Published November 21, 2016
Original Research
Early-Time GPR: A Method to
Monitor Spatial Variations
in Soil Water Content during
Irrigation in Clay Soils
Jonathan Algeo,* Remke L. Van Dam, and Lee Slater
Core Ideas
•Early-time analysis extends the use of
GPR to attenuating, clay-rich soils.
•Early-time GPR can upscale point
measurements of water content
when calibrated.
•Ongoing work studies alternative
statistics to represent the early-time
signal.
We used a recent ground-penetrating radar (GPR) methodology, earlytime amplitude analysis, with the goal of monitoring changes in soil water
content (SWC) in response to irrigation in clayey soils. We hypothesized that
early-time analysis could be used to monitor changes in SWC in clay-rich soil
where ground wave and reflection-based GPR methods traditionally fail. An
overnight irrigation experiment was performed in a 20- by 14-m section of
natural grassland at the Samford Ecological Research Facility in southeastern Queensland, Australia. Both GPR reflection surveys and ground wave
velocity analysis were ineffective at the site due to the signal attenuation
associated with the clay-rich soil. We collected daily GPR and time-domain
reflectometry (TDR) data sets during a 5-d period in August 2014, with soil
samples collected for gravimetric analysis at the conclusion of data collection. The GPR data display a clear response of the early-time signal
amplitude to changes in SWC. The GPR data sets exhibit a strong correlation
with SWC, as measured by TDR and gravimetric analysis of soil cores, which
is consistent with the dependence of GPR early-time amplitude on relative
permittivity. The results suggest that the early-time method can be used to
obtain spatially distributed information on subsurface moisture content in
clay-rich soils.
Abbreviations: AEA, average envelope amplitude; GPR, ground-penetrating radar;
GWC, gravimetric water content; SWC, soil water content; TDR, time-domain reflectometry; WARR, wide-angle reflection and refraction.
J. Algeo and L. Slater, Dep. of Earth
and Environmental Sciences, Rutgers
Univ., Newark, NJ 07102; R.L. Van
Dam, Institute for Future Environments,
Queensland Univ. of Technology,
Brisbane, QLD, Australia, and Dep.
of Civil Engineering, CEFET-MG, Belo
Horizonte, MG, Brazil, and Dep. of
Earth and Environmental Sciences,
Michigan State Univ., East Lansing,
MI 48824. *Corresponding author
(jonathan.algeo@gmail.com).
Vadose Zone J.
doi:10.2136/vzj2016.03.0026
Open access.
Received 31 Mar. 2016.
Accepted 25 July 2016.
© Soil Science Society of America.
This is an open access article distributed
under the CC BY-NC-ND license
(http://creativecommons.org/licenses/
by-nc-nd/4.0/)
Understanding the spatial distribution of soil moisture is critical to climate
modeling, agricultural water management, and research on biological and chemical processes in the vadose zone. Recent reviews by Vereecken et al. (2008) and Robinson et al.
(2008) explored the importance of soil moisture measurements in detail. However, traditional methods of estimating soil moisture have significant limitations. Methods such as
time-domain reflectometry (TDR) and soil sampling are both invasive methods that can
interfere with the processes being studied. Additionally, both are point measurements that
cannot easily provide dense, spatially expansive estimates of soil moisture. On the other
hand, large-scale estimates of soil water content (SWC) such as those collected by cosmic
ray probe (Franz et al., 2013) or satellite (Kerr et al., 2010) do not easily allow analysis of
field-scale processes.
Ground-penetrating radar (GPR) has long been established as a tool for measuring SWC
variation at the field scale (Huisman et al., 2003) using reflection data (van Overmeeren
et al., 1997; Lunt et al., 2005), ground wave data (Galagedara et al., 2003; Grote et al.,
2003; Huisman et al., 2001), and borehole transillumination data (Cassiani et al., 2004;
Rucker, 2011). It provides a noninvasive way to efficiently collect data across large areas,
which can be used to estimate the SWC in the vadose zone. However, both reflection and
ground wave analyses have limitations. Traditional reflection analysis requires detection
of a reflector at a known depth to calculate the two-way travel time, although in certain
Vadose Zone Journal | Advancing Critical Zone Science
instances this requirement can be circumvented (Gerhards et al.,
2008). The ground wave method requires the operator to find an
antenna offset where the direct ground wave is not interfered with
by the air wave or any reflections, regardless of changes in the SWC
or location at the field site. Both methodologies are hampered by
highly conductive soils, such as those with a high clay content,
which attenuate the signal.
