil (ca. 0–15 cm) and for the subsoil (ca. 15–60 cm). More detailed
information can be found in Martini et al. (2015).
In the last decades, a number of sensors were developed for field
measurements of ECa, based on the electromagnetic induction theory (Keller
and Frischknecht, 1966). Technically, a transmitter and a receiver coil are
placed on (or near) the soil surface at a fixed distance from each other,
and the transmitter coil is energized with an alternating current. This
generates a time-varying magnetic field, which induces electric fields in
the soil, which in turn induce a secondary magnetic field. Such phenomena
are described by Ampere's and Maxwell's laws. Both the primary and the
secondary magnetic fields are sensed by the receiver coil and, under certain
geometric conditions indicated as “low induction number” (McNeill, 1980;
Callegary et al., 2007, 2012), the ratio between the
primary and the secondary magnetic field can be used to estimate the ECa of
the volume of soil under investigation.
Generally, EMI systems consist of a transmitter and a receiver coil spaced s
and operating at a certain frequency f. As s increases, the EM field
propagates through a larger volume of soil. As f increases, the EM field is
more attenuated and therefore penetrates less into the soil, reducing the
volume of investigation. Transmitter and receiver coils are commonly
adjusted in coplanar configuration. Vertical coplanar coils (VCP) generate a
horizontal magnetic dipole orientation (HDP), whilst the horizontal coplanar
coil configuration (HCP) generates a vertical magnetic dipole (VDP). The
coil configuration has implications for the volume of investigation. McNeill (1980)
provided the relative response versus depth for an EMI device in both
HDP and VDP and the “cumulative response” for homogeneous and layered
soils. According to that, the response of an EMI device in HDP has larger
sensitivity close to the soil surface (or, more precisely, immediately below
the coils), with monotonic decay with depth, whilst the VDP configuration
provides maximum sensitivity to the depth of ca. 0.4 ⋅s (i.e.,
40 % of the inter-coil spacing s). Additionally, the effective depth of
exploration, defined as the portion that contributes with 70 % to the
measured value of ECa, is 0.75 ⋅s for the HDP and 1.50 ⋅s
for the VDP configuration.
For this study, soil ECa was measured using an EM38-DD device (Geonics Ltd., Ontario,
Canada), widely used for environmental studies and hence severely tested under
various conditions. The system is composed of two units mounted
perpendicularly to each other, both consisting of a transmitter and a
receiver coil (s= 1 m), which allows simultaneous measurements of ECa over
two depths for every measurement location. In VDP, given the operating
frequency f= 14.5 kHz, the theoretical maximum sensitivity corresponds to
the depth of ca. 0.40 m and the theoretical maximum investigation depth to
ca. 1.50 m. In HDP (f= 17 kHz), the sensitivity of the device decreases
with depth down to a theoretical maximum depth of investigation of ca. 0.75 m (McNeill, 1980).
Surveys were conducted on seven measurement dates within the soil moisture
monitoring period (Fig. 2), with three measurement dates (19 September,
18 October and 20 November 2012) during the wetting transition; two dates
(18 April and 28 May 2013) during the dynamic spring period; and two dates
(31 July and 29 August 2013) during the dry summer season. The surveys were
conducted with the EMI device mounted on a sledge (made of wood and plastic,
in order to avoid conductivity anomalies) at ca. 0.05 m above ground and
pulled by one operator at constant walking speed. The study area was divided
into three fields: northern slope (i.e., STUs 1 and 2), valley bottom (i.e.,
STU 3) and southern slope (i.e., STU 4), and each field was measured
separately. A fixed location next to the study area (Fig. 1), was used as
calibration point for instrument nulling (McNeill, 1980) before each survey,
and according to the recommendations of, e.g., Robinson et al. (2004), a
warm-up period of at least 30 min was ensured before measurements were
started. Before and after the surveys, ECa was measured along the reference
profile (i.e., a fixed 40 m transect, Fig. 1) in order to assess and
correct a possible drift in the data (e.g., Sudduth et al., 2001; Abraham et
al., 2006). ECa was measured along survey lines spaced ca. 5 m apart with a rate
of 5 records s-1, resulting in an approximate resolution of 0.2 m along the
main direction. Towards the end of each of the surveys, crossing lines
(Simpson et al., 2009) were measured in order to use the crossover points
for drift correction (CWA 16373, 2011; Delefortrie et al., 2014).
Processing and integration of time-lapse ECa measurements
Data collected using the HDP configuration showed strong noise. This caused
critical problems in data processing and hindered a purposeful data
interpretation. As the datasets of ECa measured in VDP did not show
significant noise or drift, only those data were used for the present work.
Similar to Rudolph et al. (2016), data points located within a 2 m circular
buffer area around each of the soil moisture monitoring network nodes were
removed in order to exclude any possible data alteration induced by the
magnetic components of the network nodes. By plotting the measured ECa data
over time, a limited number of additional outliers could be identified as
isolated extreme and unrealistic values.
Data collected with EMI devices may be affected by drift due to instability
of the calibration or to temperature changes (Robinson et al., 2004). The
measured crossing lines were used to identify and correct the drift: with
the help of the interfaces of normal profiles and crossing-lines, a linear
drift function was derived for the datasets which required this and was used
for drift correction. On average, the observed drift was as low as 1.14 mS m-1.
