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Agricultural Water Management 132 (2014) 111– 119

Contents lists available at ScienceDirect

Agricultural Water Management

j ourna l h omepage: www.elsev ier .com/ locate /agwat

aboratory and field assessment of the capacitance sensors Decagon0HS and 5TE for estimating the water content of irrigated soils

ernando Visconti a,b,∗, José Miguel de Paza, Delfina Martínezb, Ma José Molinab

Instituto Valenciano de Investigaciones Agrarias-IVIA (GV), Centro para el Desarrollo de la Agricultura Sostenible-CDAS, Crta. Moncada-Náquera Km 4.5,6113 Moncada, València, SpainCentro de Investigaciones sobre Desertificación-CIDE (CSIC, UVEG, GV), Crta. Moncada-Náquera Km 4.5, 46113 Moncada, València, Spain

r t i c l e i n f o

rticle history:eceived 22 July 2013ccepted 13 October 2013vailable online 8 November 2013

eywords:rrigated clayey soilsielectric permittivity measurementslectrical conductivityodel calibration

ield validationE Spain

a b s t r a c t

Capacitance sensors such as Decagon 10HS and 5TE are increasingly used for soil water content (�) esti-mation. However, their reliability and limitations in clayey soils irrigated with saline waters have notbeen completely characterized under field conditions. Four levels of soil water content were combinedwith six levels of soil salinity in twenty-four pots to assess the performance of both sensors in a widerange of soil salinities. A simplified power-law dielectric mixing model was calibrated in the laboratoryto estimate the � of a clay loam soil from the measurements of apparent dielectric permittivity (εb) per-formed with both sensors. The calibrated equation was subsequently validated for the estimation of � attwo depths in six irrigated salt-threatened soils with clayey textures in SE Spain. The 10HS sensor pro-vides higher estimations of εb than the 5TE. Besides, the 5TE sensor was more sensitive to soil salinity.Consequently, a different calibration was carried out for each sensor. When all the soil salinity treatmentswere included in the calibrations, the results were poor. However, for soil apparent electrical conduc-tivities below 1.7 dS m−1 the 5TE sensor could be calibrated with low prediction errors, and with the

calibration parameters b0 and b1 very close to their characteristic values in clayey and mineral soils. Infield testing, the 5TE sensors calibrated with the obtained equation provided average correct estimationswith an error of ±0.05 m3 m−3. On the contrary, the 10HS sensor overestimated the soil water content by0.07 m3 m−3 on average. The proposed simple calibration equation for the 5TE sensor can be reliably usedunder field conditions to estimate � of irrigated clayey soils up to an apparent electrical conductivity of1.7 dS m−1.

. Introduction

The sustainable management of irrigation demands an accurateonitoring of the soil water content (�). The estimation of � can be

one using dielectric sensors of the frequency domain type such ashose based on frequency domain reflectometry (FDR) and capaci-ance. They offer a practical and cost-effective alternative to otherevices based on neutron moderation and time domain reflectom-try (TDR), which are regarded as the most accurate methods for

oil water content estimation (Topp, 2003; Dobriyal et al., 2012).

All dielectric sensors (TDR, FDR, capacitance, etc.) estimatehe apparent dielectric permittivity (εb) of a soil volume, which

Abbreviations: DDL, diffuse double layer; FDR, frequency domain reflectometry;MSD, root mean square deviation; TDR, time domain reflectometry; εb, appar-nt soil dielectric permittivity; �, soil volumetric water content; �b, apparent soillectrical conductivity.∗ Corresponding author. Tel.: +34 963 424 218; fax: +34 963 424 001.

E-mail address: fernando.visconti@uv.es (F. Visconti).

378-3774/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.agwat.2013.10.005

© 2013 Elsevier B.V. All rights reserved.

depends mainly on its water content (Evett, 2007). The Decagon10HS and 5TE sensors are two widely known sensors of the capac-itance type (Decagon Devices, 2012a,b). They come with empiricalmanufacturer equations to estimate � from εb, which are regardedvalid for a wide range of soils. One of these equations is the well-known Topp equation (Topp et al., 1980). Despite being empiricaland originally developed for mineral soils using TDR, the merit ofthe Topp equation was that it showed that the soil water contentdepends only on the apparent dielectric permittivity of the soil.However, each dielectric method for � estimation exploits a differ-ent electrical phenomenon to estimate the soil apparent dielectricpermittivity (Munoz-Carpena, 2012), and, therefore, an equationvalid for TDR is not necessarily appropriate for use with anothersensor.

