Mapping Field-Scale Physical Properties of Soil with Electrical ResistivityO. Banton,* M.-K. Seguin, and M.-A. Cimon

ABSTRACTThe spatial variability of physical properties that significantly influ-

ence the fate of water and solute in soils needs a large number ofmeasurements to be quantified. Surface electrical resistivity tech-niques could be used as a simple and practical method to determinethis spatial variability. Electrical sounding and profiling measurementswere taken on a small agricultural field (30 by 60 m) under twodifferent soil conditions (dry and wet conditions). The soil profile iscomposed of three layers: a highly permeable sandy loam (alluvialterrace) overlying a gravelly sandy till that covers a friable sandy tosilty shale. The soil physical properties (grain size distribution, poros-ity, hydraulic conductivity, bulk density, and organic matter content)of the uppermost layer were measured in the laboratory on undis-turbed soil cores taken at three different depths on a 6 by 15 m gridin the field. Correlations were established between these parametersand the electrical conductivity. The best correlations were betweenthe electrical conductivity and the sand, silt, clay, and organic mattercontents. Their correlation coefficients, r, were, respectively, 0.64,0.53, 0.64, and 0.65 for the dry conditions and 0.54, 0.45, 0.53, and0.52 for the wet conditions. No relation was established betweenthe electrical conductivity and the porosity, the bulk density, or thehydraulic conductivity. The correlation coefficients were, respectively,0.04,0.16, and 0.10 for the dry conditions and 0.14,0.12, and 0.14 for thewet conditions. Electrical conductivity seems to be more influenced bythe soil texture, i.e., by the electrical properties of the soil constituents,than by the structure, i.e., the water-related properties. Besides, thetwo sets of electrical resistivities obtained in dry and wet conditionsare not significantly different, as shown by the regression betweenthem (slope = 0.92, r = 0.71) and by the isoresistivity maps. Thisstudy seems to indicate that the electrical method could be used toevaluate the spatial variability of some soil properties when theirvariability is sufficiently large, i.e., when the investigation scale or thelevel of contrast is large enough.

THE SPATIAL VARIABILITY of soil physical propertiesis recognized as a limiting factor in the acquisition

and representation of field data (Nielsen et al., 1973;Jury et al, 1987). Such variability constitutes a signifi-cant constraint for the use of mathematical models thatsimulate water percolation, solute transport, or soil ero-sion (Nielsen et al., 1986; van Genuchten and Jury,1987). Because a large soil sampling and the relatedparameter characterization are time consuming and ex-pensive, alternative methods to investigate spatial vari-ability are desirable. Geophysical theory indicates anempirical relation between the electrical resistivity (ER)of the soil and some soil properties (Archie, 1942;Rhoades et al., 1976). With electrical methods, apparentER can be obtained by different techniques: conductionthrough the soil (Telford et al., 1976; Barker, 1989;Zohdy, 1989) and ionospheric plasma resonance(Strorey et al., 1969; Grard and Tabbagh, 1991). Electro-

O. Banton and M.-A. Cimon, INRS-Eau, Universite du Quebec, CP7500, Sainte-Foy, QC, G1V 4C7 Canada; and M.-K. Seguin, Departe-ment de Geologic et Genie Geologique, Universite Laval, QC, G1K7P4 Canada. Received 4 Oct. 1995. *Corresponding author (bantonol@inrs-eau.uquebec.ca).

Published in Soil Sci. Soc. Am. J. 61:1010-1017 (1997).

magnetic (EM) methods can also be used to determinethe apparent ER (De Jong et al., 1979; Rhoades andCorwin, 1981).