In this step, we assumed that ECa along the reference profile
remains constant during the time of the survey, i.e., ca. 45 min for the
south-facing slope (STU 1 and STU 2 in Fig. 1), ca. 15 min for the valley
bottom (STU 3) and ca. 30 min for north-facing slope (STU 4), hence the
reference profile measured at the beginning and at the end of each of the
surveys must show similar values of ECa.
Due to the sensor nulling performed prior to each survey, a small offset may
occur between the datasets collected at different measurement dates, e.g.,
because of differences in weather conditions which may affect the
measurement signal (e.g., Triantafilis et al., 2000). For this reason, we
refrain from comparing absolute values from different measurement dates in
this study and concentrate (i) on the analysis of differences in spatial
patterns of θ and ECa identified using the Spearman rank correlation
coefficient and (ii) on differences in the relationship between ECa and θ
for the individual measurement dates. By doing so, we are well
able to discuss the data in terms of hydrological processes and do not
attempt to quantify temporal changes of θ from the EMI measurements,
which would not be supported by the dataset.
ECa data measured along the reference profile for the same day were plotted
against time and, if necessary, field data were corrected by applying a
shift (on average as low as 0.86 mS m-1) based on the mean ECa of the
reference profiles. We tested that this did not produce artifacts in the
spatial pattern of ECa. Based on the assumption that ECa along the reference
profile does not vary within the duration of the measurement (i.e., a few
hours), such a procedure ensured the data collected with different surveys
within the same day to be quantitatively comparable. Measured ECa data were
standardized to the reference temperature of 25 ∘C using the
correction factors provided by U.S.D.A. (1954). Three different reference soil
temperatures were calculated (one for the valley bottom and one for each of
the two slopes with opposite exposition) averaging all available temperature
values measured hourly at the depths of 25 and 50 cm between 09:00 and
16:00 LT on each EMI measurement date (i.e., the time frame in which the surveys were
carried out). Except for the topsoil, temperature variations within such
time intervals are negligible. Among the different measurement dates, the
lowest soil temperature was recorded for the EMI survey in November 2012
(i.e., 4 ∘C on the south-exposed hillslope), and the highest in
July 2013 (i.e., 19 ∘C in the valley bottom).
For each measurement date and for each independent dataset, the experimental
variogram was calculated for the temperature-corrected ECa and fitted using
a linear model for comparability. The fitting parameters were used to
interpolate the data using block kriging with a cell size of 1 m. The choice
of using linear variogram models was supported by the fact that, despite not
all experimental variograms showing a linear behavior at larger lag
distances, linear behavior is always given for the 1 m distance used later
on for interpolation (data not shown). Afterwards, for each measurement
date, the three datasets for the northern slope, the valley bottom, and the
southern slope were aggregated, and ECa values of the kriging cell
corresponding to the location of each network node were extracted for each
measurement date, similar to Zhu et al. (2010). For the following analysis,
extracted ECa values (ECae) for the seven measurement dates were used
in combination with the daily-averaged soil moisture (θd) at
the depths of 5, 25, and 50 cm (based on the available hourly measurements
between 09:00 and 16:00 LT) at each single network node for the same measurement
dates. As the two methods refer to very different measurement volumes (i.e.,
integrated ECa values from EMI versus local soil moisture estimation from the
SPADE sensors which compose the monitoring network), an integrated soil
moisture value (θd,CS) was calculated for every measurement
date and for every node of the monitoring network:
θd,CS=∑i=1nθnCSzi-1-CSzi∑i=1nCSzi,
where θn are the soil moisture measurements at the three
depths of monitoring and CS(zi) refers to the cumulative sensitivity
function of the EMI (McNeill, 1980).
Although this simple approach neglects vertical changes of soil properties
within the soil profile (i.e., soil horizons which may affect the vertical
distribution of θ), we assume that the integrated soil moisture
values θd,CS provide representative information about the
weighted θ within the volume of soil sensed by the EMI device.
Analysis of the temporal stability of soil moisture and ECa patterns
The Spearman rank correlation coefficient was used to investigate the
temporal stability of the spatial pattern of ECae and θd
over the seven dates of survey. This coefficient was proposed by Vachaud et
al. (1985) as a measure of similarity between two datasets, based on the
comparison of the rank of spatially distributed observations between two
times, and is defined as follows:
rsj1j2=6∑i=1NRi,j1-Ri,j22Ns-1NsNs+1,
where Ns is the total number of spatial observation locations,
R(i,j1) is the rank of the observation for the position i and for the
time j1, and R(i,j2) is the rank of the observation for the same position, but
for the time j2. The rs coefficient ranges between -1 and 1, and
describes the statistical dependence between the two ranked variables: rs= 1
when there are no changes in the rank of the observations and decreases
proportionally to the number of observations for which the rank varies and
the number of position changed both toward a higher or lower rank. In other
words, the Spearman rank correlation coefficient shows the qualitative
similarity between spatially distributed observations.