The recently proposed early-time methodology (Pettinelli et al.,
2007, 2014) provides an alternative way to estimate SWC variation
at the field scale using GPR. The methodology includes an investigation into changes in the amplitude of the combined air and
ground waves to estimate changes in the electromagnetic properties of the near surface. The so-called average envelope amplitude
(AEA) statistic allows researchers to quantitatively analyze these
changes in amplitude. It is also sometimes referred to as the “average envelope” (Pettinelli et al., 2014) or “envelope amplitude” (Di
Matteo et al., 2013; Ferrara et al., 2013). The early-time method
can be applied when there is superposition of the air and ground
waves. Additionally, because the method does not rely on reflections, no near-surface reflector is required. These benefits mean
that the early-time method may provide a solution to clay-rich field
sites, where GPR is not commonly utilized due to the unavoidable
effects of excessive signal attenuation.
In this study, we used early-time GPR to monitor changes in SWC
using time-lapse measurements before and after an irrigation event
at a clay-rich field site. We hope to show that the early-time methodology can be applied in a highly attenuating environment at the
field scale. This would provide a way to use GPR in clay-rich field
sites, where it is traditionally considered a nonviable technology.
66Overview
of
Ground-Penetrating Radar
A GPR measurement typically involves two antennae: a transmitting antenna emits a short-pulsed electromagnetic signal, and
transmitted and reflected energy from the subsurface are then
measured by the receiving antenna. The signal travels through
the air and subsurface at different velocities, depending on the
relative dielectric permittivity (e r) of the materials, which is the
ratio of the absolute permittivity of the material to the permittivity
of a vacuum. Ground-penetrating radar is a valuable tool for soil
moisture studies because the water content of a soil has a significant effect on its e r. The e r of fresh water is around 80, while for air
and for mineral soil grains it is 1 and around 4, respectively. Small
changes in water content can thus lead to significant variations in
e r (Van Dam and Schlager, 2000). Additionally, water can have a
wide range of conductivities depending on its salt content, which
affects signal attenuation (Davis and Annan, 1989). Therefore, a
change in water content will significantly affect the electromagnetic properties of the subsurface.
VZJ | Advancing Critical Zone Science
The e r is related to the wave velocity by
v=
c0
( m r e r 2 ) éêë(1 + P 2 ) +1ùúû
[1]
where v is the wave velocity, c 0 is the speed of light, m r is the relative magnetic permeability of the material, m = m r m 0, where m 0 is
the magnetic permeability in a vacuum, m r is 1 for most common
Earth soils, and P is a loss factor:
P=
s
we
[2]
where s is the conductivity of the material, w is the angular
frequency, and e is represented by e = e r e 0, where e 0 is the permittivity of a vacuum. Ground wave and reflection-based methods
use Eq. [1] for estimation of e r by measuring the travel time of the
GPR signal that travels directly from the transmitter to the receiver
through the shallow subsurface (Grote et al., 2003) and the signal
that reflects off subsurface interfaces where there is a e r contrast,
respectively. The permittivity value can then be converted into
an estimate of the SWC by use of a pedotransfer function (Van
Dam, 2014).
The usefulness of traditional GPR methods is limited in high-conductivity material, such as clay-rich soils or materials with saline
pore fluids. The GPR attenuation coefficient (a) is related to s
and e by
a=w
æ
÷ö÷
me çç
s2
çç 1 + 2 2 -1÷÷ [3]
÷ø
2 çè
w e
In highly conductive soils, the GPR signal will often attenuate to
very low amplitude before it reaches the receiver.
GPR Early-Time Method
Early-time amplitude analysis, a methodology recently proposed by
Pettinelli et al. (2007), provides an alternate way to extract information on changes in SWC from common-offset GPR surveys.