A few studies have tested the spatial variability ofthe electrical properties of the soil as a proxy for thevariability of different soil physical properties. The elec-trical properties of soils have been compared with thesaturated hydraulic conductivity (Mazac et al., 1988),the salt and clay contents (Williams and Hoey, 1987),the soil water content (Kachanoski et al., 1988), the soilsalinity (Lesch et al., 1992; Vaughan et al., 1995), theforest soil quality (McBride et al., 1990), and the herbi-cide partition coefficient (Jaynes et al., 1995). Advan-tages of geophysical methods are their rapidity, theirefficiency, and their low cost when compared with labo-ratory measurements of in situ soil samples (Dabas etal., 1989). Mazac et al. (1988) combined two geophysicalmethods, vertical electrical sounding (VES) and dipoleelectromagnetic profiling, to study the spatial relation-ship between the saturated hydraulic conductivity (ATsal)and the ER of soils. The 36 measurements of ER andA^sat on a 100 by 100 m area exhibited, for five depthintervals from 0.5 to 5 m, negative rank correlationcoefficients, R, between -0.01 and -0.34, and a positiveR value of 0.13 for the surface interval (0-0.5 m). Onthe other hand, they found a good linear relation forthe vertical covariation of A:sat and ER (using the meanvalues of ER and Ksa at the four depth intervals toabout 6 m), with correlation coefficients r of -0.98 and-0.89, respectively. Williams and Hoey (1987) used EMtechnique to study the variability of the clay and saltcontents on a 250-ha field. They found a weak predictiverelationship between the clay content and the apparentelectrical conductivity, EC (n = 210, r = 0.42), but therelation improved when the salt and clay contents wereused together (n = 210, r = 0.83). Kachanoski et al.(1988) also investigated texture and water content usingthe EM technique on a 1.8-ha field (52 EM measure-ments, 37 soil sampling locations). They found weakrelationships between apparent EC and the sand, silt,or clay contents (r between 0.30 and 0.70). However, astrong relationship was established between the appar-ent EC and the water content (r = 0.91). Jaynes et al.(1995) found a strong correlation between the organicC fraction (closely related to the organic matter content)and the apparent EC (r = 0.77, n = 117) on a field of250 by 250 m. Vaughan et al. (1995) found an increasewith depth in the correlation between the apparent ECand the EC of the soil paste extract (r - 0.22 to 0.73,n = 2378, 2400 ha), and a decrease for the correlationbetween apparent EC and water content (r = 0.60 to0.53).

The objective of our study was to investigate the spa-

Abbreviations: ER, electrical resistivity; VES, vertical electricalsounding; KSM, saturated hydraulic conductivity; EM, electromagnetic;EC, electrical conductivity; ERP, electrical resistivity profiling.

1010

BANTON ET AL,: MAPPING FIELD-SCALE PROPERTIES WITH ELECTRICAL RESISTIVITY 1011

tial variability (both horizontal and vertical) of the ap-parent ER on the scale of a small agricultural field andto test the possibility of its application as a proxy forthe spatial variability of some soil physical properties.To this end, the thickness and the ER of each layer of asoil were characterized with a series of vertical electricalsoundings using a Schlumberger array (Astier, 1971;Best and Boniwell, 1989). Second, ER profiling surveys(ERP) were carried out using the Wenner array (Telfordet al., 1976; Burger, 1992) under dry and wet conditionsto elaborate isoline maps in order to describe the spatialdistribution of ER and to possibly correlate the apparentER to the soil physical properties (obtained from Ban-ton et al., 1992). The water content and the soil waterER, which are transient variables of the soil, were notmeasured.

MATERIALS AND METHODSThe experiments were conducted on the experimental farm

of the Quebec Ministry of Agriculture at Saint-Augustin-de-Desmaures (Portneuf County), 25 km west of Quebec City.The sampling area (30 by 60 m) is located near the lower limitof an alluvial terrace with a slope between 2 and 5%. Thisarea was planted to corn (Zea mays L.) and potato (Solatiumtuberosum L.) with no significant differences in the manage-ment practices. The soil profile is composed of three geologicallayers. The upper layer is a permeable, moderately coarse-textured soil formed in alluvial deposits of the Saint-LawrenceRiver. Taxonomically, this soil is classified as a loamy, mixed,frigid Typic Haplorthod (U.S. soil classification) or a humo-ferric podzol (international soil classification). The alluvialdeposits are underlain by a glacial till (sandy material withstones of 5 to 20 cm made of granite, shale, and other sedimen-tary rock) that overlies bedrock composed of a clayey andgritty friable shale (Nicolet formation, Ordovician age). Threeexcavations done in this area indicate that the alluvium isbetween 1.2 and 1.6 m thick while the till formation is morethan 1.6 m thick (bottom not located).