It has been also acknowledged that the apparent dielectric per-mittivity estimated by dielectric sensors is not only dependent on

the soil water content, but also on other soil properties such as soilsalinity, and soil texture due to the so called dielectric losses (Toppet al., 2000). Dielectric losses associated to salinity are caused by thesoil apparent electrical conductivity (�b), whereas clayey textures

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12 F. Visconti et al. / Agricultural Wa

ause Maxwell-Wagner and bound water dielectric losses (Hasted,973). These dielectric losses are frequency dependent and par-icularly important for sensors working at frequencies between 1nd 200 MHz (Hilhorst and Dirksen, 1994), which frames the fre-uency used by most capacitance sensors such as the 10HS and 5TE70 MHz).

The effect of �b on Decagon capacitance sensors has been pre-iously studied (Kizito et al., 2008), and specifically the 10HS andTE sensors have been tested (Varble and Chávez, 2011; Kargas andoulis, 2012; Schwartz et al., 2013). However, most of this work haseen carried out in the laboratory, and therefore its applicability toctual field conditions, where other factors such as soil cracking,toniness, roots and fauna act, has not been addressed. Specifi-ally, the recently developed 10HS sensor has been tested in theaboratory under non-saline conditions (Kargas and Soulis, 2012).owever, to know the adequate range of application of this sensor

n irrigated soils with slight to moderate salinity, first it would beecessary to know at what threshold of �b the dielectric losses byalinity are no longer negligible. Next, it should be calibrated and,nally, it should be validated under field conditions.

The objective of this work was to assess, under laboratory andeld conditions, the performance of the Decagon 10HS and 5TE sen-ors to estimate the water content of agricultural irrigated soils oflayey texture in SE Spain, and, subsequently, to discuss the impli-ations of these findings for irrigation management.

. Theory

.1. Apparent soil dielectric permittivity

The 10HS and 5TE sensors use the capacitance technique form-ng a capacitor with their prongs and the soil in between. Anlternating square potential is applied through the sensor circuitsing an oscillator. The capacitance (C) of the sensor is thus deter-ined from the average time (t) needed to charge the capacitor

rom a starting voltage V0 to a voltage V, where R is the series resis-ance of the sensor circuit and E is the applied voltage (Bogena et al.,007):

= t

R ln[V0 − E/V − E](1)

The capacitance is related to the apparent dielectric permittivityf the soil (εb) through:

= C ′ + gε0εb (2)

here ε0 is the vacuum dielectric permittivity (ε0 = 8.854 pF m−1),nd g is a geometrical factor in length units that depends on thehape, dimensions and separation of the sensor prongs. The para-itic capacitance from the circuit connections (C′) contributes alsoo the resultant capacitance of the sensor and, therefore, g and C′ areointly calibrated by the manufacturer taking measurements of theensor capacitance in several liquids of known dielectric permittiv-ty that extend over the range of usual variation of εb (Rosenbaumt al., 2010).

The apparent dielectric permittivity of soils can be viewed ashe result of the average contribution of the dielectric permittivityf the main soil constituents: (i) soil solids (εs ≈ 3–5), (ii) soil waterεw ≈ 80), and (iii) soil air (εa ≈ 1). The contribution of the dielectricermittivity of the soil water splits, in turn, into the permittivity ofhe free water within the soil pores (εfw), and the permittivity ofhe bound water (εbw) associated to the diffuse double layer (DDL)

f the soil particles. A semi-empirical power-law dielectric mixingodel (Sihvola, 1999) takes account of this model:

mb = (ϕ − �)εm

a + (1 − ϕ)εms + (� − �bw)εm

fw + �bwεmbw (3)

nagement 132 (2014) 111– 119

where ϕ is the soil porosity, and �bw is the volumetric bound watercontent.

Depending on soil particle shape and arrangement, the mparameter presents a value between −1 and 1. A specific value of½ has been found consistent with a linear refractive index mix-ing model (Whalley, 1993), which has led to a simplified dielectricmixing model:

� = (ε1/2b

− b0)

b1(4)

In Eq. (4) the parameters b0 and b1 are equal to (1 − �b/�r)(εma −

εms ) + εm

s + (εmbw − εm

fw)�bw and (εmfw − εm

a ), respectively, and ϕ hasbeen expressed as function of the soil bulk (�b) and soil particle(�r) densities (ϕ = 1 − �b/�r). In practice, b0 and b1 are found bycalibration of � against ε1/2

bdata. Apart from these models there are

purely empirical approaches for the estimation of � on the basis ofεb:

� = aε3b + bε2

b + cεb + d (5)

which is the option used in the 10HS and 5TE sensors. Specifi-cally, the 5TE sensor uses the third order polynomial known asthe Topp equation (Topp et al., 1980) with empirical parametersa = 4.3 × 10−6, b = −5.5 × 10−4, c = 2.92 × 10−2 and d = 5.3 × 10−2.