A benefit of the method is that the antenna offset does not need
to be large. This means that the method works with commercial
bistatic common-offset antenna equipment with inseparable
antennae. The method allows the collection of amplitude information sensitive to permittivity and/or conductivity variations at
shallow depth. In early-time analysis, the earliest part of the signal
in a GPR reflection survey—the combination of the ground and air
waves—is analyzed without regard to whether they are separated in
time, an often restrictive requirement for the ground wave method
(Di Matteo et al., 2013). By analyzing changes in the amplitude
attributes of the early-time signal from both modeled and field
data, Di Matteo et al. (2013) were able to map near-surface variations in e r. They suggested that the method is highly capable of
mapping SWC variations when field calibration can be performed
using discrete point measurements.
p. 2 of 9
The early-time method has been used in a number of recent
studies to determine its efficacy. Pettinelli et al. (2014) built a
polyvinyl chloride tank and filled it with a layer of gravel at the
bottom and a layer of river sand above. Water pipes in the gravel
layer permitted raising the water level incrementally, allowing
them to monitor changes in the early-time signal as the water
approached the surface. They concluded that (i) the results of
their GPR measurements were in line with previous numerical modeling done by Pettinelli et al. (2007) and Di Matteo et
al. (2013), (ii) the average envelope amplitude of the early-time
signal and dielectric constant (i.e., SWC) are inversely correlated,
and (iii) the thickness of the portion of the subsurface affecting the early-time signal is on the same order as the GPR signal
wavelength in the materials they tested.
The amplitudes of the air wave (Aair-wave) and ground wave
(Aground-wave) are determined by (Di Matteo et al., 2013)
Aair-wave =
e0m 0
2pe 0 ( e r -1)S 2
[4]
é æ1ö m
ù
0
Aground-wave =- Aground-wave
exp êê -ççç ÷÷÷ 0 s S úú [5]
êë è 2 ø e 0 e r
úû
where
0
=
Aground-wave
er m0
2pe 0 ( e r -1)S 2
[6]
where m 0 is the magnetic permeability, e 0 is the dielectric permittivity of a vacuum, s is the soil electrical conductivity, A0 is the
amplitude of the ground wave in a vacuum, and S is the antenna
separation. The exponential term in Eq. [5] accounts for the evanescent portion of the ground wave that propagates, decaying,
above the surface (Annan, 1973; Di Matteo et al., 2013). One
drawback to the early-time methodology is that it is difficult to
determine to what extent changes in the amplitude of the earlytime signal are caused by changes in permittivity vs. changes in
conductivity, although the effect of each property on the earlytime signal has been analyzed independently in a controlled setting
(Comite et al., 2016). This is because the relative amplitude of the
direct signal is affected both by the conductivity of the subsurface,
as shown in Eq. [5], and by the permittivity of the subsurface, as
seen in Eq. [4–6]. However, it has been suggested that permittivity
plays a larger role in affecting the early-time amplitude, and it has
been shown that changes in conductivity do not affect the goodness of correlation between early-time amplitude and changes in
permittivity (Di Matteo et al., 2013). Comite et al. (2016), however,
suggested that envelope amplitude as a statistic for relating the
GPR signal to relative permittivity may be negatively impacted by
high-conductivity materials. They proposed an alternate statistic,
which they refer to as the “carrier frequency amplitude,” as a potentially more robust alternative.
VZJ | Advancing Critical Zone Science
66Methods
Site Description
The Samford Ecological Research Facility (SERF) is a 51-ha property in Queensland, Australia (Fig. 1). The property is used for a
wide variety of ecological, engineering, environmental, and educational programs, with a strong focus on understanding greenhouse
gas emissions (Karan et al., 2016). In support of these goals, a large
portion of the property is maintained as a typical Australian subtropical grassland, with grain grown and harvested on an annual
basis. The soil at the site is clay rich, with up to 40% clay content
in some horizons. This means that traditional transmission–
reflection GPR methods to characterize the site are likely to be
ineffective. The combination of a need for spatial soil moisture
data and a clay-rich soil was a strong motivation for testing the
early-time method at this site.
This study focused on an area in the northwest corner of
SERF’s grassland, where there is a relatively f lat area near a
stream, which provided water for an irrigation experiment. We
selected a 20- by 14-m portion of this area for irrigation and
geophysical measurements.
Background Characterization
Upon selecting the field site, we performed a series of geophysical
measurements to improve our understanding of the subsurface.
We collected a GPR wide-angle ref lection and refraction
(WARR) survey (Fig. 2). We also collected GPR common-offset
measurements (Fig. 3) over an irrigated patch using a PulseEKKO
system equipped with a SmartCart to determine the optimal GPR
settings for the site. The GPR data were collected using a bistatic
system with 200-MHz antennae at 1-m separation, with a pulser
voltage of 400 V. The SmartCart was equipped with an odometer
wheel to accurately position measurements every 5 cm as the GPR
moved along the transect. For both WARR and common-offset
measurements we used a 0.2-ns sampling interval.