The physical parameters of the soil layers were determinedby methods described by Banton et al. (1992). Core samplingwas done over a 6 by 15 m network. This design provided 25sample sites. At each site, samples were taken at three depths:10, 45, and 90 cm (Fig. 1). The mineralogical composition ofthe surface soil was evaluated with washed samples (to elimi-

80

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nate the fine particles adhering to the minerals). Cobaltinitritewas used to differentiate potash feldspars from plagioclase.The observed mineralogical constituents were fine clay andsilt (35%), quartz (9%), potash feldspars (33%), plagioclase(3%), mica (2%), magnetite and titanomagnetite (5%), horn-blende (5%), garnet (1%), and some rock fragments.

The ER methods (by conduction) allow a current to circu-late through the ground between a pair of electrodes (A andB) and the potential difference is measured with another pairof electrodes (M and N), providing a measure of the electricalresistance of the soil system. The depth of penetration in-creases with the distance between the AB electrodes. Appar-ent ER is calculated as the product of the electrical resistance(measured between M and N) with the geometrical factor(specific to the electrode array AMNB). Apparent ER is ameasurement that can be interpreted as due to homogeneousground of some ER (Milsom, 1989), equivalent to the actualheterogeneous soil system composed of different layers havingdifferent ER. Soil ER depends on the ER of the solid constit-uents, the ER of the fluid content (salinity), the porosity,and the degree of saturation. Empirical relationships existaccording to Archie's law (Archie, 1942): pa = b n~c f ~d pw,where pa is the apparent ER; n, the porosity; /, the fractionof pores containing water; pw, the water ER; and b, c, and dare fitting parameters (0.5 < b < 2.5, c = 2, 1.3 < d < 2.5).

The ER of the soil water was measured in water samplesextracted from 30 suction lysimeters (i.e., 12,12, and 6 lysime-ters, respectively, at depths of 0.5, 1.0, and 1.5 m) within thestudy site. The average soil water ER was 33 H-m (EC = 30.1mS/m). The average annual ER of the atmospheric water was1088 ft-m (EC = 0.92 mS/m) (obtained from analyses of theionic concentration of rainwater performed by the QuebecMinistry of Environment at Sainte-Catherine-de-la-Jacques-Cartier station). The low resistivity of the soil water is believedto reflect the mineralization of the soil water.

To characterize the ER of the different geologic layers, ERvalues were measured directly on exposures of these materials.For small electrode separations, the penetration depth is smalland the measured apparent ER can be considered as the ERof the contact material. On the study site, three excavationswere carried out to a maximum depth of 2.80 m. Because thepits were excavated using a mechanical digger in steps of about1-m depth, the measurements were done on the wall of thesepits. At each interval of about 20-cm depth (Fig. 2), measure-

Electrica! resistivity (ohm-m)

-100 O 100 200 30010

400

u

O)Q

100

O 20 40Fig. 1. Location of soil samplings (for soil physical property measure-

ments) and electrical investigations (distances in meters).

1000Fig. 2. Schematic profile of the soil indicating the electrical resistivity

measured for the upper soil and the till in the three excavationsand for the shale in the outside outcrop.

1012 SOIL SCI. SOC. AM. J., VOL. 61, JULY-AUGUST 1997

Table 1. Thicknesses and electrical resistivities (ER) measuredon outcrops and estimated by inverse modeling of the electricalsoundings (range given with mean value in parentheses).

