Insights into soil water use
through interpreting moisture sensor data
Colin S. Campbell1,2 , Lauren L. Bissey1 , Douglas R. Cobos1,2 , Kelsey M. Dunne1 ,Gaylon S. Campbell1 and David J. Brown2

Abstract: Data interpretation is a major challenge given
the increasing number of wireless soil moisture sensor networks. Although sometimes ignored, site-specific information like topography, morphology, and soil type are key
to understanding soil moisture behavior. To improve ourunderstanding of soil moisture sensor behavior, we evaluated three sites that were constituents of a 12 node wirelessnetwork deployed across a 37 ha field in locations selected
for their geographic diversity. Each site had previously
been characterized with a detailed soil profile analysis.
Continuously monitored water content sensors were installed at 5 depths at 30 cm spacing starting at 30 cm. Sitespecific characteristics significantly affected soil water dynamics. A hardpan in the soil explained unexpected differences in soil water with depth at one site, while low landscape position along with subsurface drainage explained
another. Diurnal redistribution of water could also be seen
at a third location down to 150 cm. Combining data from
intensely monitored sites with plant type, topography, andsoil morphology greatly improve our interpretation of soil
moisture data in the field.
Key Words : soil moisture, water content, data analysis,
soil water use

1. Introduction
Soil water content varies considerably over space andtime. One of the current challenges in soils research is toquantify those differences at large scales and apply themto understanding watershed hydrology, ground water contamination, and plant water availability and use (Robinson et al., 2008; Vereecken et al., 2008). New inexpensive
soil water volumetric water content (VWC) sensors makesuch ubiquitous measurements possible, but do not solve
the problem of large amounts of data that require interpretation. Correlating typical sensor responses in the field tosite knowledge is crucial to the success of these large-scale
projects.
1 Decagon Devices, Inc., 2365 NE Hopkins Ct., Pullman, WA 99163 USA.

Corresponding author: Colin S. Campbell
2 Department of Crop and Soil Science, Washington State University,
Pullman, WA 99163 USA
2009 年 10 月 5 日受稿 2010 年 1 月 26 日受理
土壌の物理性 114 号, 19–22 (2009)

Studies have already tested the accuracy and repeatability of soil moisture sensors (Bogena et al., 2009; Kizito
et al., 2008) as well as their viability in distributed wireless networks (Robinson et al., 2008). The sheer availability of the sensors, as well as the simple application ofa distributed network can easily lead to large amounts ofdata that must be archived, displayed and evaluated. Inherent to these datasets will be three kinds of phenomenathat must be dealt with correctly for proper interpretation:
real data whose trends match the expectations of site, real
data whose trends go against the expectation of the site but
represent fragments of unexpected information about thesystem, and artifacts, such as temperature dependence andsensor failure, that should be filtered from the data. The objective of this note is to provide some perspectives on soil
moisture measurement by discussing typical soil moisturedata as a learning and evaluation tool for future monitoring
projects.

2. Background
The Cook Experimental Farm at Washington State University is a 37 ha research farm which serves to study many
aspects of no-till crop production. Started in 1999, therain-fed farm grows a rotation of crops, divided between
three major sections of the Farm. Precipitation averages
510 mm annually, with the greater portion occurring in
the winter and spring. Soils on the farm are Mollisols(Palouse (Ultic Haploxerol), Naff (Typic Argixeroll), andThatuna (Oxyaquic Argixeroll)) with argillic horizons andhard pans in some locations. The terrain is moderately
hilly with low-lying areas receiving considerable runoff.
A study is underway to better understand spatially distributed soil water and evaluate the relationship between
soil moisture dynamics and morphological features. To
that end, 12 wireless nodes were installed at various points
in the fields using a stratified random procedure to ensure
variability and sufficient distance between sensor locations(Fig. 1). At each site we buried VWC, temperature, andelectrical conductivity (EC) sensors (ECH2 O-TE and 5TE,
Decagon Devices, Inc., Pullman, WA) at 30 cm increments

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data were collected continuously at 1 hour increments(Em50 Datalogger, Decagon Devices, Inc.) and transferred
via a central collection point (DataStation, Decagon Devices, Inc. and CR850, Campbell Scientific, Inc, Logan,
UT) and cell modem gateway (AirLink, Campbell Scientific, Inc.) to the internet. Gaps in data were generated
from regular infield operations (spraying, fertilizing, etc.)
as well as harvest when the data collection systems were
removed from the field. General environmental data, suchas precipitation, were collected at a central location in thefield. Slope, aspect, elevation, and morphology were measured directly at each of the 12 sites.
Fig. 1 Cook Experiment Farm at Washington State University.
This study focuses on three sites (marked Site 1, 2, and 3); a subset of the 12 measurements sites (stars) in the project. Contour
lines and shading indicate possible drainage paths for water toward Site 2.

