Journal of Applied Geophysics 78 (2012) 113–122

Contents lists available at SciVerse ScienceDirect

Journal of Applied Geophysics

j ourna l homepage: www.e lsev ie r.com/ locate / jappgeo

High-resolution imaging of a vineyard in south of France using ground-penetratingradar, electromagnetic induction and electrical resistivity tomography

Frédéric André a,⁎, Cornelis van Leeuwen c, Stéphanie Saussez b, Renaud Van Durmen b, Patrick Bogaert b,Davood Moghadas a, Laure de Rességuier c, Bruno Delvaux b, Harry Vereecken a, Sébastien Lambot a,b

a Agrosphere (IBG-3), Institute of Bio and Geo Sciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germanyb Earth and Life Institute, Université catholique de Louvain, Croix du Sud 2 Box L7.05.02, 1348 Louvain-la-Neuve, Belgiumc ENITA de Bordeaux, I.S.V.V., UMR EGFV, Cours du Général de Gaulle 1, CS 40201, 33175 Gradignan-Cedex, France

⁎ Corresponding author. Tel.: +49 2461 61 1882; faxE-mail address: f.andre@fz-juelich.de (F. André).

0926-9851/$ – see front matter © 2011 Elsevier B.V. Aldoi:10.1016/j.jappgeo.2011.08.002

a b s t r a c t

a r t i c l e i n f o

Article history:Received 15 November 2010Accepted 3 August 2011Available online 11 August 2011

Keywords:Ground-penetrating radarElectromagnetic inductionElectrical resistivity tomography3D-imagingSoil hydrogeophysical properties

Detailed knowledge of soil properties regulating soil water availability for the vines is of prime importance foroptimal vineyard management. As soil characteristics may vary strongly over short distances, specific toolsare required for efficient assessment and monitoring of soil water distribution and dynamics with fine spatialresolution. In that respect, ground-penetrating radar (GPR), electromagnetic induction (EMI) and electricalresistivity tomography (ERT) surveys were carried out in a vineyard in southern France in order to producehigh-resolution maps of soil stratigraphy and to retrieve soil hydrogeophysical properties of the different soillayers. The results presented in this paper show large spatial variations in vineyard soil properties, which arein accordance with the distribution of the different soil types within the study area. This is particularlyobservable from EMI and ERT data, which show strong spatial correlation with large areas of comparablevalues delimited by well-defined discontinuities, revealing sharp variations of soil characteristics over shortdistances. These discontinuities almost systematically correspond to the limits of the vineyard plots, thoughareas of contrasting soil electrical conductivity values are also found within some plots. Furthermore, thepatterns of soil electrical conductivity and resistivity are in good agreement with soil stratigraphy observedfrom GPRmeasurements. Finally, these results also highlighted anthropogenic soil compaction resulting fromagricultural practices during too wet soil conditions as a likely explanation to vine vigor problems observedlocally in the vineyard, which is corroborated by the observation of lower values of the normalized differencevegetation index (NDVI) for the vine in zones where soil compaction was evidenced by both geophysicalmeasurements and soil profile description.

: +49 2461 61 2518.

l rights reserved.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

The notion of terroir is a key concept in viticulture as it establishesthe relationship between wine quality and anthropogenic factorsaffecting grape development (van Leeuwen and Seguin, 2006). Vinewater status is considered to be one of the main parameters of a terroiras it strongly affects vine and grape growth togetherwith berry ripenessand composition, thereby influencing wine quality potential (Ojedaet al., 2001; Deloire et al., 2004, 2005a, 2005b; Etchebarne et al., 2009;van Leeuwen et al., 2009). Soil and climate are acknowledged to havegreat effects on vinewater status (van Leeuwenet al., 2004, 2008;Coipelet al., 2006). Over a limited area, climatic factors may be considered asrather homogeneous for a given vintage. In contrast, soil depth and soilphysical properties may vary strongly over short distances, inducingnon-uniform soilwater availability and significant variability of the vine

water status within vineyards (Ojeda et al., 2005; Tisseyre et al., 2005;van Leeuwen et al., 2006; Taylor et al., 2010). In other respects, soilcompaction caused by tillage operations and machine traffic invineyards is also likely to have effects on vine growth and grapeproduction (Legros et al., 1998; Coulouma et al., 2006; Lagacherie et al.,2006).As a result, detailed characterization of the spatial variation of soilhydrogeophysical properties is of prime importance for the definition ofoptimal vineyardmanagement practices (Bramley and Hamilton, 2007;van Leeuwen et al., 2008; Costantini et al., 2009).

Ground-penetrating radar (GPR), electromagnetic induction (EMI)and electrical resistivity tomography (ERT) with mobile arrays areeffective geophysical techniques for fast, high-resolution and non-invasive determination of shallow subsurface hydrogeophysical prop-erties at the field scale through the measurement of soil electricalproperties (McNeill, 1980; Panissod et al., 1998; Huisman et al., 2003;Lambot et al., 2008). GPR is mainly sensitive to soil dielectricpermittivity, which primarily depends on soilwater content (Huismanet al., 2003). EMI and ERT are sensitive to soil electrical conductivity,which is essentially affected by soil water content, clay content, salinity

Plot B Plot D

Area 1

Plot C

Plot A

Fig. 1. Map of the soil apparent electrical conductivity measured with EMI over theentire vineyard area (33 ha) at an operating frequency of 15 kHz. Note that the colorscale is nonlinear. Data collected from the 6th to the 18th August 2009.