The 10HS sensor also uses a third order polynomial on thesensor signal measured in “raw counts” with a = 1.17 × 10−9,b = −3.95 × 10−6, c = 4.9 × 10−3 and d = −1.92 (Decagon Devices,2012a). These coefficients are different from the ones used in thefirst version of the manual: a = 0, b = 3.13 × 10−7, c = −1.47 × 10−4

and d = −5.82 × 10−2 (Decagon Devices, 2008a).

2.2. Apparent soil electrical conductivity

The 5TE sensor also measures the apparent soil electrical con-ductivity (�b). It forms a conductivity cell with a pair of metalscrews in two adjacent prongs of the sensor in contact withthe soil. The resistance (R) of the soil between the screws isdetermined from the voltage drop across them similarly to anohmmeter. The apparent soil electrical conductivity (�b) is thencalculated from the resistance and the cell constant (k), whichhas been previously calibrated using a KCl aqueous standard,according to:

�b = k

R(6)

The soil acts as a conductor because of the presence of ionsable to move in an applied electrical field. The soil conductiontakes place mainly through two parallel conductors (Rhoades et al.,1999). These are the ions within the water filled pores, and the ionswithin the soil DDL.

The soil apparent dielectric permittivity as measured bydielectric methods depends on the angular frequency of thecircuit oscillator (ω), the temperature (T) and the soil appar-ent electrical conductivity. The following equation (von Hippel,1954):

εb = ε′(ω, T)2

⎡⎣1 +

√(ε′′(ω, T) + �b(T)/ωε0

ε′(ω, T)

)2

+ 1

⎤⎦ (7)

has been adapted to describe this relationship in soils (Robinson

et al., 2003; Schwartz et al., 2009), where ε′(ω, T) is the static dielec-tric permittivity, ε′′(ω, T) is a dielectric loss factor for the relaxationof pure water, and �b(T)/ωε0 is a dielectric loss factor for electricalconduction.

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F. Visconti et al. / Agricultural Wa

. Materials and methods

.1. Decagon sensors

The 10HS and 5TE have evolved from, respectively, the EC-5Decagon Devices, 2012c), and ECH2O-TE (Decagon Devices, 2008b)ensors. The 10HS has two 14.5 cm long prongs, spaced 3.3 cm,hich gives an apparent permittivity measurement volume of

dm3. The 5TE has three 10 cm long prongs with 1 cm spacingetween adjacent prongs. While the conductivity cell is formed byhe centre and one side prong, the capacitor is formed by the cen-re and the other side prong, which gives an apparent permittivity

easurement volume of 0.3 dm3 (Decagon Devices, 2012a). Bothensors use an oscillator operating at 70 MHz for measuring thepparent dielectric permittivity of the soil. Additionally, the 5TE has

two-electrode array and a built-in thermistor, which respectivelyeasure the apparent soil electrical conductivity and temperature.

he �b output from the 5TE is given at the reference temperaturef 25 ◦C using the temperature correcting factors of U.S. Salinityaboratory Staff (1954).

.2. Selection of sensors

Thirteen 5TE and twelve 10HS sensors were tested in sixqueous saline solutions differing in their salt concentration and,ence, electrical conductivity at 25 ◦C (�w) of <0.001, 1.1, 3.5, 5.7,.8, and 9.9 dS m−1, and with constant sodium adsorption ratioSAR = [Na+]/([Mg2+] + [Ca2+])1/2 = 5 (mmol L−1)1/2) for the 1.1 to.9 dS m−1 solutions, which were obtained dissolving adequatemounts of reagent grade sodium and calcium chlorides (NaCl andaCl2·2H2O) in deionized water. The sensors were completely sub-erged into the solutions and their dielectric permittivity (10HS

nd 5TE), electrical conductivity (5TE) and temperature (5TE) mea-ured.

.3. Study area

Following the World Reference Base for Soil Resources (WRBSR,998), the soils used in this study are classified as Calcaric FluvisolsOrtiz et al., 2003). They are high in calcium carbonate equiva-ent, low in organic matter content, slightly saline, and with clayontents ranging from 27 to 46% (Table 1). The climate of the studyrea is classified as semiarid Mediterranean, with annual averagesf 335 mm for rainfall, 17.9 ◦C for temperature, 1195 mm for refer-nce evapotranspiration, and 10 h day−1 for sunshine (SIAR, 2012).

.4. Soil material to fill the pots

The soil was taken from an agricultural plot cropped to citruszone 30, northern hemisphere UTMx: 702801, UTMy: 4221398, z:, Datum European 1950) located in Guardamar del Segura (Ali-ante province, SE Spain). In total, twenty samples of around 6 kgach were taken from the 0–10 cm depth layer during spring 2011.hey were put in pots and transported to the laboratory, where theyere air-dried, passed through a 2 mm mesh sieve, and thoroughlyixed in a single composite sample.