Figure 2 shows the results of the WARR survey collected at the
field site on 11 August, prior to the main experiment. There is a
clear air wave in both the raw and gained data, while the ground
wave only appears after dewow and gain were applied—we used a
manual linear gain increasing from 2.5 dB at time zero to 15 dB
at 30 ns—and still attenuates to the point of being difficult to
pick by the maximum offset. No reflections are visible in the data
down to 30 ns.
Figure 3 shows the results of the common-offset survey collected
on 22 July. The direct signal at the start and end of the line is
a combination of the overlapping air and direct ground waves.
A 1- by 1-m segment in the center of the line was wetted with
120 L of water, causing the direct ground wave to slow down
and separate from the air wave. However, this separation cannot
be maintained across a wide range of SWC values, and the first
p. 3 of 9
Fig. 1. (a) Location of the Samford Ecological Research Facility within Australia; (b) location of the grid at the field site; and (c) setup within the 14- by
20-m grid. Vertical lines represent ground-penetrating radar transects, ovals are time-domain reflectometry (TDR) locations, and diamonds represent
a pair of soil sampling locations (where TDR measurements were also collected). The large gray semicircle is the sprinkler-irrigated area, and the gray
squares represent box infiltrometers.
break is difficult to identify. No reflections are evident down
to 30 ns.
Field Instrumentation and Data Collection
One week prior to data collection, 3 cm of rain fell at the field site.
We created a tarp and placed it over the area of our geophysical
investigation, but it is likely that some water flowed under the tarp
and infiltrated at the edges of our 20- by 14-m grid.
The timeline of measurements and irrigation can be seen in
Table 1. The grid was irrigated on the night of Day 1 using a
garden sprinkler. The sprinkler was set along the edge of the
grid, spraying water into the grid in a 180° arc, to a distance
of approximately 8 m. The sprinkler applied water at a rate of
approximately 2.5 cm h−1 at a 3-m radius from the sprinkler,
decreasing in rate both closer to and farther away from the
sprinkler. The sprinkler ran for approximately 8 h, starting at
4:33 PM. In addition to the sprinkler irrigation, three 1- by 1-m
box infiltrometers were placed in the grid (Fig. 1) and filled with
100 L of water at 4:00 PM on Day 1. The water was allowed to
completely infiltrate before the infiltrometers were removed from
the grid. This provided an alternate type of wetted area: a small
VZJ | Advancing Critical Zone Science
area of high water content surrounded by dry soil, as opposed to
the large swath of land irrigated by the sprinkler.
For the main experiment, we collected GPR measurements at 10 AM
daily from Day 1 until Day 5. Each data set consisted of 15 lines,
20 m long, at 1-m spacing (Fig. 1). Data collection took approximately 1 h. We used the same SmartCart GPR setup to collect these
data sets as for the 2 July common-offset measurements, except with
a 0.5-m antenna separation instead of 1 m to ensure that the air and
ground waves would overlap at all levels of saturation.
We collected TDR, in conjunction with the GPR data sets, at
10 AM using a HydroSense CS620 soil water measurement system
with a two-rod probe with 12-cm rods. The system determines
the period average output from the probe, t, in milliseconds. This
varies based on the e r of the subsurface and is therefore affected
by changes in the SWC. A total of 101 TDR points were taken
each day, focused around the locations selected for collecting soil
samples. These locations were selected to provide information on
the widest possible range of wetting conditions: completely dry,
within the box infiltrometers, and at 3 and 6 m from the sprinkler.
The TDR data collection took approximately 90 min.
p. 4 of 9
Fig. 3. Common-offset 200-MHz ground-penetrating radar line with
5-cm step size collected at the Samford Ecological Research Facility on 22 July 2014. Water (120 L) was applied to the surface over a
1-m-square area centered at 4 m along the line. Color represents normalized amplitude, from −1 (blue) to +1 (purple).
Fig. 2. Wide-angle reflection and refraction survey collected at the
Samford Ecological Research Facility on 11 Aug. 2014: (a) raw data,
and (b) dewowed data, with a linear gain of 2.5 dB at time zero,
increasing to 15 dB at 30 ns. Note that the ground and air waves do
not separate until after 1.1 m, at which point the ground wave is excessively attenuated. Color represents normalized amplitude, from −1
(blue) to +1 (purple).