114 F. André et al. / Journal of Applied Geophysics 78 (2012) 113–122

and temperature (Corwinand Lesch, 2005; Friedman,2005). These threetechniques have already been used in vineyards to characterize soilproperties and/or to investigate the distribution of soil water content.GPR was applied in several studies to estimate vineyard soil watercontent with high spatial resolution (Hubbard et al., 2002; Grote et al.,2003;Hubbard andRubin, 2004; Lunt et al., 2005). Several authors foundsoil electrical conductivity or its reciprocal, soil electrical resistivity, to bea relevant parameter for explaining spatial variability of vine waterstatus and grape production and delineating management units withinvineyards (Bramley, 2005; Tisseyre et al., 2005; Goulet and Barbeau,2006; Acevedo-Opazo et al., 2008;Morari et al., 2009; Taylor et al., 2009;Courjault-Radéet al., 2010).Yet, noneof theseworks combinedGPR, EMIandERTdatawhile itwould allow tomerge complementary informationand would lead to more accurate characterization of soils (Moghadaset al., 2010a).

The general objective of this study is to produce high-resolutionmaps of the soil stratigraphy of a vineyard located in south of France(Saint-Emilion). In this paper, we present results of GPR, EMI and ERTsurveys performed over a 30-ha vineyard area characterized by strongspatial variations of soil properties. Data from the three geophysicaltechniques are jointly analyzed in order to relate the spatial patternsof these measurements to the distribution of soil types within thearea. Furthermore, these analyses are completed by the integration ofnormalized difference vegetation index (NDVI) data allowing toinvestigate the influence of soil characteristics on vine vigor andwaterstatus.

2. Material and methods

2.1. Site description

The studywas carriedout in a vinery of the Saint-Emilion appellationcontrolée, located approximately 50 km to the east of Bordeaux,Gironde, France. The study area extends over a 37 hectares area, with33hectares of vineyard. The climate of the region is oceanicwith ameanannual air temperature of 13.7 °C and 811 mm annual rainfall depth.

The substrate of the study area consists of amosaic of non-calcareoussoils with various textures developed from fluvial geological formationsof the Lower Oligocene and from Quaternary alluvial and colluvialmaterials. These different soil types may be classified into three maincategories according to the texture: sand over clay soil, gravelly-sandysoil and sandy soil with pseudogley, denominated, respectively, asPLANOSOL, PEYROSOL, and REDOXISOL or REDUCTISOL following theFrench classification (Baize and Girard, 1998). The sand over clay soilconsists of a sandy layer (30–50 cm thickness) superposing a compactclay horizon which has a relatively high water retention capacity. Thegravelly-sandy soil corresponds to sand mixed with more than 50%coarse (N2 mm diameter) grains and has a relatively lower waterretention capacity. Finally, the sandy soil has a seasonally variablewatertable ranging between 0.5 and 3.0 m below ground surface. These soiltypes result in contrasting wine quality potentials. Indeed, morefrequent mild water stresses of grapevines growing on gravelly-sandyand sand over clay soils are favorable to the production of high qualitywines while larger water availability for vines on soils with pseudogleyis prejudicial to wine quality (Renouf et al., 2010).

2.2. Data acquisition

GPR and EMI data were collected in a short time interval alongidentical transects during three differentperiods: (1) from the 6th to the18th August 2009, (2) on the 29th March 2010, and (3) on the 1st April2010. During the first period, 30 hectares of the vineyard area werecoveredwith transects spaced approximately 4 m apart (i.e., 1 in 3 rowswere investigated). The two latter measurement campaigns focused onrestricted areas (see plots A and B in Fig. 1) with a resolution of around1.30 mseparationbetween contiguous transects. Thefirstmeasurement

period was characterized by dry and stable soil conditions while thesecond and the third ones occurred after 25 mm and 28mm rainfallevents, respectively.

EMImeasurements were carried out with a Profiler sensor (EMP-400,Geophysical SurveySystems, Inc., Salem,Massachusetts,USA)operating invertical dipole orientation at three frequencies (5, 10 and 15 kHz). EMIsurveys were performed at walking speedwith the sensor held at around0.20 m above the ground surface. EMI data were recorded every second,corresponding to an average sampling interval of about 0.80 m, andweregeo-referenced bymeans of a differential GPS. In order to avoid problemsof data comparability due to sensor calibration issues (see e.g., Sudduthet al., 2001; Abdu et al., 2007; Lavoué et al., 2010), the calibration of theProfiler was kept constant for all the EMI campaigns.

GPR measurements were performed using a time-domain system(model SIR-20, Geophysical Survey Systems, Inc., Salem, Massachusetts,USA)equippedwitha transmitting–receiving400 MHzcenter-frequencybowtie antenna (∼0.15 m antenna offset)at ground level. GPR datawerecollectedwitha sampling interval of 5 cm,using a time rangeof 50 ns and512 samples per scan were recorded with 16 bits per sample.

ERTdatawere acquired on the4thApril 2005 and from the 2nd to the4th May 2005 all over the vineyard area using an automatic resistivityprofiling (ARP) system (Panissod et al., 1998). This device allowsdetermination of vertical variations in soil electrical resistivity at threeinvestigation depths (i.e., 0–0.8 m, 0–1.2 m and 0–2.2 m). The raw ERTdata were processed initially using 1D median filtering along theacquisition profiles andwere then re-interpolated by a bicubic spline ona square mesh of 3 m.

In addition, reflectance data for near-infrared and red wavelengthswere collected on the 30th July 2009 for NDVI determination, using an

115F. André et al. / Journal of Applied Geophysics 78 (2012) 113–122

airborne camera with 10 cm horizontal resolution. Healthy vegetationgenerally absorbs most of the red wavelengths that falls on it andreflects a large portion of the near-infrared wavelengths, whileunhealthy vegetation reflects more red wavelengths and less near-infrared wavelengths. Therefore, quantifying the relative differencebetween the near-infrared reflectance ‘peak’ and the red reflectance‘trough’, the NDVI index may be used as an indicator of vegetationvigor (e.g., Hall et al., 2002; Goutouly et al., 2006; Acevedo-Opazoet al., 2007, 2008; Mazzetto et al., 2009). Vine rows were detected onthe images using an automatic procedure such that pixels corre-sponding to inter-rows were suppressed in order to remove the effectof soil for NDVI computation. NDVI was determined for each vine byvectorizing the area occupied by each individual and attributing theaverage NDVI value over this area. Finally, kriging was applied toprovide a continuous NDVI map of the vineyard. This index may beused as an indicator of vegetation vigor and has been recently appliedin several vineyard studies.