.5. Sensor measurements in the pots

Twenty-four pots of conical shape with a permeable filter paperut at the bottom were filled with 3500 g of air-dried soil. Theimensions of each pot were 22 cm height, and 7.75 and 6.4 cm

ajor and minor radii, respectively. The 24 pots were split into 6

roups of four each. Each group was equilibrated with one of thequeous saline solutions previously obtained for the testing of theensors (Section 3.2).

nagement 132 (2014) 111– 119 113

The pots were filled following a standardized procedure toensure a homogeneous soil bulk density within and between pots,and to avoid the presence of trapped air within the soil. The fillingprocess was carried out by careful addition of the soil in five frac-tions of 700 g each. After the first soil fraction was added to the pots,they were put into a bucket. The corresponding saline solution wascarefully added to the bucket until its level was two centimetresbelow the soil surface. Once the soil had been completely wettedfrom below, another soil fraction was added to the pots and thesaline solution was raised again to 2 cm below the soil surface.

Once completely filled, soil and saline solutions were left toequilibrate at saturation for 24 h. Afterwards, the salt solutionswere removed from the buckets by gravity using a siphon. Then,the soils were leached with their corresponding saline solutionsuntil constant electrical conductivity of the leachates. The leach-ing was carried out at constant head using a dropper adjusted toa hydraulic conductivity of 40 mm h−1. Evaporative losses wereavoided by covering the soil surface with a plastic film.

Following equilibration, the pots were left to drain, and after-wards they were put into a forced air oven under a controlledtemperature of 30 ◦C. In this way it was assured that the waterwas evenly distributed within the pots (Wilczek et al., 2012). Next,every 24 h one pot per salinity treatment was taken out of the ovento perform the measurements of apparent dielectric permittivity,apparent soil electrical conductivity, and temperature with the 5TEsensor, and apparent dielectric permittivity with the 10HS sensor.To take the measurements, the 5TE and 10HS sensors were com-pletely inserted vertically into the soils, one at a time, to preventinterferences. An Em50 data logger and the ECH2O Utility softwarewas used to collect the data (Decagon Devices, Inc©, Pullman, WA).

3.6. Determination of the reference soil water content in the pots

The gravimetric water content of the air-dried soil used to fill thepots was determined before the experiment, and the exact quantityof dry soil in each pot was calculated. After the sensor measure-ments, the pots were weighted and the gravimetric water contentin each pot was calculated. Next, three replicated measurements ofthe depth to the soil surface from the rim of the pots were takento assess the soil volume. On the basis of these data the volumetricwater content (�) of each pot was calculated.

3.7. Sensor measurements and soil sampling in the plots

The soil apparent dielectric permittivity, apparent electricalconductivity and temperature at two depths (15 and 45 cm) of sixplots in commercial production in the traditional irrigated area ofthe Vega Baja del Segura and Camp d’Elx (SE Spain) were monitoredsince summer 2011 till winter 2013 with 10HS and 5TE sensors.The irrigation in the plots was carried out by trickle (5) and by sur-face (1), and the irrigation water presented electrical conductivityat 25 ◦C between 1.3 and 3.0 dS m−1 (Table 2). In the trickle irrigatedplots, the sensors were buried under a drip emitter close to the cen-tre of the plot. The soil sampling was carried out four times duringthe monitoring, at three points below other drip emitters close tothe monitored one. The samples were taken with a steel tube fromthe 5–25 and 35–55 cm soil layers. In the surface irrigated plot thesamples were taken at three equidistant points between tree rowsclose to the monitored point. The soil samples were immediatelyput into a plastic bag, sealed and carried to the laboratory.

3.8. Determination of the reference soil water content in the plots

A representative subsample of each soil bag was weighted, driedat 105 ◦C for 24 h, and weighted again. The gravimetric water con-tent so determined was subsequently multiplied by the soil bulk

114 F. Visconti et al. / Agricultural Water Management 132 (2014) 111– 119

Table 1Main properties of the soils used in this work.

Soil Depth (cm) �se (dS m−1) CCE (%) SOM (%) Sand (%) Clay (%) Textural class USDA

Calibration 0–10 1.80 49 2.0 24 38 Clay loamLas Baias 0–10 1.22 64 2.2 32 34 Clay loamLas Baias 10–30 3.21 72 1.1 33 35 Clay loamLas Baias 30–60 2.16 62 0.8 45 33 Clay loamGuardamar 0–10 9.42 42 2.6 43 29 Clay loamGuardamar 10–30 3.14 41 2.3 38 28 Clay loamGuardamar 30–60 3.94 40 3.6 49 27 Sandy clay loamDaya Nueva 0–10 7.12 39 3.6 16 41 Silty clayDaya Nueva 10–30 7.44 40 4.7 15 41 Silty clayDaya Nueva 30–60 3.08 41 4.1 14 41 Silty clayHeredades 0–10 1.79 39 4.6 21 35 Clay loamHeredades 10–30 1.79 45 2.7 22 39 Clay loamHeredades 30–60 1.93 49 2.3 17 41 Silty clayMudamiento 0–10 3.96 47 6.0 12 40 Silty clayMudamiento 10–30 2.56 49 2.4 12 40 Silty clayMudamiento 30–60 1.78 50 0.8 6 46 Silty clay

�se: electrical conductivity at 25 ◦C in the soil saturation extract at the beginning of the monitoring; CCE: calcium carbonate equivalent; SOM: soil organic matter content.