Results and discussionObserved spatial patterns of ECa and soil moisture
In contrast to results from other sites (Martinez et al., 2010; Robinson et
al., 2012; Lausch et al., 2013; Martini et al., 2013), ECa measured on the
Schäfertal hillslope was low, ranging between 0 and 24 mS m-1 during
the complete measurement period and showed a very small range of spatial
variation, which we attribute predominantly to the small heterogeneity of
soil texture. The range in ECae measured along the slopes varied
between 7.6 mS m-1 in August 2013 and 11.8 mS m-1 in November 2013.
This small range makes the interpretation of the dynamics in ECa
challenging. Nevertheless, the low soil textural variation along the slopes
provides the opportunity to evaluate the effect of soil moisture on the
measured ECa without the need to account for significant influences of soil texture.
Spatial maps of (a) measured ECa (after processing);
(b) extracted apparent electrical conductivity (ECae) for
the positions of the 40 nodes of the soil moisture monitoring network;
(c) daily mean soil moisture at 5 cm (θd,05);
(d) daily mean soil moisture at 25 cm (θd,25);
(e) daily mean soil moisture at 50 cm (θd,50).
Spearman rank correlation coefficients (rs) between spatial
patterns of ECae. Values of rs≥ 0.9 are highlighted in bold.
Spearman rank correlation coefficients (rs) between spatial
patterns of soil moisture (θd,05) in the topsoil. Values of
rs≥ 0.9 are highlighted in bold.
Spearman rank correlation coefficients (rs) between spatial
patterns of soil moisture (θd,25) in the intermediate soil horizon.
Values of rs≥ 0.9 are highlighted in bold.
Spearman rank correlation coefficients (rs) between spatial
patterns of soil moisture (θd,50) in the deep soil horizon. Values
of rs≥ 0.9 are highlighted in bold.
For the seven measurement dates, the overall spatial pattern of measured ECa
as well as the extracted apparent electrical conductivity at the positions
of the network nodes (ECae) showed highest values in the valley bottom
(STU 3) and on the footslope (STU 2), whereas the hillslopes (STU 1 and STU 4)
showed lower values (Fig. 3a and b). Similar spatial patterns were observed
for soil moisture (Fig. 3c–e) at the three depths of monitoring. Absolute
values of measured ECa were lowest in September 2012, May and July 2013 and
highest in October 2012, April, and August 2013.
For the dates of the EMI surveys, the overall spatial distribution of soil
moisture measured at the nodes of the monitoring network showed similar
distributions (Fig. 3c–e), with the lowest θd being measured
in summit and backslope positions of the south-exposed slope, and highest
θd in the valley bottom. The topsoil's daily average moisture (θd,05)
exhibited the largest temporal variability, with
overall hillslope minimum in September 2012 and July and August 2013 (i.e.,
0.15, 0.16, and 0.10 m3 m-3, respectively; Fig. 2) and maximum in
April and May 2013 (i.e., 0.41 and 0.43 m3 m-3, respectively).
Daily average soil moisture of the intermediate soil horizon (θd,25)
ranged between 0.17 (measured in August 2013) and 0.37 m3 m-3
(in April and May 2013). The deep soil horizon showed less
variable daily average soil moisture ranging between 0.23 and 0.25 m3 m-3
except for the measurement dates of April and May 2013 (θd,50= 0.35 m3 m-3).
The fact that, during the monitoring period, soil moisture values covered
the complete annual range from very dry (in August 2013) to near-saturation
(in May 2013), while few variations were observed in the range and
absolute values of ECa for the different measurement dates, gives a first,
strong indication that, for the Schäfertal hillslope site, soil moisture
has little influence on the measured ECa.
Temporal persistence of the spatial patterns
To further analyze the temporal persistence of the generally similar spatial
patterns of ECa and soil moisture, the Spearman rank correlation coefficient (rs)
was used. To this end, we investigated the temporal persistence of
the spatial pattern of ECae as well as the temporal persistence of the
spatial pattern of θd at the three depths of observation. The
overall spatial pattern of ECae exhibited a similar distribution for
all measurement dates (higher values in the valley bottom, lower values on
the slopes), whilst the spatial organization of the values within the site
showed some differences. Two distinct spatial patterns (Table 2, rs≥ 0.9)
of ECae were highlighted: one being present in September,
October, and November 2012 and May 2013, and another one in April, July, and August 2013.
The spatial pattern of soil moisture in the topsoil (θd,05)
showed low persistence with rs decreasing proportionally to the time
between two measurement dates (Table 3). The intermediate and deep soil
horizons (θd,25 and θd,50; Tables 4 and 5) showed
a similar evolution of the pattern, however, as expected, with higher
persistence than observed for the topsoil moisture. Three groups (rs≥ 0.9)
of spatial distribution could be identified: (i) transition from
dry to wet state (September, October, and November 2012); (ii) wet state
(April and May 2013); and (iii) dry state (July and August 2013).
The direct comparison of the spatial patterns of ECae and θd
showed a clear difference for all three measurement depths. This
again supports the observation that, for the Schäfertal hillslope site,
soil moisture has little influence on the measured ECa.
Correlation between ECa and soil moisture at the measurement node positions
Figure 4 shows the relationship between ECae and θd for
the different EMI measurement dates and depths of soil moisture monitoring.