3. Results and discussion

3.1. Imaging spatial variations of soil properties using EMI and ERT data

Fig. 1 presents the map of soil apparent electrical conductivitymeasured with EMI at 15 kHz in August 2009. As a result of the dryconditions during the measurements and the predominance of sand inthe upper soil layers, soil electrical conductivity is generally relativelylow with 75% of the measured values ranging from 0 to 58 ms/m.Significant spatial correlation is observed in the data, such that thevineyard domain can be subdivided into large areas of comparableconductivity values. These areas are delimited by well-defineddiscontinuities and show fairly good agreement with the distributionof the different soil types over the area, as illustrated by the comparisonbetween the soil map presented in Fig. 2 and the corresponding zone ofEMI measurements delimited by the dashed line in Fig. 1. Consideringthe 0–100 ms/m soil conductivity range (i.e., excluding the noisy data(see below)), lowest and highest electrical conductivity values areobserved for gravelly-sandy soils (PEYROSOL) and sand over clay soils(PLANOSOL), respectively, while intermediate values are found for soilswith temporary or permanent water logging (REDOXISOL and REDUC-TISOL). However, strong deviations from these general patterns wereobserved near the limits of the plots; this is likely to result from astronger influence of cultural operations (i.e., tillage, tractor traffic) on

REDUCTISOLREDOXISOL

PEYROSOLPEYROSOL on deep gley

0

Fig. 2. Distribution of the different soil types within the vineyard area 1 delimited by the daand Girard, 1998).

geophysical measurements within these plots as these patterns followplot limits. A particularly strong evidence of the influence of culturaloperations on soil electrical conductivity is the relatively high valuesobserved for plot B (see Fig. 1), resulting from theuse of heavymachinesduring too wet soil conditions for the preparation of this plot to vineplantation and which induced soil compaction (see Section 3.3.2 forfurther details). In addition, linear pattern anomalies appeared inportions of the data, such that electrical conductivity values alongcertain transects contrasted significantly with those of the surroundingarea. Such observations may partly arise from the influence of themetallic wires holding the vines (e.g., Lamb et al., 2005). Finally, noisydata alternating between very low and very high soil electricalconductivity values were observed along portions of the eastern limitand along the central path of the vineyard; these observations areexplained by thepresence of power lines buried in the soil.While spatialpatterns of soil electrical conductivity measured at frequencies of 5 and10 kHz are similar to those presented here for 15 kHz, soil electricalconductivity values tended to increase with decreasing operatingfrequency. Measurement differences between 10 and 15 kHz generallydid not exceed 10%; nevertheless, in high conductivity areas differencesmay reach 40%. More significant differences in electrical conductivity(up to 70%) were seen between 5 and 15 kHz, however. Theseobservations indicate increasing soil electrical conductivity withdepth, which would arise from increasing water and clay content.

EMI measurements are also in good agreement with soil electricalresistivity data collected in April-May 2005 (Fig. 3). Areas with low soilelectrical conductivity generally correspond with zones showing highelectrical resistivity values. Nevertheless, deviations from these generalobservations were seen in some areas, notably for plots B, C and D inFig. 1, which show both high electrical conductivity and high electricalresistivity values. Such results may partly arise from differences ofinvestigation depths between the two techniques. Indeed, the Profilersensor in vertical dipole orientation would present sensitivity for soillayers up to relatively large depths, roughly between 1.5 m and 4.0 mdepending on the operating frequency and on the soil properties, whilethe maximum investigation depth for ERT was estimated to be around2.2 mwith the device used. Therefore, electrical conductivity values forsoil presenting relatively deep and highly conductive soil layers due tothe presence of clay and/or water are likely to present higher values forsoil electrical conductivitywith EMI thanwith ERT. Another explanationfor these observations could be the occurrence of strong modificationsof the soil structure in the time interval between ERT and EMI

100 300 m200

shed line in Fig. 1. Soil units are denominated following the French classification (Baize

Plot D

Area 1

Plot C

Plot B

ohm.m

Plot A

Fig. 3. Map of soil apparent electrical resistivity with ERT over the entire vineyard area(33 ha) for 0–2.2 m investigation depth. Logarithmic scale 10–800 ohm.m. Datacollected on the 4th April 2005 and from the 2nd to the 4th May 2005.

Fig. 4. Vegetation index (NDVI) as a function of soil electrical conductivity measuredwith EMI at an operating frequency of 15 kHz. Colors identify vineyard plots.Surrounded points correspond to plot B (see Fig. 1).

Plot D

Area 1

Plot B

Plot C

Plot A

Fig. 5.Map of the vegetation index (NDVI) measured all over the vineyard area (33 ha).Large NDVI values correspond to high vegetation vigor. Data collected on the 30th July2009.

116 F. André et al. / Journal of Applied Geophysics 78 (2012) 113–122

campaigns, such as notably compaction which would have increasedsoil electrical conductivity as already aforementioned for plot B (see alsoSection 3.3.2). Finally, these discrepancies between ERT and EMI datamay also be due to varying seasonal soil water conditions or fromartifacts (e.g., trellising) influencing measurements; these aspects willbe discussed in more details when examining data from plot A inSection 3.3.1.