Table 2Plots monitored to validate the laboratory calibrations.

Code Plot Crop Irrigation Water supply

�25a (dS m−1)

1 Las Baias 1 Pomegranate Trickle Tajo-Segura transfer-Crevillent reservoir 1.32 Las Baias 2 Pomegranate Trickle Tajo-Segura transfer-Crevillent reservoir 1.33 Heredades Mandarin Surface Segura River-Alfeitamí diversion dam 3.04 Guardamar Young orange Trickle Reclaimed wastewater Guardamar del Segura 2.75 Daya Nueva Adult orange Trickle Segura River-Alfeitamí diversion dam 3.0

dvdetdp(diosscswk(lausoi

4

4

sd8T

6 Mudamiento Artichoke Trickle

a �25: mean electrical conductivity at 25 ◦C of the water throughout the year.

ensity at 15 and 45 cm depth (�b) to determine the referenceolumetric water content. The soil bulk density in the plots wasetermined using the core method (Blake and Hartge, 1986). Inach plot two points (1 and 2) were selected. In point 1 a core fromhe soil surface was sampled with a non-commercial steel cylin-er 12.5 cm in diameter and 6.2 cm in height (large cylinder). Inoint 2 commercial steel cylinders 5 cm in both diameter and heightsmall cylinder) were used to obtain cores at 0, 15, 30, 40 and 50 cmepth. To correct for the compression caused by the small cylinder

n the soil sample an equation previously developed for the soilsf the area was used. This was developed based on data from 34ites systematic-randomly distributed in the whole area. In eachite two similar points (1 and 2) representative of the location andlose to field capacity had been selected, and sampled following theame method as previously described in this section. An equationas developed by means of the robust linear regression method

nown as Least Trimmed Squares (LTS) with breakdown point 0.5Rousseeuw, 1984) to relate the bulk density obtained using thearge cylinder to the bulk density obtained using the small cylindert the soil surface in the 34 sites. This equation was subsequentlysed to convert the values of the bulk density obtained with themall cylinder (SCBD) to the bulk density that would have beenbtained with the large cylinder (LCBD) at the 15 and 45 cm depthsn the plots.

. Results and discussion

.1. Selection of sensors

The dielectric permittivity measured with the twelve 10HS sen-

ors were homogeneously distributed between 39.4 and 79.4. Theielectric permittivity measured with the thirteen 5TE sensors was1.9 for the aqueous solutions of 0.001 dS m−1, 7.8 and 9.9 dS m−1.he dielectric permittivity was between 52.2 and 79.8 for the other

Segura River-Callosa diversion dam 2.2

three solutions (1.1, 3.5 and 5.7 dS m−1). The thirteen 5TE sensorscould be split into two groups according to the dielectric permittiv-ity measured for the intermediate values of electrical conductivity(1.1–5.7 dS m−1). One characterized by lower dielectric permit-tivity, and the other characterized by higher values of dielectricpermittivity within the same range. One 5TE sensor representativeof each group (5TE(1) and 5TE(2)) was used for carrying out themeasurements in the pots.

4.2. Estimation of the apparent dielectric permittivity in the pots

A range of volumetric soil water contents from 0.28 to0.48 m3 m−3 was achieved with the laboratory experimental design(Table 3). According to the soil water release curve these watercontents correspond to soil water potentials from saturation toroughly 200 kPa.

The apparent soil dielectric permittivity measured with the10HS and 5TE sensors was, respectively, between 14 and 42, and 14and 59 (Table 3). The 10HS sensor provided values of the apparentdielectric permittivity higher than both 5TE sensors (Fig. 1). Inter-estingly, the differences between the 10HS and 5TE measurementsof εb decreased as �b increased. As shown in Fig. 2, the εb measure-ment with both sensors depended on �b. However, this effect wasmore pronounced for the 5TE sensor than for the 10HS sensor.

For a given electrical conductivity of the wetting water, theapparent permittivity of soils depends on its apparent electricalconductivity (Wilczek et al., 2012). This is a consequence of thespurious relationship between both properties through the watercontent: both εb and �b increase as the water content increases

(Eqs. (3) and (5)). In the case of the 10HS and 5TE, the relationshipbetween both εb and �b could not be explained exclusively on thebasis of this effect, as revealed by the comparison with an idealsensor of the same characteristics (Fig. 2).