In our analysis, we consider three of the sites in depth tolearn more about sensor behavior. The first site was locatednear a hilltop with a southerly exposure and was plantedwith winter wheat (Triticum aestivum) the preceding fall.
Augering for sensor installation unearthed a hardpan layer
between the 120 and 150 cm sensors. The location of Site
2 was at the bottom of a large drainage area (Fig. 1) wherea robust crop of triticale (Triticale hexaploide) was growing; no soil anomalies were exposed in this location. Also
planted to winter wheat, the third site was located on a toe
slope and was more typical of the other nine sites in the
study.

3. Data Interpretation and Discussion
Fig. 2 Changes in VWC with time at Site 1. The sensor at
120 cm, located just above a hardpan layer in the soil, shows thelargest change over the summer dry-down period.

Fig. 3 Changes in VWC with depth (convention: negative values indicate depths below soil surface) for the same time period
at Site 1.

from 30 to 150 cm below the surface. The sensor nearest
the surface was installed into the sidewall of a 45 cm deep
trench; the other four sensors were installed in the base ofa 5 cm auger hole at their appropriate depth (one per hole)and backfilled and repacked with native soil (with care tolimit air gaps).
Soil moisture, temperature, and electrical conductivity

Water use by the winter wheat at Site 1 over the summerof 2007 was similar to what we expected for the silt loam
soil where they were installed (Fig. 2). Rain fed wheat
is known to root deeply in the soil and Fig. 2 shows thecontinuous progress of the root extraction down throughthe soil to 1.2 m deep. Curiously, the 120 cm sensor readmuch higher than the other four at the time of installation.
Further, the initial reading of 0.50 m3 m−3 is certainly near
the upper limit for the silt loam soils of the Cook Farm.
Although several things might have caused this (poor installation, low-density soil, and air gaps around the sensor), during installation we observed a hardpan between
120 and 150 cm that may be the root cause. It is likely that
infiltrating water ponds above the hardpan and keeps thesoil closer to saturation compared to other depths.
Moisture content change with depth due to crop water
use is also somewhat unexpected (Fig. 3). Water is takenfrom the 30 and 60 cm levels as expected; as the 30 cm sensor reached a minimum, the 60 cm sensor begins to drop in
earnest. Initially, moisture at 90 cm shows a similar trend,
but does not drop as low as those at 30, 60, or 120 cm. The
reason for this is unclear. At the same time, the water content at 120 cm is dropping quickly, showing a higher preference for water at that depth compared to 90 cm, possible
because of the abundant water above the hardpan. The 150
cm sensor exhibits very little change across the entire sum-センサー特集

研究ノート：Insights into soil water use through interpreting moisture sensor data

Fig. 4 Winter recharge at four depths (60 cm sensor was disconnected) at Site 1. Precipitation events are visible at the 30 cm
sensor, while recharge at deeper depths occurs much more slowly.
The soil surface was devoid of living plants until after 1/4/08.

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to a change in crop type (triticale vs. wheat), the vitalityof the crop throughout the summer (personal observation)
indicated it had adequate water. The gradual increase at 90
and 120 cm indicate water was coming from somewhereother than the soil surface as there was no change in thesensors above them. Interestingly, the 150 cm sensor does
not respond in a similar manner suggesting water may flow
more easily in some layers of the soil than others. These
data may be consistent with information gathered at Site
1 where water was shown to pond at certain depths in thesoil and have poor connectivity to lower regions.
Often it is difficult to tell the difference between a functional soil sensor and one giving meaningless data. The
30 cm sensor at Site 2 (Fig. 5.) appears to be malfunctioning at the beginning of 2008. After two rain eventsin late fall, data increases beyond 70 % and becomes erratic, with drops that cannot be explained in soil environment. Although there may be physical explanations forthis (freeze/thaw, air voids created around the sensor byfauna), a more likely explanation is a problem in the sensor itself. This sensor still performed as expected during
the next year (data not shown) suggesting the failure is intermittent.
Several studies have shown soil moisture data that contains a superimposed temperature signal of up to 0.003 m3

Fig. 5 Summer water use at the low-land site (2). After an
initial response to plant use, sensors show water content remains
relatively constant or increases across the summer.

mer. Although this is reasonably deep in the soil, most of
the other sites growing wheat exhibited as much water use
at 150 cm as at the other measurement depths. Although
there are other possible reasons, it is likely that the hardpan not only impeded water movement, but also limited
root growth below that level, thus reducing water uptake(Passioura, 2002).
The winter recharge period in Fig. 4 shows soil moisture
returning to similar VWC levels as the previous year. As
expected, infiltration events can be seen clearly at 30 cm,
but produce only small changes at 90 cm (60 cm sensor
was disconnected). Over time, the upper soil layers exceed
field capacity and drain water down to the 120 cm and finally 150 cm levels. All sensors returned to VWC values
similar to their previous spring values giving confidence
that changes observed are related to soil processes and notancillary effects.
Site 2 requires more interpretation (Fig. 5). Without
knowledge of its landscape position, the reaction of all the
sensors would be perplexing. At 30 and 60 cm, the water content initially decreases similar to Site 1, but flatten
after a rainstorm. Sensors lower in the profile show watercontent that increases at times during the season. Althoughsome of the difference in water uptake could be attributed