3.2. Soil electrical conductivity and NDVI

As the vegetation vigor integrates the influence of all environmentalfactors, vine NDVI can potentially be related to soil characteristics, suchas electrical conductivity. In particular, high soil electrical conductivityvalues correspond to either high water, clay or nutriment contents, or acombination of these three. We therefore expect that a high electricalconductivitywould correspond to a highNDVI as these factors generallyfavor plant growth. Yet, soil compaction may have an opposite effect,namely, increasing soil electrical conductivity while limiting plantgrowth. Relating soil electrical conductivity to the NDVI may thereforebeuseful toprovide insights into the origin of plant growth issues. In thisrespect, Fig. 4 shows NDVI as a function of soil electrical conductivitymeasured with EMI at 15 kHz frequency all over the study area,excluding the zones presenting noisy EMI data in the eastern part andalong the central path of the vineyard (see Fig. 1). On this graph, eachcolor represents a specific vineyard plot. The plots individualize ratherclearly within the cloud of points, showing contrasting values for theseparameters, as already observable on the corresponding maps (Figs. 1and 5). Nevertheless, no clear relationship appears between the twoparameters. An absence of correlation between NDVI and soil electricalconductivity is also found when considering the different soil typesseparately (data not shown). Plot B (points surrounded on the graph)shows very lowNDVI values,which indicates vinevigor problems in thisarea; potential causes will be investigated in Section 3.3.2.

3.3. Detailed analyses of specific plots

Detailed analyseswere carried out on areasA andB (see Fig. 1). Here,plot A shows peculiar spatial variations in soil electrical conductivity(i.e., conductive triangular area in the north-east corner of the plot)while very low NDVI values (i.e., poor vine growth) are observed in thesoutheastern part of plot B (see Section 3.2).

3.3.1. Plot ATo further investigate the soil electrical conductivity pattern of

plot A, EMI and GPR data were jointly collected and analyzed alongfour transects at different positions along the southwest–northeast

117F. André et al. / Journal of Applied Geophysics 78 (2012) 113–122

axis of the plot (see Fig. 6(a)). Fig. 7 presents (a) variations of the soilelectrical conductivity measured with EMI at the three consideredfrequencies, (b) the evolution of soil electrical conductivity deter-mined from ERT measurements carried out in April-May 2005 forthree different investigation depths, and (c) GPR reflection profiles ofthe soil stratigraphy along these transects. In each case, EMI and ERTsoil electrical conductivities show similar trends, characterized byrelatively low and stable values in the southern part of the transectswhich increase at intermediate distances before stabilizing to higherelectrical conductivities at the northern end. The extent of the highconductivity plateau in the northern area of Figs. 6(a) and (b)increases from west to east. For a given transect, the increase in soilelectrical conductivity occurs at the same locations for EMI and ERTmeasurements. Despite these similar patterns in EMI and ERT data,large differences in absolute values are found between the twotechniques, with generally much larger soil electrical conductivityvalues for EMI compared to ERT. Such differences may arise fromseveral reasons. First, as aforementioned, the adopted vertical dipoleconfiguration for Profiler presents larger investigation depth than thatof the ARP system used for ERT measurements. Therefore, due to theincrease of soil water and/or clay contents with depth, EMI is expectedto provide higher values of soil electrical conductivity than ERT;furthermore, the differences between the two techniques increase in

PEYROSOL

5.6 m X position

a

Y

X

X position35.0 m

X position68.6 m

X position93.8 m

REDOXISOL

c

Fig. 6. Map of (a) soil apparent electrical conductivity (EMI), (b) apparent electrical resisti

the north of the transects where a clay layer appears at shallow depth.A second explanation could be different soil properties during the twomeasurement campaigns. Meteorological data collected at the studysite indicated cumulated precipitation amounts of 30.5 mm and70.0 mm during the 2 weeks preceding the ERT surveys (i.e., from21st March to 3 rd April 2005 and from 17th April to 1st May 2005,respectively) and correspondingmean air temperatures of 14.5 °C and14.9 °C as well as 34.0 mm and 42.6 mm potential evapotranspiration(ET0), while 13.8 mm precipitation, 21 °C mean air temperature and75.8 mm ET0 were recorded before the EMI campaign (i.e., from 23rdJuly to 5th August 2009). Assuming the soil hydraulic properties to besimilar for the two campaigns, these climatic conditions should resultin a wetter soil for the ERT survey compared with the EMI survey. Thissuggests that the higher soil electrical conductivity values observedfor EMI may not be attributed to a higher water content, but to otherfactors. Besides, no strong modification of the soil structure and/ortexture was noticed for this plot during the time interval separatingthe two campaigns. Another explanation for the differences betweenEMI and ERT could result from the commonly reported lack ofrobustness of commercial EMI sensor calibration, providing semi-qualitative measurements of soil electrical conductivity (Sudduthet al., 2001; Abdu et al., 2007; Gebbers et al., 2009; Lavoué et al.,2010). Finally, another possible source of discrepancies between EMI

b

d

vity (ERT), (c) soil type distribution, and (d) NDVI for plot A (see Fig. 1, ∼1.1 ha area).

0 20 40 60 80 100

20

40

60

EM

I (m

S/m

)

Transect at X position 5.6 m

a

5 kHz

10 kHz

15 kHz

0 20 40 60 80 100

10

20

30

40

ER

T (

mS

/m)

b

Tim

e (n

s)

c

0 10 20 30 40 50 60 70 80 90 100

0

20

40

20

40

60

EM

I (m

S/m

)

Transect at X position 35 m

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

10

20

30

40

ER

T (

mS

/m)

Tim

e (n

s)

0 10 20 30 40 50 60 70 80 90 100

0

20

40

0 10 20 30 40 50 60 70 80 90 100

20

40

60

EM

I (m

S/m

)

a Transect at X position 68.6 m

0 10 20 30 40 50 60 70 80 90 100

10

20

30

40

ER

T (

mS

/m)

b

Tim

e (n

s)

c

a

b

c

a

b

c

0 10 20 30 40 50 60 70 80 90 100

0

20

40

0 10 20 30 40 50 60 70 80 90 100

20

40

60

EM

I (m

S/m

)

Transect at X position 93.8 m

0 10 20 30 40 50 60 70 80 90 100

10

20

30

40

ER

T (

mS

/m)

Tim

e (n

s)

0 10 20 30 40 50 60 70 80 90 100

0

20

40

Fig. 7. Variation of soil electrical conductivity (a) measured with EMI and (b) determined from ERT measurements, and (c) GPR profiles of soil stratigraphy along four transects atdifferent positions along the southwest-northeast axis of plot A (see Fig. 6 (a)).