The plots in the bottom panels relate ECae to the θ values
calculated based on the cumulative sensitivity function (θd,CS)
proposed by McNeil (1980). As discussed earlier in the text, intrinsic
limitations exist in the EMI measurement technique which may limit the
comparability of absolute ECa values; thus we did not attempt to interpret
the temporal changes of ECae from one measurement date to the other,
but rather we focus on the ECae–θd relationship for every
single measurement date and depth of monitoring which, however, provides
useful hints about the strength and persistence of the relationship.
Taking a closer look at the ECa–θ relationship shown in Fig. 4,
the topsoil moisture (θd,05) generally showed an overall poor
correlation with ECa, with the exception of the survey in April 2013.
Nevertheless, the EM38-DD in VDP has little sensitivity to shallow
structures (Callegary et al., 2007, 2012; McNeil, 1980). For the depths of
25 and 50 cm, very poor correlation was found during the wetting transition
(i.e., September, October, and November 2012, with p> 0.05) and
for May 2013. Better correlation was found for the measurements in April 2013
and in the dry state (July and August 2013). In particular, R2> 0.50
was found for the ECae–θd relationship
for both the intermediate and the deep soil moisture measurements in April
and July 2013, as well as for the 25 cm depth in August 2013. The same is
well summarized by the ECae–θd,CS relationship, as
expected. Overall, when the entire hillslope area is considered, ECae
was observed to show some correlation with θd for only one of
the two measurement dates in the wet state and on both measurement dates in
the dry state. Nevertheless, no unique correlation between ECae and
θd could be identified for the complete time series, which
clearly shows that ECae cannot be used as a proxy for quantitative
spatial soil moisture distribution at the investigated site.
Deeper insights into the factors controlling the temporal dynamics of the
ECae–θd relationship can be gained by considering the
relative position of the point clouds of the four STUs (represented with
different colors in Fig. 4). The fact that measurement points within the
same STU clustered within a limited region of the scatter plot illustrates
the rather low within-STU variability of the soil bulk electrical conductivity at the site.
Linear regression between ECae and θd for every
EMI measurement date and every depth of soil moisture monitoring (θd,05,
θd,25 and θd,50, respectively), as well as for the
integrated soil moisture calculated using the cumulative sensitivity
function (θd,CS). The different colors represent measurement
points located within: STU 1 – black dots; STU 2 – red dots; STU 3 – blue
dots; and STU 4 – green dots. Regression coefficients R2 are indicated;
the significance levels p< 0.05 and p< 0.01 are indicated as *
and **, respectively.
For some of the measurement dates, the point clouds of the different STUs
occupied different positions relative to each other following changes of ECa
and, especially, θ. A distinction, in terms of moisture content, can
be observed (Fig. 4) between the soils on the slopes (STU 1 and STU 4,
south- and north-exposed, respectively, but with similar soil texture),
which can be referred to differences of ET on the north- and south-exposed
slopes leading to lower ET and higher soil moisture values for the north-exposed STU 4. Such an effect was evident at the beginning of the monitoring
period (measurement date in September 2012) as the result of the summer
period during which ET is presumed to have led to persistently different
moisture values, which remained visible during the rest of the wetting
transition (October and November 2012). Similarly, the two measurement dates
in the dry season (July and August 2013) showed higher θd
values for STU 4 than for STU 1 at all depths of measurement. ET is also
presumed to have had important effects on the topsoil moisture in the valley
bottom (θd,05, blue dots in Fig. 4), which has high porosity
and remained rather dry in the summer period. This circumstance was inferred
as favorable for the occurrence of preferential flow through the topsoil in
the valley bottom (STU 3) at the end of the dry seasons in 2012 and 2013
(Martini et al., 2015). Higher θd compared to the slopes
persisted in the valley bottom during the winter period, due to the
combination of local soil properties (i.e., higher porosity), topographic
position and the presence of a shallow groundwater table. In particular, the
latter allowed the soil to reach saturation in the valley bottom, locally,
according to the local topographic features. This is evident in Fig. 4 for
the measurement date in April 2013, represented by five network nodes (out
of seven in STU 3, blue dots) which are now well separated from the rest of
the data points showing soil moisture values as high as 0.72 m3 m-3.
At the same time, the groundwater-distant soils on the
slopes received water only from snowmelt and from rainfall. The flood event
with strong rainfall at the end of May 2013 is responsible for the overall
high θd measured at the site. Large areas within the valley
bottom were saturated due to shallow groundwater level, and patches of
ponding water were observed; local emergence of return flow was observed at
footslope positions within the catchment. In the summer period (measurement
dates in July and August 2013 in Fig. 4), ET plays an important role in
conjunction with local soil properties. Thus, the different moisture content
between the soils on opposite slopes (STU 1 and 4) is visible, as well as
the higher θd in the subsoil in the valley bottom (STU 3).
Based on this, the three distinct spatial patterns of soil moisture observed
in Tables 3–5 can be attributed to distinct factors: local soil
properties and ET in the dry state (July and August 2013); local soil
properties, topography, and a shallow groundwater table in the wet state (April
and May 2013); and local soil properties, progressive reduction of ET and
progressive rise of the groundwater table in the valley bottom during the
transition from dry to wet (September, October, and November 2012).