Fig. 6 Water content (a) and temperature (b) dynamics at thetoe slope site (3). Although temperature clearly affects the 30 cm
sensor, diurnal variations at 60, 90, and 150 cm (a) are difficult toelucidate. One possible explanation is the roots are redistributing
the water in the root zone at night.

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土壌の物理性m−3 /◦ C or more (McMichael and Lascano, 2003; Or and
Wraith, 1999). Although most sites did not exhibit this behavior, it can be seen in Site 3 at all but the 120 cm depth
at various times during the summer (Fig. 6a). Our initial
reaction was to attribute it to temperature affects on themeasured dielectric. However, Figure 6b shows little or no
diurnal temperature change beyond the 30 cm sensor. This,
coupled with the fact that the fluctuations can only be seen
once VWC begins to decrease (i.e. root water uptake fromthat depth), suggest redistribution of water by plant roots
as a possible explanation for the phenomenon.

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References
Bogena, H., Huisman, J.A., Oberdorster, C. and Vereecken, H.
(2009): Evaluation of a low-cost soil water content sensor for
wireless network applications. J. Hydrol., 344: 32–42.
Kizito, F., Campbell, C.S., Campbell, G.S., Cobos, D.R., Teare,
B.L., Carter, B. and Hopmans, J.W. (2008): Frequency, electrical conductivity and temperature analysis of a low-cost capacitance soil moisture sensor. J. Hydrol., 352: 367–378.
McMichael, B. and Lascano, R.J. (2003): Laboratory evaluationof a commercial dielectric soil water sensor. Vados Zone J., 3:
650–654.

4. Summary

Or, D. and Wraith, J.M. (1999): Temperature effects on soil bulk

Large amounts of data that require intensive processing
are a result of new inexpensive sensors and more sophisticated dataloggers. Although the opportunity for more intensive sampling can lead to better understanding of thenatural environment, correct data interpretation of thesedata is a necessary precursor. Our review of data from the
Cook Experiment Farm shows soil moisture often does nottrend in the manner we expect. Things like landscape position, soil morphology, and plant response change watercontent data in ways that would not be expected by simply
looking at precipitation data or soil surface features. Successful analysis of these characteristics can lead to higher
quality data analysis and a better understanding of how water behaves in the soil.

要dielectric permittivity measured by time domain reflectometry: A physical model. Water Resour. Res., 35: 371–383.
Passioura, J. B. (2002): Soil conditions and plant growth. Plant,
Cell, and the Environment, 25:311–318.
Robinson, D. A., Campbell, C.S., Hopmans, J.W., Hornbuckle,
B.K., Jones, S.B., Knight, R., Ogden, F., Selker, J. and Wendroth, O. (2008): Soil moisture measurement for ecologicaland hydrological watershed-scale observatories: A Review.
Vadose Zone J., 7: 358–389.
Vereecken, H., Huisman, J.A., Bogena H., Vanderborght,
J., Vrugt, J.A. and Hopmans, J.W. (2008):

On thevalue of soil moisture measurements in vadose zone hydrology: A review. Water Resour. Res. 44: W00D06,
doi:10.1029/2008WR006829.

旨

ワイヤレス土壌水分センサーネットワークの数が増加してくると，そのデータ解釈が主要な課題となってくる．土壌水分の特性を理解するためには，現状では無視されている地形，形態，土の種類などの現場特有の情報が重要となる． 現場の条件に応じた土壌水分センサーの特性を理解するため，我々は地形が異なる区域内にある 37 ha の圃場の 3 つのサイトを評価した．これらのサイトには 12 個のノードを持つワイヤレスネットワークが配備されている．各サイトでは事前に詳細な土壌断面調査が行われている．連続測定する土壌水分センサーを深さ 30 cm から 30 cm 間隔で 5 深度に埋設した．観測の結果，現場特有の特徴が土壌水分の動きに大きく影響を与えることがわかった．サイト 1 では深さ方向に対する想定外の土壌水分の違いを土壌中の硬盤により説明できたが，サイト 2 では地下排水を伴う低地によって説明できた．サイト 3 では，150 cm の深さまで水分の日変動が見られた．集中観測地点のデータと植生，地形，土壌形態を合わせることにより，圃場における土壌水分データの解釈が飛躍的に向上する．
キーワード：土壌水分，含水率，データ解析，土壌水分利用