118 F. André et al. / Journal of Applied Geophysics 78 (2012) 113–122

and ERT data is the presence of measurement artifacts. In this respect,the metallic wires sustaining the vines are likely influencing EMIresults and, as aforementioned, may be responsible for the linearpatterns observed in the raw soil electrical conductivity map (Fig. 1).In other respects, the accuracy of ERT data depends on the quality ofthe galvanic contact between the soil and the electrodes (Tabbagh,1988). Furthermore, ERT measurements with ARP are also acknowl-edged to be affected by the intensity of soil tillage and the timeinterval with respect to the previous tillage (Basso et al., 2010).

An examination of the GPR scans (Fig. 7(c)) reveals that the increasein soil electrical conductivity systematically corresponds to theappearance of a strong continuous reflection in the 10–20 ns wavepropagation time interval, while lower conductive zones show weakerand less structured reflections occurring at slightly later times.Combining GPR scans yields three-dimensional (3-D) images of theplot stratigraphy, as presented in Fig. 8(a). This figure clearly shows adecrease in propagation time that is consistent withmore predominantreflection events in the northwestern portions of the plot (i.e., withincreasing abscissas of the y-axis). A similar pattern is observed inFig. 8(b) which presents a 3-D view of the strongest measuredreflections. Moreover, the reflections appearing at the shortestpropagation times form a triangular area in the north-east corner of

the plot, which is consistent with the zone of higher soil electricalconductivity observed from the EMI and the ERT measurements(Figs. 6(a) and (b)). These observations are also in agreement withthe distribution of the soil within the plot; here, the northern triangulararea is occupied by a sandy soil with pseudogley (REDOXISOL), while agravelly-sandy (PEYROSOL) soil is found in the southern part of the plot(Fig. 6(c)). The NDVI data also show contrasting values between thenorthern and the southern parts of the plot (Fig. 6(d)), which isconsistent with the transition delimitated by the EMI and ERT data.Higher NDVI values, reflecting greater vegetation vigor, are observed forvines growing on the REDOXISOL. Such observations would arise fromlarger water availability during summer on the REDOXISOL comparedwith the PEYROSOL due to the presence of a shallow water table.

3.3.2. Plot BFig. 9(b) showsGPRmeasurements collected along a transect of plot

B covering zones with contrasting soil properties (i.e., white dotted linein Fig. 1). A continuous strong reflection is observed in the 20–30 nstimewindow overmost of the transect length except between 29–32 mand 62–76 m position intervals for which the reflection is much lesspronounced; these zones are highlighted by the red arrows in Fig. 9(b).Two pits were dug consecutively to the measurements at different

Fig. 8. (a) Three-dimensional GPR images profiles for the soil of plot A (see Fig. 1) and (b) image of the strongest reflections considering a relative signal amplitude threshold of 85%.Axes x and y correspond to the local cartesian system used in Fig. 6(a). Assuming an average �r of 5 for relatively dry sand, 10 ns correspond to a depth of 0.67 m.

c

b

Position (m)

Tim

e (n

s)

0 10 20 30 40 50 60 70 80 90

0

10

20

30

40

50

a

Fig. 9. (a) Differences of vine development at two specific locations along the transect represented by the white dotted line in Fig. 1, (b) GPR reflection profile of the transect and(c) soil profiles up to 1.2 m depth.

119F. André et al. / Journal of Applied Geophysics 78 (2012) 113–122

120 F. André et al. / Journal of Applied Geophysics 78 (2012) 113–122

locations on the same transect in support to the interpretation of theGPRdata. The first pitwas located in a zone of the GPR scan presenting astrong reflection (25 m location) and the second pit was situated at aposition without strong reflection (64 m location). The locations of thetwo pits are depicted by the vertical black lines on the GPR image.Observation of the soil profile from the pit opened at 25 m locationreveals the existence of a strong discontinuity in the soil structure,resulting from the presence of a compacted soil layer at shallow depthdue to the passage of agricultural machines in wet soil conditions. Incontrast, at the 64 m location, the soil profile is more homogeneous,leading to a lower contrast in soil electrical properties and a weakerwave reflection (Fig. 9(c)). As illustrated by the pictures in Fig. 9(a),strong differences in vine vigor exist between the two locations. Indeed,the vines show normal development at the 64 m position while theypresent growth problems around the 25 m position, presumably givenlimited root development due to soil compaction (see Section 3.2). Therestricted spatial extend of soil compaction within the plot results fromthe concentration of the machine traffic at the corresponding locations.

The extent of the more compacted zone in plot B can be evaluatedexamining the spatial electrical conductivity patterns over the area, andtheir evolution during varying moisture conditions. Fig. 10(a) and (b)presents maps of soil apparent electrical conductivity measured withEMI at 15 kHz on plot B on the 29th March 2010 and on the 1st April2010, respectively. The first data set was acquired two days after a25 mmrain event,while the secondset ofmeasurementswasperformedafter a 28 mm rainfall event. For the 29th March (Fig. 10(a)), high soilelectrical conductivity values are mainly observed over an area in thesouthern part of the plot, extending from the southeast corner to themiddle of thewestern limit of theplot (defined as zone 1). Similar spatialpatterns were observed on the 1st April (Fig. 10(b)); however, the zoneof high soil electrical conductivity values expanded toward thenorthwest corner of the plot (zone 2), while a highly conductive areaformed near the northeast corner (zone 3). The differences between thetwodata sets (April-March) generally donot exceed 20%, except for zone2 and zone 3, which show an increase of soil electrical conductivity by asmuch as 60% (Fig. 10(c)). Areas with persistently low conductivity forthese measurement dates correspond to well-drained areas. Theseobservations are in accordancewith water dynamics of a gravelly-sandysoil (PEYROSOL), which occupies the entire plot area (see Fig. 2). Incontrast, high soil electrical conductivity values observed for the twodates in zone 1may be an indicator of compacted soil. Indeed, for a giventype of soil, electrical conductivity tends to increase as soil compactionincreases (Besson et al., 2004; Hedley et al., 2004; Petersen et al., 2006).In addition, soil compactness would limit natural drainage and rainwater would accumulate at the depth of compaction, thereby leading to