Measured ModeledSoil Measured Modeled layer layerlayer ER ER thickness thickness

ft-m - m •Alluvium 24.7-84.5 (59.8) 43.1-122.4 (68.1) 1.2-1.6 0.7-1.3 (1.05)Till 102.9-313.3 (190.9) 147.2-374.9 (244.3) >1.6 3.4-5.8 (4.46)Shale 17.3-40.4(28.3) 19.6^16.6(31.7) unknown (infinite)

ments were taken with a horizontal Wenner array (AB = 1.5m). The depth of investigation can be approximated as z =0.17 AB (Barker, 1989). The radius of the current ellipsoidin soil between A and B is then evaluated to ±25 cm aroundthe measurement axis. For the first depth interval (20 ± 25cm), the error induced by the soil surface that constitutes theupper limit for the current ellipsoid is negligible. These measure-ments (Table 1) characterized the ER of the alluvial deposits(24.7-84.5 n-m, mean value 59.8 fl-m) and of the till (102.9-313.3 fl-m, mean value 190.9 fl-m). For the shale, ER measure-ments were taken on an outcrop located outside the site. Themean ER value obtained for the shale was 28.3 fl-m. Figure2 shows a schematic profile of the investigated area indicatingthe ER measured in the three excavations and on the out-side outcrop.

Both VES and ERP were used on the site. The VES methodinvestigates soil layering, using arrays in which the distancesbetween some or all of the electrodes are systematically in-creased. In this method, apparent ER are plotted against ABexpansion on log-log paper (Fig. 3). The ERP method wasused to detect lateral change in soil properties. In the ERPmethod, array parameters are kept constant. Therefore, thetheoretical depth of penetration varies only with changes insubsurface layering. The instrumentation consists of a trans-mitter-receiver (ABEM Terrameter 300B), three reels of elec-trical wire (two 750-m reels with a section of 0.75 mm2 andone 50-m reel with a 1-mm2 section), and four stainless steelelectrodes. This instrumentation has a measurement range ofnearly O to 2 X 106 O with a standard error of ±2%. Theoutput voltage is 160 V and the current intensity used was 20mA. The alternating current provides a square wave with adominant low frequency of approximately 4 Hz.

1000

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i

100.1 1 10 100

AB/2 distance (m)Fig. 3. Typical example of an electrical resistivity sounding (inter-

preted by inverse modeling as a 0.9-m stratum of 44.5 li-m, a 4.2-m stratum of 257.4 fl-m, and an infinite stratum of 26.1 Jl-m).

Vertical electrical soundings were used to estimate the oc-currence, the thickness, and the ER of the different layersby inverse modeling of the measured apparent ERs. Inversemodeling was performed with the ABEM Super VES-resisti-vity software (ABEM, Bromma, Sweden). This software calcu-lates the optimal set of layer thicknesses and ERs that modelthe curve of measured apparent ERs. For both VES and ERP,the electrical resistance, R, was measured and transformedinto apparent ER, pa , through the relation: pa = kR, wherek is a geometric factor equal to tr(AB)2/4MN for the Schlum-berger array, and 2irMN for the Wenner array. To improvethe modeling, the calibration of ER was carried out using theER values obtained in the wall of the three excavations madeon the site.

For the VES investigations, a Schlumberger array was usedwith a varying AB/MN ratio between 5 and 20. For the firstmeter of soil, an increment of AB/2 equal to 0.4 m was usedbetween each reading. This increment was expanded to 1 m fordeeper investigations. When the MN separation was increased,three overlapping readings were taken. The ERP was donewith a Wenner array (constant AB/MN ratio of three). Ateach station (25) of a regular grid (10 by 10 m) (Fig. 1), threemeasurements were taken using AB distances of 0.6, 2.6, and5.3 m, respectively. These AB distances correspond approxi-mately to the investigation depths of 10, 45, and 90 cm asestimated by Barker's (1989) empirical relation: z — 0.17 AB,where z is the planned investigation depth corresponding tothe AB distance. Two data sets were obtained in the ERPmode: one in dry conditions (11 July 1990) and the other inwet conditions (24 July 1990) after 2 d of rain (19 July: 28mm and 23 July: 37 mm). Each data set was obtained duringa single day in order to minimize variations of apparent ERdue to changes in the soil water content.