At the end of the final day of measurements, soil samples were
collected from the 5- to 10- and 15- to 20-cm depths at 24 locations throughout the field. We selected these depths to avoid the
significant root zone in the grassland soil, as well as to mitigate
the effects of evapotranspiration during the course of the day. The
soil samples were collected using a standard size soil sampling ring
and promptly bagged. They were refrigerated on return to the
laboratory, within 3 h of their collection. A standard oven-drying
methodology was applied to calculate the gravimetric water content (GWC) of the 48 soil cores (Klute, 1965).
Data Processing
The GPR surveys contained approximately 400 measurements
along each line. Before processing, a moving average was applied
to the data in blocks of seven. Each averaged data point thus represents a portion of the transect approximately 0.35 m in length and
overlaps 30 cm of the data points before and after it. The data were
averaged to remove small-scale variation (outliers) that probably
resulted from challenges with ground coupling of the antennae in
the thick grassland.
A Hilbert transform was performed on the first positive half cycle
(Fig. 4) of each of the averaged measurements, according to Di
Matteo et al. (2013), using MATLAB’s built-in hilbert() function.
We passed each trace’s averaged GPR data directly into the hilbert() function. The Hilbert transform is described by
xˆ ( t ) =
1 ¥ x( s)
ds p ò-¥ t - s
[7]
where xˆ(t ) is the Hilbert transform of the function x(s), and the
integral is the Cauchy principle value integral. The Hilbert transform provides the imaginary part of the complex GPR trace. The
acquired GPR signals represent the real portion of the trace. The
Hilbert transform allows calculation of the envelope of the trace
(Fig. 4), also known as the instantaneous amplitude, by taking the
absolute value of the transformed GPR signal (Taner et al., 1979;
Ferrara et al., 2013).
A custom function was used to extract the first positive half
cycle of each measurement, which was shown by Di Matteo et al.
(2013) to provide the greatest signal/noise ratio of any portion
of the GPR early-time signal. We took the absolute value of the
Hilbert transform of the measurements and the integral of the
result divided by the unit length of the integral. The resulting
value is the AEA, which was then inverted to be consistent with
the work of Di Matteo et al. (2013) and Pettinelli et al. (2014). The
AEA−1 data were then compared from day to day and with the soil
core and TDR data sets, with an increase in AEA−1 expected to
correspond to an increase in GWC. The AEA−1 data are in unitless “amplitude units,” meaning that the GPR data show a relative
change in GWC rather than an absolute GWC value (Pettinelli
et al., 2007, 2014; Di Matteo et al., 2013). Hislop (2015) proposed
an alternate methodology that may estimate an absolute value of
GWC from the early-time signal.
Table 1. Gaant chart showing the timing of time-domain reflectometry (TDR) and ground-penetrating radar (GPR) surveys and irrigation at the field
site from Thursday, 28 Aug. 2014 (Day 1), to Monday, 1 Sept. 2014 (Day 5).
Day 1
Event
AM
TDR
GPR
Irrigation
Day 2
PM
Overnight
X
X
AM
Day 3
PM
AM
Day 4
PM
AM
Day 5
PM
AM
X
X
X
X
X
X
X
X
PM
X
Soil sampling
VZJ | Advancing Critical Zone Science
X
p. 5 of 9
Fig. 4. Representative ground-penetrating
radar trace (black) and corresponding envelope
(gray) from a line collected in the field. Shaded
area is the first positive half cycle.
We derived a site-specific empirical relationship between our TDR
measurements of t and our GWC values based on the 5- to 10-cm
soil cores. This equation was used to convert all TDR measurements of t into GWC values (in %). The TDR values used for
the calibration ranged from 11 to 22% GWC, which encompasses
most of the saturation levels observed during the study.
66Results
Results from Time-Domain Reflectometry
The results from TDR data collection are shown in Fig. 5. The
background TDR data set collected on Day 1 shows a mean and
median GWC of 13% within the grid. A few areas are around 15%
GWC, probably due to the effects of the storm that took place the
prior week. The first data set after irrigation, on Day 2, shows an
increase in the GWC in the wetted areas, both the area irrigated
by sprinkler and the area irrigated by the box infiltrometer at grid
coordinate (9,17). The area irrigated by the sprinkler ranged from
13 to 20% GWC, peaking between 2 and 3 m from the sprinkler.