In a similar manner, the two distinct spatial patterns of ECae (Table 2)
can be discussed referring to Fig. 4. Under dry soil conditions (July
and August 2013), the higher ECa measured in the valley bottom (STU 3)
compared to the slopes can be attributed to the presence of loam and silty
loam stagnic Gleysols, with finer texture and high organic matter content.
The silty loam Cambisols on the slopes (STU 1, 2, and 4) showed similar
values of ECa in response to overall similar textural characteristics. In
April 2013, an important contribution to the high moisture content on the
slopes came from snowmelt. Thus, a large volume of water within the volume
of soil sensed by the EMI device was likely to have low ECw leading to
overall low ECae values being comparable to the dry state, when the
pores were air-filled. Furthermore, the influence of more conductive water
(from groundwater which drains the fertilized agricultural fields of the
Schäfertal catchment) enhanced the higher ECae values for the
valley bottom compared to the slopes. This is evident from the gap, in terms
of ECae, between the blue dots and all other points in Fig. 4. As a
consequence, the spatial pattern of ECae for the measurement date in
April 2013 was substantially similar to the spatial pattern observed in the
dry season (Table 2). This is not the case for the measurement date in May 2013,
when there was no contribution of water from snowmelt. As a
consequence, a higher concentration of ions in the soil solution can be
assumed, causing a higher ECw that is presumed to have contributed
significantly to the bulk soil electrical conductivity for the entire study
area, with the effect of masking the textural differences between the valley
bottom and the slopes. Therefore, the spatial pattern of ECae for the
measurement date in May 2013 did not reflect those of July, August, and April 2013.
Similarly, the spatial pattern of ECae during the wetting
transition (September, October, and November 2012) is presumed to reflect the
contribution of water with different ECw due to subsurface flow through
the soil, where the solid matrix can be enriched with ions due to the
process of evaporation and consequent precipitation of ions. Furthermore,
the poor correlation between ECae and θd for the
measurement dates in May 2013 and during the wetting transition was
determined by the fact that the soil in the valley bottom did not show
significantly higher θd compared to the soils on the slopes,
as it occurred, instead, for the measurement dates in April, July, and August 2013
(Fig. 4). This can be explained with the lower θd in the
topsoil and intermediate soil horizon (θd,05 and θd,25,
respectively) for the valley bottom (Martini et al., 2015), and
with the occurrence of the flood event in May 2013, when the soil reached
saturation in large portions of the entire Schäfertal catchment, locally
with overland flow. Under such conditions, ECw could be altered by the
flushing of soil organic matter, nutrients and ions released from the solid
matrix of the soil from the catchment. Another reason for the different
ECae patterns observed (measurement dates in September, October, and
November 2012 and May 2013, on one hand, and April, July, and August 2013, on
the other hand, Table 2) lies in the varying relative position of STU 1 and
STU 4 along the x axes (i.e., in terms of ECa). Based on the interpretation
discussed above, such differences can be attributed to the occurrence of
different water infiltration and transport processes which may take place at
different positions according to local soil properties and nonlocal factors
such as topography, and therefore influence the within-field variability
ECw. It is important to remark that measurements of ECw are not
available for the Schäfertal hillslope site with adequate spatial
coverage. However, for the different states of soil moisture, distinct
hydrological processes were described as taking place at different locations
within the hillslope area and at different soil horizons. This knowledge,
based on high-resolution monitoring of soil water content combined with
information on spatial heterogeneity of soil characteristics, enabled
inference of spatial and temporal changes of variables (including ECw)
relevant for ECa data interpretation.
The spatial pattern of ECae (Table 2) appears to mirror primarily the
spatial heterogeneity of soil textural properties, i.e., higher ECa for the
valley bottom (STU 3) than for the slopes (STUs 1, 2, and 4). The occurrence
of different hydrological processes (e.g., water infiltration and transport
through the vadose zone as well as dynamics of the groundwater level) which
take place at different positions along the slope can modify ECw
differently and, in turn, induce small changes in the ECa pattern. In
summary, our observations suggest that static soil properties (such as
texture, porosity, and organic matter content) and superimposed temporal
variations of ECw control the spatial pattern of ECa measured with EMI
at our site. Soil moisture itself has only a minor effect on ECa, although
it is clear that it acts as the carrying agent for transporting the ions
leading to ECw. Given the proven site-specific nature of EMI applied to
soil studies and the relatively strong correlations that have been recorded
between soil water content and ECa at some other locations, it seems
important to acknowledge that this statement is not necessarily valid at all
sites. However, the strength of the relationship between ECa and soil
moisture can only be evaluated if data measured during different
hydrological states are available. This is also obvious from our data since
it must be considered that if EMI surveys would have been conducted only on
measurement dates in April, July, and August 2013, ECa would have been
interpreted as a reasonable proxy for θ (Fig. 4), which clearly
shows the importance of time-series data for proper interpretation of EMI.