Apparent ER values obtained with ERP were plotted onisoline maps. The mapping was done with the Surfer softwarethrough an interpolation technique using the kriging method.This geostatistical method is based on the autocorrelation ofone parameter, i.e., on its degree of variability through space(its spatial variability pattern). Thereafter, apparent ER valueswere interpolated at the corresponding soil sampling locationsto establish the correlation between the different physicalproperties and the ER values. One should note that this opera-tion (interpolation before correlation) could have an impacton the results of the correlation.

RESULTS AND DISCUSSIONModeling of the 28 VESs (25 sample and three cali-

bration sites) (example on Fig. 3) indicates that the soilconsists of three layers, the central layer (till) beingmore resistive than the overlying layer (alluvium) orthe underlying layer (shale). The influence of the thirdlayer (shale) on the resistivity curve is often discreet,because of the depth of this layer that is not deeplyinvestigated by the maximum AB measurements. Thecalculated average thickness of the alluvial deposits is1.05 m while that of the till is 4.46 m. The thickness ofthe underlying Ordovician shale is considered infiniteby the ER modeling. Table 1 gives the ranges of thick-ness and ER of the three layers as evaluated by theinverse modeling of the VES data compared with thosemeasured directly on exposures of these materials. Foreach material, both ER values are close, implying thatthe model accurately approximates the measured appar-ent ER (from VES). It should be noted that the ER is

BANTON ET AL.: MAPPING FIELD-SCALE PROPERTIES WITH ELECTRICAL RESISTIVITY 1013

characteristic of each layer and that there are no ERvalues overlapping two adjacent layers. The thicknessof the alluvium obtained by the interpretation of ERsoundings (0.7-1.3 m) is inferior but close to the oneobserved in the in situ excavations (1.2-1.6 m).

Isoresistivity maps (Fig. 4) provide a reliable visual-ization of the spatial distribution of apparent ER (fromWenner ERP). In dry conditions, the apparent ERclearly increases with depth. For depths of 10, 45, and90 cm, the mean ERs are 53, 71, and 98 fl-m, respec-tively. This increase of ER with depth could be attrib-uted mainly to the decrease in the clay content (whichis a good conductor) established by laboratory soil anal-yses (21.5, 19.5, 14.5%; Banton et al., 1992). However,a vertical distribution of water content could also influ-ence the ER. The horizontal distribution patterns of ERvalues are similar for the three depths. The difference inthe magnitude of the ER variability at the differentdepths could be explained by the higher degree of homo-geneity in the upper zone induced by the tillage prac-tices. At the two lower soil depths, areas with minimalER values are believed to correspond with zones ofhigher clay content.

The same conclusions apply to the apparent ER mea-surements obtained under wet conditions. The meanapparent ER values at 10-, 45-, and 90-cm depths (51,71, and 93 fi-m, respectively) are approximately the sameas those under dry conditions (53,71, and 98 fl-m). Whilethe spatial variability patterns are different for the 10-cm depth between dry and wet conditions, the spatialpatterns are very similar for the 45- and 90-cm depths.This similarity can be attributed to different factors.First, the 65-mm rain (during a 5-d period) is not suffi-cient to significantly increase the water content of thesoil. Second, if there were sufficient water for drainageto occur, then the alluvial deposit is sufficiently perme-able to allow the infiltration water to drain easily andrapidly through the soil; the water content thereforeremains similar in both periods before and after therain. Third, since the electrodes are pushed about 10cm into the ground, the variation in moisture contentof the upper few centimeters has little effect becausethe current is flowing more deeply into the ground.Finally, due to the use of mineral fertilizers, the numberof ions present in the soil is high (giving a measuredsoil water ER of 33 fl-m). During the short time intervalbetween the wet and dry conditions, the total amountof ions did not change significantly in the soil. Thus, theincrease in water content (decreasing the soil ER) couldbe compensated by the decrease in the ionic concentra-tion of the soil water (increasing the water ER) as aresult of the ion dilution. The electrical conductivity ofwater is indeed proportional to the ion concentration,the resistivity being inversely proportional. Accordingto Archie's Law (see above), the apparent soil ER isdirectly proportional to the water ER and inverselyproportional to the water content in terms of the fractionof pores containing water (with an exponent of 1.3-2.5).For example, if the water content doubles (withoutchange in ion mass), the water ER doubles as well asthe water content. Because of the exponent affecting