Zone 1

Zone 3

Zone 1

a b

Fig. 10.Maps of EMI-derived soil apparent electrical conductivity measured at 15 kHz over plbetween April and March 2010.

elevated soil electrical conductivity values. The soil profile presenting acompacted layer at 25 m locationon the transect (see Fig. 9) is situated inzone 1. The large increases in soil electrical conductivity of zones 2 and 3following rain events may also reflect accumulation of water at shallowdepth, likely because of higher soil compaction.Nevertheless, the lowsoilelectrical conductivity values observed for these zones on the 29thMarch, two days after a rainfall, indicate that water ponding is moretemporal than for zone 1, presumably as a result of lower soil compaction.Further, zone 1, zone 2 and, to a lesser extent, zone 3 represent lowerNDVIvalues compared with the rest of the plot (Fig. 11), which suggests thatsoil compaction is a factor contributing to the reduced vine vigor withinthis area (see Section 3.2).

4. Conclusions and perspectives

In this study, GPR, EMI and ERT surveys were carried out in avineyard in order to image the3-D structure of the soil and to determineits hydrogeophysical properties. The joint analysis of these geophysicaldata sets presented in this paper reveals strong spatial variations in soilproperties,whichare in accordancewith thedistribution of thedifferentsoil types within the study area. These results highlight anthropogenicsoil compaction as a key factor contributing to vine vigor problemsobserved locally in the vineyard. Combining geophysical data setspermitted efficient and non-invasive characterization of soil properties.Here, EMI and ERT data provided general spatial patterns in soilelectrical characteristics and enabled delineation of the main soil units,while GPR provided detailed information on soil stratigraphy. Althoughgood agreement is generally observed between data from the differenttechniques, EMI measurements appeared to be more affected than ERTdata by the trellis and by the presence of metallic objects (e.g., pipes,power lines,…) in the soil. Based on the collected data, a subdivision ofsomeplots of the vineyard intomore homogeneous unitswith regard tosoil properties would potentially allow for a better control of the winequality, given the significant effects of soil type on grape ripeness andcomposition (van Leeuwenet al., 2008; Renouf et al., 2010). Futureworkwill focus on the filtering of the measurement artifacts out of the EMIdata using advanced geostatistical techniques and on full-waveforminversion of GPR and EMI data to retrieve the properties of the differentsoil layers (e.g., Lambot et al., 2004, 2010; Moghadas et al., 2010b).Suchwork should allow us to investigate spatial and temporal variationin soil hydraulic properties and water availability between and withintheplots of thedomain (Ritter et al., 2004). Suchmoisture information isuseful for optimal vineyard management given the fundamental role ofwater in vine and grape development and overall in wine quality.

Zone 1

Zone 3Zone 2Zone 2

c

ot B (see Fig. 1, ∼0.6 ha area) on (a) March 2010, (b) April 2010 and of (c) the difference

Fig. 11. Map of NDVI for plot B (see Fig. 1, ∼0.6 ha area). Large NDVI values correspondto high vegetation vigor. Data collected on the 30th July 2009.

121F. André et al. / Journal of Applied Geophysics 78 (2012) 113–122

Acknowledgments

This research was supported by the DIGISOIL project financed bythe European Commission under the 7th Framework Programme forResearch and Technological Development, Area “Environment”,Activity 6.3 “Environmental Technologies” and by the Fonds de laRecherche Scientifique (FNRS, Belgium). We are grateful to Mr. X.Cassassoles from Geocarta SA (Paris, France) for providing us the ERTdata.We also gratefully thank the two reviewers for their constructivecomments and suggestions on an earlier version of the manuscript.

References

Abdu, H., Robinson, D.A., Jones, S.B., 2007. Comparing bulk soil electrical conductivitydetermination using the DUALEM-1S and EM38-DD electromagnetic inductioninstruments. Soil Science Society of America Journal 71 (1), 189–196.

Acevedo-Opazo, C., Tisseyre, B., Guillaume, S., Ojeda, H., 2007. Test of NDVI informationfor a relevant vineyard zoning related to vine water status. Precision agriculture'07. Papers presented at the 6th European Conference on Precision Agriculture,Skiathos, Greece, 3–6 June, 2007. Wageningen Academic Publishers, pp. 547–554.

Acevedo-Opazo, C., Tisseyre, B., Guillaume, S., Ojeda, H., 2008. The potential of highspatial resolution information to define within-vineyard zones related to vinewater status. Precision Agriculture 9 (5), 285–302.

Baize, D., Girard, M.C., 1998. A sound Reference Base for Soils. INRA, Paris.Basso, B., Amato, M., Bitella, G., Rossi, R., Kravchenko, A., Sartori, L., Carvahlo, L.M.,

Gomes, J., 2010. Two-dimensional spatial and temporal variation of soil physicalproperties in tillage systems using electrical resistivity tomography. AgronomyJournal 102 (2), 440–449.

Besson, A., Cousin, I., Samouëlian, A., Boizard, H., Richard, G., 2004. Structuralheterogeneity of the soil tilled layer as characterized by 2D electrical resistivitysurveying. Soil and Tillage Research 79 (2), 239–249.

Bramley, R.G.V., 2005. Understanding variability in winegrape production systems - 2.Within vineyard variation in quality over several vintages. Australian Journal ofGrape and Wine Research 11 (1), 33–42.

Bramley, R.G.V., Hamilton, R.P., 2007. Terroir and precision viticulture: are theycompatible? Journal International des Sciences de la Vigne et du Vin 41 (1), 1–8.