It is evident from Fig. 4 that the range of ECa remained rather constant
for the seven measurement dates, although θ varied significantly. The
variability of ECa within a single STU was rather small, especially for the
soils on the slopes, and the ECae–θd relationship in
Fig. 4 is controlled by the relative position of the STU clusters. As a
consequence, the correlations between ECae and θd may
become more evident when applied to a site with more contrasting soil
properties: for instance, if only STU 1 and STU 3 would be considered for
the Schäfertal hillslope site, rather high R2 values would be found,
simply because the two soils show constantly lower ECae and lower
θd, and consistently higher ECae and higher θd,
respectively. In contrast, if soils with similar texture would be
considered (e.g., only the soils on the slopes, excluding the STU 3), no
correlation would be found between ECae and θd throughout
the monitoring period, because there are no clear differences in ECae
among STUs 1, 2, and 4 (Fig. 4), and because changes of θd do
not affect ECae, unless they are responsible for significant variations
of ECw. But the latter effect would in turn lead to comparable changes
on both slopes.
Using EMI for mapping soil moisture and implications for soil mapping
It is widely acknowledged that EMI surveys offer the potential to map the
soil spatial variability over large areas within relatively short time,
non-invasively and with high spatial resolution (e.g., Doolittle and Brevik,
2014). This makes EMI methods an important aid for optimizing the number of
soil samples required to generate a soil map, and for the numerous
applications which require detailed soil maps. The results of this study show
the importance of repeated surveys in order to capture the dynamics of the
spatial pattern of ECa. This, combined with a sound interpretation of the
factors controlling such dynamics, allows for obtainment of the most reliable
information from the ECa maps. With respect to that, EMI-based ECa maps can
certainly be important supports for hydrological studies, as repeated EMI
surveys at one site provide the opportunity to identify stable patterns of
soil ECa controlled by the spatial heterogeneity of soil properties, which
in turn have important effects on the soil water dynamics.
Similar to our findings, Zhu et al. (2010) observed that “wetter sites were
generally distributed in the areas with lower elevations, gentler slopes,
and depressional landscape positions. These areas also corresponded to a
shallower water table and deeper depth to bedrock. These observations
suggest that soil ECa is more soil moisture dependent in wetter landscape
positions than in drier positions”. For the Schäfertal site, the
increase in soil EC can be related to two different reasons: (i) to the
wetting of the shallower sections of the soil profile with higher clay
content and higher organic matter content (peat soils of the valley bottom),
which leads to a release of adsorbed ions from the mineral and organic
surfaces and thus releases ions to the soil solution, or (ii) to the flushing
of the valley bottom by groundwater with higher electrical conductivity,
which would also lead to an increase in soil ECa .
The observed temporal variations of the ECae–θd
relationship clearly showed that soil moisture at the Schäfertal site is
not the major control on the measured ECa values, and temporal changes of
the ECa pattern are to a large extent unrelated to changes of soil moisture.
For EMI measurements conducted at different dates and for different moisture
conditions, Farahani et al. (2005) found that higher θ does not
necessarily correspond to higher ECa values, which is in good agreement with
our observations. Furthermore, Zhu et al. (2010) described that the wetness
condition was not the only factor influencing the spatial variability of ECa
at their site, and that terrain and soil properties masked the effects of
soil moisture on ECa during dry periods, whereas soil ECa was strongly
influenced by θ during wetter periods and at wetter locations.
Shanahan et al. (2015) found different ECa–θ relationships between a
sandy clay loam and a loamy sand, but for both, soil EC decreased with
depth, although gravimetric soil water content at depth was higher than or
similar to that at the surface.
Referring to the soil equivalent resistance model (Rhoades et al., 1989) on
the physical principle behind the ECa measurements, Corwin et al. (2008)
discussed the complexity of ECa measurements as being influenced by any soil
property or state that influences electrical conductance pathways in soils,
and explained that most of the soil properties that influence bulk soil
electrical conductivity exhibit co-dependency and thus provide overlapping
information on ECa. Furthermore, our data clearly show that the relationship
between ECa and a given soil property or state is time-of-measurement-dependent, which results from the dynamic nature of, e.g., groundwater levels,
soil water content, and concentration of pore water solution which influence
the electrical conductance pathway. This is also confirmed by field data
presented in Farahani et al. (2005), and the authors argued that the
relationship that they observed between ECa and θ can be partially
explained by the dependency of θ on stable soil properties, such as
clay content. Furthermore, they showed that such behavior may produce the
effect of magnifying the relationship between ECa and a given soil property
at certain times. In the same direction, our results clearly show the
difficulties of simply relating ECa to θ. The few differences of
soil texture and the rather low clay content at the Schäfertal hillslope
site are responsible for the small range of measured ECa.
Corwin et al. (2008) observed that, at sites where dynamic variables (e.g.,
salinity) dominate the ECa measurement, temporal changes in spatial patterns
exhibit more fluidity than systems that are dominated by static properties
(e.g., soil texture). Other studies (Zhu et al., 2010; Robinson et al.,
2012; Calamita et al., 2015) observed larger spatial variability of soil ECa
during the wetter periods and stronger correlation of ECa with clay and
topography patterns, as well as poor spatial organization under dry conditions,
supporting the concept of preferred soil moisture states as described in
Grayson et al. (1997).