the water content, the apparent soil ER decreases inthis case by a factor of 1.2 to 2.8. An increase in watercontent by a factor of two would correspond (for thissoil) to a change from the wilting point to saturation(i.e., an unusual case). A linear regression of both ERsets obtained for the three depths under dry and wetconditions gives a slope value of 0.92 (Fig. 5). This valueis close to the 1:1 slope, indicating that the ER valuesobtained under wet conditions are only a little lowerthan those obtained under dry conditions. However,the correlation coefficient of this regression (r = 0.71)indicates that the ER sets are not very closely correlated,perhaps as a result of the extrinsic variability related tothe measurement method or of the intrinsic variabilityof the wetting processes and conditions.

Regression analyses were attempted to determine thestatistical relationship between in situ EC and variousphysical properties of the soil. While geophysics com-monly uses the ER values for representation and calcu-lation, soil physics more commonly uses EC values. Inorder to agree with this convention, the linear regres-sions were calculated with EC rather than with ER.They are calculated for each tested soil parameter withthe 25 values obtained at the three depths (10, 45, and90 cm). In addition, regressions were performed for thethree depths combined, with a total of 75 values. Figure6 shows some linear regressions obtained with the 75samples combined. Table 2 gives the values of the corre-lation coefficients obtained for all the regressions. Thevalues of the correlation coefficients, r, range from 0.02to 0.65. In general, the best results were obtained whenthe three depths were combined (75 values). This resultcould indicate that (i) a 25-value set is too small consid-ering the high variability of both the EC and the physicalparameters, (ii) the vertical variability of the soil proper-ties, being greater than their horizontal variability, isbetter represented by the EC measurements, or (iii) ECis not closely correlated to these soil physical pa-rameters.

The best regressions (best correlation coefficients)are obtained for the component contents (sand, silt, clay,and organic matter) under dry conditions. For thesecontents, the 75 values align to form elongated clusters(Fig. 6). The relation between the sand content and ECis inversely proportional. The relations between clay,silt, and organic matter contents and EC are directlyproportional. The relations for the sand, silt, and claycontents can be explained by the respective ECs of theconstitutive materials. Still, a large amount of scatteringis observed (r between 0.45 and 0.64 for all the valuesunder dry or wet conditions). A good relation (r of 0.52and 0.65) seems to exist also between EC and the organicmatter content, when taking into account the decreaseof the OM content with depth (i.e., with the three depthintervals). On the other hand, the correlations betweenEC and the porosity, the hydraulic conductivity andthe apparent bulk density are very weak. These resultsindicate that, at this site, EC is more dependent on therespective content of the conductive (clayey minerals)and nonconductive (quartz, feldspar) constituents of thesoil rather than on the porosity (air and/or water con-

1014 SOIL SCI. SOC. AM. J., VOL. 61, JULY-AUGUST 1997

Dry condition isoresistivity map

DEPTH 10cm DEPTH 45cm DEPTH 90cm

mean = 53 Ohm-ID rmean = 71rmean = 98 Ohm-m

Wet condition isoresistivity mapDEPTH 10cm DEPTH 45cm DEPTH 90cm

rmean = 51 Ohm-mFig. 4. Isoresistivity maps for the different soil depth

mean = m-m rmean = 93 Ohm-mintervals obtained under dry and wet conditions.

BANTON ET AL.: MAPPING FIELD-SCALE PROPERTIES WITH ELECTRICAL RESISTIVITY 1015

tent) or the hydraulic conductivity of the soil. The corre-lations obtained under dry conditions are better thanthose under wet conditions except for the porosity andthe hydraulic conductivity. This result could indicate aninfluence of the water content on the EC values andcould result from the higher spatial variability of thewater content under wet conditions.