Coipel, J., Lovelle, B.R., Sipp, C., Leeuwen, C.V., 2006. “Terroir” effect, as a result ofenvironmental stress, depends more on soil depth than on soil type (Vitis vinifera L.cv. Grenache noir, Cotes du Rhone, France, 2000). Journal International des Sciencesde la Vigne et du Vin 40 (4), 177–185.

Corwin, D.L., Lesch, S.M., 2005. Apparent soil electrical conductivity measurements inagriculture. Computers and Electronics in Agriculture 46 (1–3), 11–43.

Costantini, E.A.C., Pellegrini, S., Bucelli, P., Storchi, P., Vignozzi, N., Barbetti, R.,Campagnolo, S., 2009. Relevance of the Lin's and Host hydropedological models

to predict grape yield and wine quality. Hydrology and Earth System Sciences 13(9), 1635–1648.

Coulouma, G., Boizard, H., Trotoux, G., Lagacherie, P., Richard, G., 2006. Effect of deeptillage for vineyard establishment on soil structure: a case study in SouthernFrance. Soil and Tillage Research 88 (1/2), 132–143.

Courjault-Radé, P., Llubes, M., Darrozes, J., Munoz, M., Maire, E., Hirissou, N., 2010. A 2Delectrical resistivity tomography survey of a vineyard plot of the Gaillac appellation(France): interpretation with respect to possible implications on vinewater supply.Journal International des Sciences de la Vigne et du Vin 44 (2), 51–60.

Deloire, A., Carbonneau, A., Wang, Z., Ojeda, H., 2004. Vine and water: a short review.Journal International des Sciences de la Vigne et du Vin 38 (1), 1–13.

Deloire, A., Ojeda, H., Zebic, O., Bernard, N., Hunter, J.J., Carbonneau, A., 2005a. Influenceof grapevine water status on the style of wine. Progrès Agricole et Viticole 122 (21),455–462.

Deloire, A., Vaudour, E., Carey, V., Bonnardot, V., Van Leeuwen, C., 2005b. Grapevineresponses to terroir: a global approach. Journal International des Sciences de laVigne et du Vin 39 (4), 149–162.

Etchebarne, F., Ojeda, H., Deloire, A., 2009. Influence of water status on mineralcomposition of berries in‘ Grenache Noir’ (Vitis vinifera L.). Vitis 48 (2), 63–68.

Friedman, S.P., 2005. Soil properties influencing apparent electrical conductivity: areview. Computers and Electronics in Agriculture 46 (1–3), 45–70.

Gebbers, R., Luck, E., Dabas, M., Domsch, H., 2009. Comparison of instruments forgeoelectrical soil mapping at the field scale. Near Surface Geophysics 7 (3),179–190.

Goulet, E., Barbeau, G., 2006. Contribution of soil electric resistivity measurements tothe studies on soil/grapevine water relations. Journal International des Sciences dela Vigne et du Vin 40 (2), 57–69.

Goutouly, J.P., Drissi, R., Forget, D., Gaudillère, J.P., 2006. Characterization of vine vigourby ground based NDVI measurements. 6th International Terroir Congress.Bordeaux, France, pp. 237–241.

Grote, K., Hubbard, S., Rubin, Y., 2003. Field-scale estimation of volumetric watercontent using ground-penetrating radar groundwave techniques. Water ResourcesResearch 39 (11), 1321.

Hall, A., Lamb, D.W., Holzapfel, B., Louis, J., 2002. Optical remote sensing applications inviticulture— a review. Australian Journal of Grape andWine Research 8 (1), 36–47.

Hedley, C.B., Yule, I.J., Eastwood, C.R., Shepherd, T.G., Arnold, G., 2004. Rapididentification of soil textural and management zones using electromagneticinduction sensing of soils. Australian Journal of Soil Research 42 (4), 389–400.

Hubbard, S., Rubin, Y., 2004. The quest for better wine using geophysics. Geotimes 8,30–34.

Hubbard, S., Grote, K., Rubin, Y., 2002. Mapping the volumetric soil water content of aCalifornia vineyard using high-frequency GPR ground wave data. The Leading Edge21 (6), 552–559.

Huisman, J.A., Hubbard, S.S., Redman, J.D., Annan, A.P., 2003. Measuring soil watercontent with ground penetrating radar: a review. Vadose Zone Journal 2, 476–491.

Lagacherie, P., Coulouma, G., Ariagno, P., Virat, P., Boizard, H., Richard, G., 2006. Spatialvariability of soil compaction over a vineyard region in relation with soils andcultivation operations. Geoderma 134 (1/2), 207–216.

Lamb, D., Mitchell, A., Hyde, G., 2005. Vineyard trellising with steel posts distorts datafrom EM soil surveys. Australian Journal of Grape andWine Research 11 (1), 24–32.

Lambot, S., Slob, E.C., van den Bosch, I., Stockbroeckx, B., Vanclooster, M., 2004.Modeling of ground-penetrating radar for accurate characterization of subsurfaceelectric properties. IEEE Transactions on Geoscience and Remote Sensing 42,2555–2568.

Lambot, S., Binley, A., Slob, E., Hubbard, S., 2008. Ground penetrating radar inhydrogeophysics. Vadose Zone Journal 7 (1), 137–139.

Lambot, S., André, F., Jadoon, K., Slob, E., Vereecken, H., 2010. Full-waveform modelingof ground-coupled GPR antennas for wave propagation in multilayered media: theproblem solved? 13th International Conference on Ground Penetrating Radar(GPR). Lecce (Italy), pp. 1–5.

Lavoué, F., van der Kruk, J., Rings, J., André, F., Moghadas, D., Huisman, J., Lambot, S.,Weihermüller, L., vanderBorght, J., Vereecken, H., 2010. Electromagnetic inductioncalibration using apparent electrical conductivity modelling based on electricalresistivity tomography. Near Surface Geophysics 8, 553–561.