F. Visconti et al. / Agricultural Water Management 132 (2014) 111– 119 115

Table 3Statistical summary of the measurements in the pots.

�b (g cm−3) � (m3 m−3) 10HS 5TE(1) 5TE(2)

εb T (◦C) εb �b (dS m−1) T (◦C) εb �b (dS m−1)

N 24 24 24 24 24 24 24 24 24Mean 1.22 0.404 30.0 26.5 24.0 1.50 26.7 27.1 1.36St. Dev. 0.02 0.054 7.0 0.5 8.1 0.84 0.5 11.8 0.74Max. 1.26 0.479 42.3 27.5 41.9 3.46 27.4 59.3 2.98Min. 1.18 0.280 13.6 25.5 14.6 0.32 25.8 14.4 0.45

Fig. 1. Comparison of the soil apparent permittivity (εb) measured with the 10HS and 5TE sensors in the pots classified according to the electrical conductivity of the wetting

s

F

t

olution .

ig. 2. Soil apparent permittivity (εb) against the soil electrical conductivity (�b) for the 10

o the electrical conductivity of the wetting solution

HS, 5TE(1), 5TE(2), and an ideal sensor of similar characteristics, classified according

.

1 ter Management 132 (2014) 111– 119

brcae2iohpi�(wdeEctfTmb�

4t

mtsmb

cts54twvbW

pst

Table 4Calibrations of the simplified power-law dielectric mixing model for estimating thesoil water content.

Sensor calibrated Dataa Model parametersb R2 RMSD

b0 b1

10HS All −1 ± 2 17 ± 7 0.550 0.0355TE(1) All −5 ± 4 23 ± 13 0.397 0.0415TE(2) All −7 ± 5 31 ± 17 0.385 0.04110HS �b ≤ 1.8 dS m−1 0 ± 2 13 ± 6 0.583 0.0365TE(1) �b ≤ 1.7 dS m−1 0 ± 3 11 ± 6 0.503 0.0405TE(2) �b ≤ 1.7 dS m−1 0.6 ± 1.7 10 ± 4 0.681 0.031

F

t

16 F. Visconti et al. / Agricultural Wa

The strength of the association between εb and �b data coulde quantified assessing the corresponding product–moment cor-elation coefficients between both measurements (r). At the 95%onfidence level, r was between 0.67 and 0.94, and between 0.42nd 0.88, for, respectively, the 5TE and 10HS sensor. The appar-nt permittivity measurements carried out in the soils for �b over

dS m−1 are most affected by the apparent electrical conductiv-ty (Fig. 2). The exact �b value up from which the measurementsf εb and �b could be considered independent was sought. Theigher measurements of �b were removed one by one until theroduct–moment correlation coefficient included the zero within

ts 95% confidence interval. This occurred when the six soils withb over 1.7 dS m−1 were removed from the dataset for the 5TE data

r ∈ [−0.03, 0.79]), and when the five soils with �b over 1.8 dS m−1

ere removed for the 10HS data (r ∈ [−0.12, 0.73]). The apparentielectric permittivity of soils would increase as their apparentlectrical conductivity increases according to the model shown inq. (7). Specifically, Schwartz et al. (2013) found under laboratoryonditions that in clay loam soils the apparent dielectric permit-ivity measured with the 5TE sensor remarkably increased with �brom 1.8 dS m−1 upwards. This effect has been also described forDR, e.g. Wyseure et al. (1997) found that reliable TDR measure-ents of soil water content could not be performed for �b values

eyond 2 dS m−1. It seems that the 10HS and 5TE sensors present ab limit somewhat lower than the TDR.

.3. Calibration of equations to estimate the soil water content inhe pots

The RMSD for the prediction of � in the pots with the sensoranufacturer equations were 0.036, 0.062 and 0.070 m3 m−3 for

he 10HS, 5TE(1) and 5TE(2), respectively. The 5TE sensors pre-ented higher errors because the apparent dielectric permittivityeasurements in the most salinized pots were remarkably affected

y salinity (Fig. 3).The simplified power-law dielectric mixing model (Eq. (4)) was

alibrated as a more theoretically-based and simpler alternativeo the polynomial model (Eq. (5)) used by the manufacturer. Thequare root of the apparent dielectric permittivity (εb

1/2) explained5% of the variance of � in the case of the 10HS sensor, and roughly0% in the case of both 5TE sensors (Table 4 and Fig. 4). Besides, onlyhe regression coefficients obtained with the 10HS sensor, whichere b0 = –1 ± 2 and b1 = 17 ± 7 (Table 4), fitted the characteristic

alues found for these parameters in clayey soils, which can present0 values as low as −2 and b1 values as high as 14 (Leao et al., 2010).hatever the case, the precision of these parameters is too low.