These results do not differ from those obtained byother studies and the correlation coefficients are closeto those of the literature (Table 3). On the other hand,the two moisture conditions tested have not significantlyinfluenced the correlations. Studies that have comparedthe spatial distributions of EC and water content foundgood to strong relationships (Kachanoski et al., 1988;Vaughan et al., 1995). Yet the porosity, a physical prop-erty closely related to the water content, has exhibiteda poor correlation to EC in our study, as well as thedensity and hydraulic conductivity, both potentially re-lated to the porosity. These results could indicate thatonly highly contrasting values in porosity, density, andhydraulic conductivity can be identified by EC measure-ments. Such contrasts exist when large heterogeneitiesoccur in the soil material. For example, the strong verti-cal correlation with hydraulic conductivity found by Ma-zac et al. (1988) was attributed to the sequence of fourcontrasting layers, while the depths investigated by theERP in our study are located in the same alluvial mate-

Table 2. Correlation coefficients (r) obtained for the linear re-gressions between soil physical properties and electrical con-ductivities (mS/m) obtained under wet and dry conditions.

10-cmdepth

propertyt

SandSiltClayDensityPorosityKaaOM

wet

0.090.370.150.120.300.080.15

dry

0.400.570.070.090.040.190.11

45-cmdepth

wet

0.270.170.290.130.110.070.09

dry

0.520.300.570.110.020.180.34

90-cmdepth

wet

0.210.050.380.380.150.180.09

dry

0.210.080.340.410.250.180.05

All depths

wet

0.540.450.530.120.140.140.52

dry

0.640.530.640.160.040.100.65

t Sand, silt, and clay contents (%), apparent bulk density (Mg/m3), totalporosity (%), saturated hydraulic conductivity (A',,,,) (cm/h), and organicmatter (OM) content (g/kg).

rial. Also, contrasted values (i.e., large spatial variabil-ity) are assumed to be less often encountered on thescale of the field investigated (0.2 ha) than on a largerscale (2400 ha for Vaughan et al., 1995; 1.8 ha for Kacha-noski et al., 1988; 1 ha for Mazac et al., 1988).

CONCLUSIONS1. The ER values and stratum thicknesses obtained

by geophysical data modeling (from VES) are character-istic of the materials and close to those observed in situ.This confirms the reliability of the ER values obtained

100

80 -

OC 60IU

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40 - -

20 -y = 0.85x + 9.39

R! = 0.59

200

20 40 60 80

150 -

100

50 -

B

= 0.78x + 14.38R2 = 0.46

100 50 100 150 200

200

E 150

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200

200 100 150 200O 50 100 150

Dry condition ER (ohm.m) Dry condition ER (ohm.m)Fig. 5. Linear regression of the electrical resistivity (ER) sets obtained under dry and wet conditions for the different depth intervals: (a) 10

cm; (b) 45 cm; (c) 90 cm; and (d) all three depths together.

1016 SOIL SCI. SOC. AM. J., VOL. 61, JULY-AUGUST 1997

Table 3. Correlation coefficients (r) obtained from this study andthe literature.

Soil property

SandSiltClay

Clay and saltSoil water EC§

Water content

Bulk densityPorosityKJiOrganic matter

content

This study (wet-dry)t

0.54-0.640.45-0.530.53-0.64

0.12-0.160.14-0.040.14-0.10

0.52-0.65

Literature (reference)!

0.44-0.67 (K)0.30-0.52 (K)0.50-0.70 (K)

0.42 (W)0.83 (W)

0.22-0.73 (V)0.47-0.76 (K)0.53-0.60 (V)0.88-0.94 (K)

0.01-0.34# (M)

0.77ft (J)t Value for the three depths for wet and dry conditions (EC in mS/in).t (K) Kachanoski et al., 1988 (n = 52; 1.8 ha); (W) Williams and Hoey,

1987 (n = 210; 250 ha); (V) Vaughan et al., 1995 (n = 2378; 2400 ha);(M) Mazac et al., 1988 (n = 36; six depths; 1 ha); (J) Jaynes et al., 1995(n = 117; 6.25 ha).