In addition to that, important aspects to be considered in the
interpretation of EMI-based ECa data are the volume of investigation
of the EMI instrument and its spatial sensitivity. Callegary et al. (2007)
found that the instrument vertical sensitivity varied significantly both for
homogeneous and heterogeneous soils although the general shape of all
cumulative sensitivity distributions was similar to those predicted by
McNeill (1980), which holds true only for non-conductive soils, and
decreases with increasing ECa. In a more recent study (Callegary et al.,
2012), the same authors simulated the distribution of the EM field in a 3D
space, and found that the sensitivity pattern has a highly complex shape,
including areas of negative contribution (i.e., conductive anomalies may
contribute negatively to the instrument ECa reading). This implies that
caution is required when the ECa data are to be used quantitatively, as the
volume of soil sensed by the EMI device may change spatially and temporally.
Such an effect may not be a severe limitation for the Schäfertal site,
where bulk soil electrical conductivity is low, but may be significant for
more conductive soils or with more contrasting soil textures.
Given the complexity of the EM field propagation through natural soils
(hence, with a certain degree of heterogeneity) any quantitative
interpretation of ECa data (e.g., for estimating θ or solute
concentration) is difficult to prove with field data from EMI measurements
only. In fact, for every point in space where EMI measurements are
conducted, measured ECa resembles the bulk conductivities of all sources
contributing to ECa. These are ECs, and ECw for the actual volume
of investigation of the EMI sensor, which changes according to variations in
the electrical conductivity profile. The water itself does not contribute to
the soil EC. However, it is the carrying agent for ions released into the
pore water, and it is responsible for the thickness of water films around
the minerals which themselves control the mobility of ions therein, and
consequently affect soil EC (Friedman, 2005).
Interdisciplinary combination of expertise and the use of well-constrained
numerical models can certainly improve our ability to extract reliable
information from EMI-based ECa datasets. This is not trivial, and involves
the fields of pedology, hydrology, soil physics, soil chemistry, and
geophysics, as it must account for the propagation of the EM field through
the heterogeneous soil material, where complex interactions between stable
soil properties and transient state variables take place and are spatially
and temporally dynamic. Furthermore, such models need to be trained with
time series of highly resolved spatial data.
Benefits to the use of EMI-based ECa data may arise from the use of
multiconfiguration EMI systems and calibration procedures, as they allow
for collection of ECa data from multiple depths at the same time. Following the
original work of Lavoué et al. (2010) and its further improvement by
Mester et al. (2011), recent studies (e.g., Von Hebel et al., 2014; Shanahan
et al., 2015) promoted the calibration of ECa, collected using
multiconfiguration EMI, based on inverted electrical resistivity tomography (ERT)
data. This is probably the most advanced approach available nowadays
for calibrating EMI measurements collected at different points in space and
in time. Nevertheless, the reliability of this calibration procedure still appears
limited due to a number of fundamental issues which are not solved
yet. Among others, a major source of uncertainty is due to the fact that the
solution of ERT inversion is non-unique (e.g., Keller and Frischknecht,
1966; Koefoed, 1979; Sharma and Kaikkonen, 1999; Dafflon et al., 2013).
Consequently, the risk exists to adjust the EMI-based ECa data to soil EC
profiles which do not match reality, and little control is offered about the
uncertainties. Furthermore, existing calibration approaches rely on the
standard vertical sensitivity function of EMI (McNeill, 1980), which is only
valid for homogeneous and nonconductive soils and is not easily applicable to
natural soils, as clearly illustrated by the works of Callegary et al. (2007,
2012). However, even if all issues would be solved, multi-depth
calibrated ECa data from ERT inversion can provide depth-resolved
information on soil electrical conductivity only. The separation of soil
moisture from all the other properties and states that influence the EMI
measurement will still remain a challenge. In a recent study, Michot et al. (2016)
illustrated some of the issues related to the use of ERT for soil
moisture estimation. Soil spatial heterogeneity was found to be responsible
for the nonstationary nature of the relationship between electrical resistivity (ER)
and θ in a heterogeneous soil system. Moreover, the
authors argued that changes of ER were probably related to changes of
ECw (controlled by soil–plant interactions and infiltration
processes), as θ remained unchanged.
As soil and consequently also water and solute dynamics are spatially
heterogeneous, ideally it would be required to calibrate every single
measurement point within a study site for each measurement date. A proper
calibration of ECa for soil moisture monitoring would only be possible if
the temporal variations of all other state variables that induce
co-dependencies on ECa (such as temperature and ECw) could be
determined and if the influence of the water content on ECa would be strong
enough to make it measurable with EMI. This would be an enormous effort and
to our knowledge there are no published works which attempted such an
ambitious site characterization. We consider the dataset presented in this
study as one of the most complete with respect to EMI–θ studies;
nevertheless, this is still not adequate to provide data suitable for proper
calibration of ECa.
Similar difficulties exist for multi-frequency EMI sensors. Tromp-van
Meerveld and McDonnell (2009) could not resolve the θ depth profile
using an EMI sensor comprising six frequencies, which provided similar
responses for six different frequencies, although the distribution of θ
with depth was non-uniform due to rain events. The study of Calamita et
al. (2015) showed similar limitations, and the authors highlighted that a
number of factors can make the interpretation of ECa data with respect to θ
challenging, and that the use of the EMI method for hydrological
applications can be better understood when considering the effects of
ECw and clay minerals.