The wetted area at grid coordinate (9,17) measured 19% GWC.
The data set collected on Day 3 is very similar to the Day 2 data set.
On Day 4, the GWC values began to decrease, with the area irrigated by sprinkler ranging from 12 to 18%, but with much more
of the area measuring in the 16% range than on Days 2 or 3. On
Day 5, the area near the sprinkler increased to around 23% GWC.
Early-Time GPR Analysis and Soil Sampling
Figure 6 shows the results of the early-time GPR analysis. The
plots show the AEA−1 of the first positive half cycle of the traces
derived from averaging blocks of seven traces. The Day 1 data
set, the background measurement, averaged an AEA−1 value of
5.91 ´ 10−5. There were a few higher AEA−1 areas around the edge
due to rainwater leaking under our tarp during the previous week’s
rainstorm. On Day 2, the first data set collected post-irrigation,
the values in the unirrigated region were mostly unchanged from
VZJ | Advancing Critical Zone Science
Day 1. In the area of the irrigation, the AEA−1 values jumped
to the range of 6.6 ´ 10−5 to 1.26 ´ 10−4 . The AEA−1 values
remained elevated throughout the remainder of the study period,
as infiltration occurred slowly in the clay-rich soil. However, the
average AEA−1 value of the field site on Day 2 was 7.10 ´ 10−5,
which decreased to 6.52 ´ 10−5 by Day 5.
The results from gravimetric analysis of the soil cores collected at
the 5- to 10- and 15- to 20-cm depths can be seen in Fig. 7 compared with our GPR AEA−1 data. The 5- to 10-cm samples ranged
from 8 to 22% GWC, and the 15- to 20-cm samples ranged from 9
to 19% GWC. The TDR output t, which represents travel time of
the signal along the TDR rods, was converted to GWC by correlating the data with the 24 5- to 10-cm-depth soil core measurements
collected on Day 5 of the experiment. The obtained relationship
is linear:
GWCcore = 3.8297 t + 0.2479 [8]
and has an R2 value of 0.85. There is no evidence in the data to suggest that a nonlinear equation would be more appropriate within
the range of GWC used to obtain Eq. [8]. Because we do not use
the equation to extrapolate to higher or lower GWC values, a linear
relationship is appropriate.
66Discussion
Discussion of Background Measurements
Our initial measurements at the field site, taken a month before
our experiment, demonstrated the ineffectiveness of traditional
methods of using GPR to measure the SWC in clay-rich soils. The
WARR survey (Fig. 2) showed that the ground wave method cannot
be used to determine SWC at this site due to the significant attenuation caused by the high clay content of the soil. The ground and
air waves do not separate until antenna offsets where the ground
p. 6 of 9
Discussion of Early-Time Method
wave has attenuated to the point that it is difficult to accurately pick
(Fig. 2b). These results suggest that during wet soil conditions with
higher signal attenuation, use of the ground wave method would
have been even more challenging. No reflections are visible beneath
the direct signal, rendering the reflection method ineffective as well.
This is further evidenced by the common-offset data collected at
the site prior to the irrigation experiment (Fig. 3). In contrast, the
early-time method appears to be a viable GPR methodology to map
the spatial variability in shallow SWC at this site.
Cross-plots of the GPR AEA−1 data (Fig. 7) with independently
obtained soil core GWC values show the expected relationship
(Fig. 7a and 7b), supporting the argument that the early-time
method provides a viable way to use GPR in clay soils to map GWC
variations. In the irrigated areas, a significant jump in AEA−1 is
observed, in line with the results of previous researchers (Pettinelli
et al., 2014; Ferrara et al., 2013). We also see a decrease in the
AEA−1 values by the end of the experiment, probably indicative
Fig. 5. Time-domain reflectometry data sets from (a) Day 1, using a
contour interval of 0.5%, and (b–e) Days 2 to 5, using a contour interval of 1%, with white diamonds showing the measurement locations.
The grid was acquired via kriging, using a linear variogram model with
a custom slope and nugget for each day.
Fig. 6. (a) Day 1 to (e) Day 5 ground-penetrating radar inverted average envelope amplitude (AEA−1) data plotted in unitless amplitude
units. The grid was acquired via kriging, using a linear variogram
model with a custom slope and nugget for each day.