Again, the highest errors in the calibration of the simplified

ower-law dielectric mixing model were obtained from the mostalinized soils (Fig. 4). The use of different calibrations for dielec-ric � estimation methods depending on the soil salinity has been

ig. 3. Estimated (�′) against reference (�) soil water content in the pots using the sensor

he wetting solution .

a All data or only low apparent electrical conductivity data (�b ≤ 1.7 dS m−1 and�b ≤ 1.8 dS m−1).

b 95% confidence interval.

recommended by Inoue et al. (2008), and Varble and Chávez(2011). So, data with apparent electrical conductivity beyond 1.8and 1.7 dS m−1 for the 10HS and 5TE sensors, respectively, wereremoved from the dataset to carry out the regression analyses withmeasurements of εb less affected by the electrical conductivity. Asa consequence, the percent of explained variance increased for thecalibration of both the 10HS and the 5TE sensors, but especially forthese latter (Table 4). Besides, the b0 and b1 parameters got closerto reported values for mineral and clayey soils, again especially forthe 5TE sensors. The RMSD for the 5TE(2) was the lowest (Table 4).

4.4. Determination of the reference soil water content in the plots

The values of the bulk density of the surface soils assessed withboth the small and the large cylinder were strongly related (Fig. 5).According to the data in Fig. 5 we can say that (i) the small cylindercompressed the soil regarding the large cylinder, and that (ii) thecompression decreased as the actual bulk density increased, until(iii) a maximum value around 1.6 g cm−3. The calibrated LTS equa-tion of the small cylinder bulk density (�b,small) against the largecylinder bulk density (�bL) which was �b,small = 0.9 + 0.41 �b,largewith R2 = 0.40, could sum up all these observations, while thereciprocal equation, i.e. �b,large = 0.7 + 0.43 �b,small with R2 = 0.22could not. Therefore the reciprocal of the former (�b,small = 0.9 + 0.41�b,large) was used to calculate the �b,large as the most adequate proxyfor the actual bulk density (�b = �b,large) at 15 and 45 cm depth in theplots. The estimated �b values were used to assess � in the samplingsites at 15 and 45 cm depth.

4.5. Validation of the equations to estimate the soil water contentin the plots

The validation was carried out for a total of 36 soil watercontent estimations (Table 5). The estimations carried out usingthe sensor manufacturer equations, presented RMSD values of

manufacturer equations. Data classified according to the electrical conductivity of

F. Visconti et al. / Agricultural Water Management 132 (2014) 111– 119 117

Fig. 4. Calibration curves and 95% confidence interval for point estimations with the simplified power-law dielectric mixing models using the 10HS sensor with all data,5TE(1) with all data, 5TE(2) with all data, 10HS with �b ≤ 1.8 dS m−1 data, 5TE(1) with �b ≤ 1.7 dS m−1 data, and 5TE(2) with �b ≤ 1.7 dS m−1 data. Points arranged according

to the electrical conductivity of the wetting solution .

Table 5Statistical summary of the measurements in the field.

�b (g cm−3) � (m3 m−3) 10HS 5TE

εb � (m3 m−3) T (◦C) εb � (m3 m−3) �b (dS m−1)

N 42 42 36 36 36 36 36 36Mean 1.49 0.309 25.0 0.376 20.2 15.5 0.280 0.67St. Dev. 0.24 0.038 5.1 0.032

Max. 1.69 0.402 41.6 0.473

Fp

Min. 0.98 0.231 14.9 0.306

ig. 5. Small cylinder bulk density against large cylinder bulk density for 34 surfaceoints in the study area, and LTS and 95% confidence interval LTS regression line.

9.7 3.3 0.047 0.4738.2 26.7 0.417 1.55

8.2 10.1 0.191 0.10

0.076 m3 m−3 for the 10HS sensor and 0.061 m3 m−3 for the 5TEsensor (Fig. 6). Both manufacturer equations wrongly estimated �.The manufacturer equation for the 10HS sensor overestimated � by0.06 m3 m−3 on average, while for the 5TE sensor the Topp equationunderestimated � by 0.03 m3 m−3 on average.

The apparent electrical conductivity of the field soils at thesampling time was between 0.10 and 1.55 dS m−1, i.e. lower than1.7 dS m−1 (Table 5). Therefore, the simplified power-law dielectricmixing model calibrated for low apparent electrical conductivityin the pots was used to estimate the soil water content in theplots. The RMSD of the estimations were 0.080 and 0.050 m3 m−3,respectively, for the 10HS and 5TE sensors. The 10HS sensor over-estimated the water content by 0.07 m3 m−3 on average with thecalibrated equation (Fig. 6), while the estimations of the water con-tent with the 5TE appeared uniformly scattered around the 1:1 line,i.e., the calibrated simplified power-law dielectric mixing modelused in conjunction with the 5TE sensors correctly estimated thewater content of the soils on average (Fig. 6).