Shanahan et al. (2015) remarked that in ECa–θ studies it is commonly
assumed that a change in soil EC is simply due to a change in the volume of
the fluid. Nevertheless their study showed that, under certain
circumstances, changes in EMI-based ECa may be confounded by increased
ECw and less closely associated with changes in θ. Cassiani et
al. (2015) remarked on the need for more consideration for ECw, which may
play an important role. Our study confirms this, at least for the case of
low-conductive soils, and shows that the large changes of θ at the
Schäfertal site have negligible effects on the measured ECa. Different
results may be found for different soil types. In fact, a larger ECa response
to changes in θ was observed for clay-rich soils (e.g., Martinez et
al., 2010; Robinson et al., 2012; Shanahan et al., 2015). Good relationships
between ECa and soil moisture may be achieved locally and for certain soil
conditions, triggered by co-dependencies between most of the properties and
states that influence ECa.
Our results apply to the Schäfertal site and to landscapes with similar
soil characteristics (low conductive silty loam soils evolved on loess
deposits are widespread over large areas of central and northern Europe) and
call for proper interpretation of ECa, which respond to complex
physicochemical properties of soil. To this end, an interdisciplinary
approach, combining pedological and hydrological expertise with a solid
understanding of the (geo)physical principles underlying the EMI method, may
certainly improve the results of future studies.
Summary and conclusions
Repeated EMI surveys were conducted on a hillslope site within the
Schäfertal catchment, of which soil properties and soil moisture
dynamics were known. Soil ECa was mapped on seven dates with different soil
moisture states, comprising dry, wet, and transition from dry to wet. This
allowed for investigation of the effects of θ on the measured ECa under
field conditions and provided the opportunity to discuss the physical
principles behind EMI measurements of ECa.
Although the range of θ variations was very large throughout the
monitoring period, ECa showed a very small range of variation. Temporal
changes in spatial patterns of ECa were found to differ from temporal
changes in spatial patterns of θ. The observations discussed in the
present work support the conclusion that soil moisture is not the major
control on the bulk soil electrical conductivity measured with EMI, which
is, indeed, controlled by a number of soil properties and states with a
variable and time-varying relative contribution. It is worth remarking that
time-series data have the potential to reveal the limits of applicability of
the EMI method with respect to the specific site conditions and to avoid
over-interpretation of geophysical proxies.
Comparing repeated EMI measurements with high-resolution monitoring of soil
water dynamics in the vadose zone allowed us to identify two distinct
spatial patterns of ECa: the one representing the actual heterogeneity of
soil properties, i.e., under dry conditions (July and August 2013) or when
ECw was presumably low (April 2013), and the other, when different
processes, such as water infiltration and transport through the soil and
dynamics of the groundwater from the catchment, modify ECw and in
turn change the signal from the stable pattern of soil properties (e.g., in
September, October, and November 2012 and May 2013). Furthermore, our
observations suggest that for soils with low clay content, θ itself
has little influence on the measured ECa unless the electrical conductivity
of the soil solution changes significantly.
The combination of repeated EMI measurements and distributed soil moisture
monitoring at one site enabled us to provide a process-based interpretation
of the relationship between ECa measured with EMI and soil moisture, beyond
the limits which we might be subject to if only one method were available.
Experimental evidences and data interpretation provided with this research
promote a careful use of the EMI method for any environmental application.
Time-lapse measurements of ECa conducted with multiconfiguration EMI can
enable the capture of the spatial variation (including depth information) of
soil properties as well as the temporal dynamics of the variables involved.
Datasets with these characteristics, inverted based on well-calibrated
physically based numerical models that are able to represent the spatial and
temporal patterns of, ideally, all properties and states which influence
soil bulk electrical conductivity, can certainly improve our ability to
extract reliable information on environmental variables of interest that can
be used quantitatively. However, this may not be feasible for all sites, as
it requires large technical efforts and combined expertise in different
fields of research. In such cases, repeated EMI mapping can still provide
the opportunity to noninvasively map the soil heterogeneity of the site,
which makes EMI an important aid for any environmental research.
Data availability
The data will be made available to all interested researchers upon request to
the author (edoardo.martini@ufz.de).
The authors declare that they have no conflict of interest.
Acknowledgements
This research has received funding from ExpeER, a project funded by the
European Commission's Seventh Framework Programme and from the Helmholtz
Alliance EDA – Remote Sensing and Earth System Dynamics, through the
Initiative and Networking Fund of the Helmholtz Association, Germany. In
addition, it was supported by TERENO (TERrestrial ENvironmental
Observatories) and by WESS – Water and Earth System Sciences Competence
Cluster (Tübingen, Germany). Edoardo Martini acknowledges the support of
HIGRADE, the Helmholtz Interdisciplinary Graduate School for Environmental
Research. Hendrick Paasche (UFZ – Hemholtz Centre for Environmental
Research, Leipzig, Germany) is gratefully acknowledged for discussion on the
paper. We gratefully acknowledge S. Gerlach (Agrargenossenschaft
Straßberg-Siptenfelde), who provided access to the site and logistical
support, and the colleagues who helped to conduct the EMI surveys.
Constructive comments from five anonymous referees and an additional short
comment contributed to improving the paper. Data will be made available
to all interested researchers upon request.
The article processing charges for this open-access publication
were covered by a Research Centre of the Helmholtz Association.
Edited by: N. Romano
Reviewed by: five anonymous referees
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