VZJ | Advancing Critical Zone Science
p. 7 of 9
seen in the GPR data: increasing subsurface water content corresponding with increasing AEA−1 (Fig. 7c). The TDR data
correlate better with the GPR data than do the soil core data. This
is potentially because the TDR measured the top 12 cm of the
subsurface, whereas the soil cores were taken from discontinuous,
5-cm sections of the subsurface, none of which began at the surface.
However, the fact that there are many more TDR measurements
(101 per day) than soil core measurements (24 collected in 1 d)
probably improved the correlation between the GPR and TDR
data as well. The better correlation between GPR AEA−1 and
TDR data exists even if we use the volumetric water content estimate provided from the probe, and thus does not directly result
from our linear fitting of TDR to soil GWC values.
The soil sample data also confirm the viability of the early-time
method in this environment, as well as giving some information
toward the depth of investigation of the method. Where the soil core
GWC increased, we saw an increase in the AEA−1 of the GPR signal.
This correlation is stronger with the 5- to 10-cm soil samples (Fig. 7a)
than with the 15- to 20-cm soil samples (Fig. 7b), which suggests that
the early-time signal of our antennae in this soil is primarily sensitive to the top 15 cm (approximately 1/8 of the GPR wavelength) of
the subsurface (although there is still a positive, weaker correlation
between the GPR and 15- to 20-cm soil sample data).
66Conclusions
Fig. 7. Ground-penetrating radar inverted average envelope amplitude
(GPR AEA−1) vs. the gravimetric water content (GWC) calculated
from soil samples collected via a standard size soil sampling ring at
depths of (a) 5 to 10 and (b) 15 to 20 cm and (c) GPR AEA−1 vs. 101
time-domain reflectometry data points on Day 5.
of a small amount of evapotranspiration and infiltration of water
to below the GPR region of influence.
Unfortunately, the early-time methodology has not been demonstrated to distinguish changes in conductivity from changes in
dielectric permittivity in a field setting. The irrigated water may have
had a different electrical conductivity than the pre-irrigation groundwater, and the increase in water content would also cause an increase
in the bulk relative permittivity of the unsaturated zone. Electrical
resistivity monitoring in conjunction with GPR could potentially
help isolate changes due to dielectric permittivity. Despite this limitation, the good correlations in Fig. 7 suggest that GPR AEA−1 values
could be converted to GWC values using a calibration equation.
The TDR data show an increase in GWC in the sprinkler-irrigated region from Day 1 to Day 2. These data confirm the trend
VZJ | Advancing Critical Zone Science
This irrigation experiment demonstrates the viability of the
early-time GPR method for estimating the spatial variability in
SWC in clay soils at the field scale, where other GPR methods
fail. The WARR analysis and common-offset ground wave analysis were unable to provide a reasonable method to measure the
SWC at our field site. Irrigated areas of the field site corresponded
with increases in GPR AEA−1. What percentage of the change
in AEA−1 was due to changes in permittivity vs. conductivity is
unclear. However, our data show a strong correlation between
AEA−1 and independent GWC measurements from both TDR
and gravimetric soil sample analysis. The GPR data correlate best
with the TDR data and the 5- to 10-cm soil core data. This suggests that our GPR measurements are most sensitive to the top
15 cm of the subsurface. Continuous soil cores from a larger range
of the subsurface would improve our understanding of the depth
of investigation of GPR at this site. The early-time method opens
up new avenues of research using GPR in clay-rich soils and can
benefit from further laboratory and field investigation.
Acknowledgments
The National Science Foundation and Australian Academy of Science funded this research
through an East Asia and Pacific Summer Institutes for US Graduate Students grant. The Institute for Future Environments at the Queensland University of Technology (QUT) as well as at
the Samford Ecological Research Facility supported this research by providing access to equipment and facilities. Marcus Yates, David Rowlings, and Nicholas Josephs provided invaluable
assistance with the field work. Remke Van Dam acknowledges support from Peter Grace of the
Healthy Ecosystems and Environmental Monitoring group at QUT. Jason Muhlbauer, PhD
candidate at University of Tennessee, Knoxville, assisted with the creation of the figures. Finally, we would like to thank our peer reviewers, including Dale Rucker, David Nobes, and an
anonymous reviewer, for their constructive feedback that greatly improved our paper.
p. 8 of 9
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