The high scattering of points observed in Fig. 6 is due to sev-

eral factors absent in the laboratory work but present in the field,which can be arranged into three classes. First, there is the sensor-to-sensor variability (Rosenbaum et al., 2010). Second, there areintrinsic soil factors such as the soil swelling and shrinking during

118 F. Visconti et al. / Agricultural Water Management 132 (2014) 111– 119

F the sea and

wasfiiettcti�fadocivc

tmiicwoTiu

ig. 6. Estimated (�′) against reference (�) soil water content in the field soils usingnd using the simplified power-law dielectric mixing model calibrated for the 10HS

etting-drying cycles, the variable presence of gravels, roots, fauna,nd the variability of the soil temperature, which was almost con-tant in the laboratory but varied between 8.2 and 38.2 ◦C in theeld (Table 5). The importance of most of these factors is higher

n clayey soils and shallow depths. Third, there are actual differ-nces between the soil water content in the sampling sites andhe monitored sites. The first and second mentioned factors con-ributed to the variability in εb measured by the sensors under fieldonditions regarding measurements in the laboratory, while thehird class reflects the spatial variability in the soil water contenttself. These factors, on the whole, did not affect the accuracy of the

estimations with the 5TE, they just decreased precision. There-ore, a methodological approach based on a laboratory calibrationnd a subsequent field validation appears optimal. In comparison, airect field calibration would have provided calibration parametersf very low precision and, therefore, of much more limited practi-al use. Besides, for practical monitoring of field water contentst is advisable to use several soil sensors to take into account theariability due to the three classes of factors in the eventual waterontent estimations.

For irrigation scheduling of the studied plots, the use ofhe laboratory-calibrated simplified power-law dielectric mixing

odel avoided the overestimation of the soil water content by 6%n the case of the 10HS sensor, and the underestimation by 3%n the case of the 5TE sensor. These results have practical appli-ations for irrigation scheduling and water saving purposes sinceater shortages, in the first case, and water excesses in the sec-

nd one, could be avoided leading to a more efficient water use.he 5TE sensor is, therefore, preferable over the 10HS becauset can be calibrated with more accurate equations that worknder field conditions, and besides, because it provides additional

nsor manufacturer third order polynomial equations for the 10HS and 5TE sensors,5TE(2) sensors in the pots.

information about soil temperature and salinity. Moreover, in caseno laboratory calibration is performed for these sensors, the use ofthe 5TE sensor would still be preferable over the 10HS because tofulfil the crop water requirements and leaching fractions, specifi-cally in salt-threatened soils, a slight overirrigation is preferable toa more pronounced underirrigation.

5. Conclusions

The capacitor formed by the prongs of the 10HS and 5TE sensorsrespond different to the soil water content and the soil apparentelectrical conductivity and, as a consequence, each sensor requiresits own calibration. A simplified power-law dielectric mixing modelprovides an adequate equation to estimate the soil water con-tent from the apparent dielectric permittivity with both sensors.However, because of the influence of the apparent electrical con-ductivity on the apparent dielectric permittivity measurement,models adequate enough can be obtained only for low apparentelectrical conductivities. The limit between low and high apparentelectrical conductivity has been established for these sensors to bearound 1.7–1.8 dS m−1. For low apparent electrical conductivity,the 5TE sensors present b0 and b1 parameters of the simpli-fied power-law dielectric mixing model very close to the valuesreported in the literature for clayey and mineral soils. In spite ofthe sensor-to-sensor, soil water content, and other sources of vari-ability present in the field and absent in the laboratory work, thefield validation confirmed the better performance of the 5TE sen-

sor, which was characterized by a field RMSD of 0.050 m3 m−3, anda correct average estimation of the soil water content. Contraryto this, the 10HS sensor still overestimated the soil water contentin spite of the calibration. The Decagon 5TE sensor can be reliably

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F. Visconti et al. / Agricultural Wa

sed in the field to estimate the soil water content in clayey soilsor apparent electrical conductivities below 1.7 dS m−1. This willontribute to better irrigation water dosage in the plots studiedvoiding overirrigation as well as underirrigation. In case a soilpecific calibration is not available, the 5TE sensor would still bereferable over the 10HS sensor.

cknowledgements

The funding for this work was provided by the Spanish Ministerioe Ciencia e Innovación through projects CGL2009-14592-C02-01nd CGL2009-14592-C02-02, and program “Juan de la Cierva” (F.isconti) and scholarship BES-2010-036515 (D. Martínez), andeneralitat Valenciana through program Val i+d (F. Visconti). We

hank the two anonymous reviewers and joint editor-in-chief forheir constructive comments that improved the article.

ppendix A. Supplementary data

Supplementary data associated with this article can be found,n the online version, at http://dx.doi.org/10.1016/j.agwat.2013.0.005.

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