§ EC = electrical conductivity.H KM = saturated hydraulic conductivity.# Absolute value of the rank correlation, R, of electrical resistivity values.ft Organic C content.

30

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y = -0.32x + 33.53Rz = 0.41

———I————I——30 40 50 60 70

Sand content (%)80 90

from ERP to characterize the spatial change in electricalproperties of the material.

2. The mean ER increases with depth, passing from51 to 53 n-m at the first depth to 93 to 98 H-m at the thirdone. A spatial variability pattern for ER is observed atthe three depths. The definition of this pattern increaseswith depth. The spatial repartition of ER data globallycorrespond to that of the clay content. The low ERzones are believed to coincide with the clay-rich ones.Moreover, the increase in ER with depth is attributablemainly to the decrease in the clay content. The conduc-tive material content has thus a significant influence onthe ER variability, which could mask the variability ofother physical properties.

3. The correlation coefficients obtained from linearregressions between EC and the physical properties aresimilar to those provided by the literature. The highestcorrelations were between EC and the clay, silt, andsand contents. The inverse relation between EC andthe sand content and the direct ones with the clay andsilt contents could confirm that EC is significantly influ-30

25

20-

15

10 -

5 - -

B

y = 0.48x + 3.64R' = 0.28

10 20

Silt content (%)30 40

30

? 25

920

UUJ

15 -

Q 5

O

y = 0.58x + 3.97R2 = 0.40

H

30

25

20

15

10 - -

5

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y = 2.57x + 7.24R2 = 0.43

25 30 Q 15 10 15 20

Clay content (%) OM content (%)Fig. 6. Linear regressions between electrical conductivity (EC) and some soil physical properties: (a) sand; (b) silt; (c) clay; (d) organic matter

(with values obtained for the three depths under dry conditions).

BANTON ET AL.: MAPPING FIELD-SCALE PROPERTIES WITH ELECTRICAL RESISTIVITY 1017

enced by the mineralogical composition of the differentgrain sizes. The weak relation between EC and the wa-ter-related properties (porosity and hydraulic conduc-tivity) indicates that their spatial variability cannot bedetermined by the EC method for this site (on this scaleand/or for this kind of soil).

4. The spatial variability pattern and the mean ERobtained under wet and dry conditions in this soil werenot significantly different. This could indicate the slightvariation in the soil water content or that the change insoil moisture has little effect on ER, possibly as a resultof the high ionic concentration in the soil originatingfrom mineral fertilizers. These results could indicatethat only a significant change in water content, as wella large variability in porosity and hydraulic conductivity,can be easily identified by ER measurements. This situa-tion should be more often encountered on a larger scaleof investigation than this study scale.

More investigations are thus needed before makingconclusions on the usefulness of surface ER-EC tech-niques for determining the spatial distribution of thephysical properties of soil at the field scale. Some recom-mendations for future work and some suggestions forthe needed research can be given.

A. Such a method of investigation tends to attenuatethe evidence of the variability of physical properties,maybe as a result of the integration of the influence ofmost of them. Because larger variability exists on larger'scales, investigations should be applied to large domainsrather than to small fields. On the other hand, materialhaving a greater range in properties or more markedspatial variability would be more likely to show closercorrelations between ER and soil properties.

B. The variability of physical properties is often signif-icant even for a short distance between measurements.So, measurements could be done on a higher densitygrid. As an example, it would be interesting to testa high-density one-dimensional grid on which physicalproperties and ER or EC values are measured and ana-lyzed by geostatistical variogram.

C. The change in water content seems to have a differ-ent influence depending on whethrer it occurs throughspace (literature cases) or time (this study). Investiga-tions should be conducted using continuous measure-ment devices. Simultaneous evolution through time ofboth water content and ER could then be quantified.Comparing these temporal vs. spatial variations wouldindicated the dependence of ER on water content. Ulti-mately this should determine the possibility of accu-rately measuring the spatial variability of steady-statephysical properties free from the moisture status ofthe soil.

ACKNOWLEDGMENTSThis study was funded by the Natural Sciences and Engi-

neering Research Council of Canada.