Legros, J., Argillier, J., Callot, G., Carbonneau, A., Champagnol, F., 1998. Les Sols Viticolesdu Languedoc: un état préoccupant. Le progrès Agricole et Viticole 115 (1),296–298.

Lunt, I.A., Hubbard, S.S., Rubin, Y., 2005. Soil moisture content estimation using ground-penetrating radar reflection data. Journal of Hydrology 307 (1–4), 254–269.

Mazzetto, F., Calcante, A., Mena, A., 2009. Comparing commercial optical sensors forcrop monitoring tasks in precision viticulture. Journal of Agricultural Engineering40 (1), 11–18.

McNeill, J.D., 1980. Electromagnetic terrain conductivity measurement at low inductionnumbers. Tech. Rep. Technical Note TN-6, Geonics Limited.

Moghadas, D., André, F., Slob, E.C., Vereecken, H., Lambot, S., 2010a. Joint full-waveformanalysis of off-ground zero-offset ground penetrating radar and electromagneticinduction synthetic data for estimating soil electrical properties. GeophysicalJournal International 182 (3), 1267–1278.

Moghadas, D., André, F., Vereecken, H., Lambot, S., 2010b. Efficient loop antennamodeling for zero-offset, off-ground electromagnetic induction in multilayeredmedia. Geophysics 75 (4), WA125–WA134.

Morari, F., Castrignano, A., Pagliarin, C., 2009. Application of multivariate geostatistics indelineating management zones within a gravelly vineyard using geo-electricalsensors. Computers and Electronics in Agriculture 68 (1), 97–107.

Ojeda, H., Deloire, A., Carbonneau, A., 2001. Influence of water deficits on grape berrygrowth. Vitis 40 (3), 141–145.

122 F. André et al. / Journal of Applied Geophysics 78 (2012) 113–122

Ojeda, H., Carrillo, N., Deis, L., Tisseyre, B., Heywang, M., Carbonneau, A., 2005. Precisionviticulture and water status II: quantitative and qualitative performance ofdifferent within field zones, defined from water potential mapping. XIVInternational GESCO Viticulture Congress, Geisenheim, Germany, 23–27 August,2005. Groupe d'Etude des Systemes de COnduite de la vigne (GESCO), pp. 741–748.

Panissod, C., Dabas, M., Hesse, A., Jolivet, A., Tabbagh, J., Tabbagh, A., 1998. Recentdevelopments in shallow-depth electrical and electrostatic prospecting usingmobile arrays. Geophysics 63 (5), 1542–1550.

Petersen, H., Fleige, H., Rabbel, W., Horn, R., 2006. Geophysical methods for imaging soilcompaction and variability of soil texture on farm land. Advances in Geoecology 38,261–272.

Renouf, V., Trégoat, O., Roby, J.P., van Leeuwen, C., 2010. Soils, rootstocks andgrapevines varieties in prestigious Bordeaux vineyards and their impact on yieldand quality. Journal International des Sciences de la Vigne et du Vin 44 (3),127–134.

Ritter, A., Munoz-Carpena, R., Regalado, C.M., Vanclooster, M., Lambot, S., 2004. Analysisof alternative measurement strategies for the inverse optimization of the hydraulicproperties of a volcanic soil. Journal of Hydrology 295 (1–4), 124–139.

Sudduth, K.A., Drummond, S.T., Kitchen, N.R., 2001. Accuracy issues in electromagneticinduction sensing of soil electrical conductivity for precision agriculture.Computers and Electronics in Agriculture 31 (3), 239–264.

Tabbagh, J., 1988. Traitement des données et élimination des valeurs erronées enprospection électrique en continu. Revue d Archéométrie 12, 1–9.

Taylor, J.A., Coulouma, G., Lagacherie, P., Tisseyre, B., 2009. Mapping soil units within avineyard using statistics associated with high-resolution apparent soil electricalconductivity data and factorial discriminant analysis. Geoderma 153 (1–2),278–284.

Taylor, J.A., Acevedo-Opazo, C., Ojeda, H., Tisseyre, B., 2010. Identification andsignificance of sources of spatial variation in grapevine water status. AustralianJournal of Grape and Wine Research 16 (1), 218–226.

Tisseyre, B., Ojeda, H., Carrillo, N., Deis, L., Heywang, M., 2005. Precision viticulture andwater status, mapping the predawn water potential to define within-vineyardzones. In: Shultz, H. (Ed.), Proceedings of 14th GESCO Congress. Geisenheim,Germany, pp. 23–27.

van Leeuwen, C., Seguin, G., 2006. The concept of terroir in viticulture. Journal of WineResearch 17 (1), 1–10.

van Leeuwen, C., Friant, P., Chone, X., Tregoat, O., Koundouras, S., Dubourdieu, D., 2004.Influence of climate, soil, and cultivar on terroir. American Journal of Enology andViticulture 55 (3), 207–217.

van Leeuwen, C., Goutouly, J.P., Azais, C., Costa-Ferreira, A.M., Marguerit, E., Roby, J.P.,Choné, X., Gaudillère, J.P., 2006. Intra-block variations of vine water status in timeand space. VIth International Terroir Congress. ENITA de Bordeaux — SyndicatViticole des Coteaux du Languedoc, Bordeaux, France, pp. 64–69.

van Leeuwen, C., Trégoat, O., Choné, X., Gaudillère, J.P., Pernet, D., 2008. Differentenvironmental conditions, different results: the role of controlled environmentalstress on grape quality potential and the way to monitor it. Thirteenth AustralianWine Industry Technical Conference. Australian Wine Industry Technical Confer-ence Inc., Adelaide, South Australia, pp. 39–46.

van Leeuwen, C., Trégoat, O., Choné, X., Bois, B., Pernet, D., Gaudillère, J.P., 2009. Vinewater status is a key factor in grape ripening and vintage quality for red Bordeauxwine. How can it be assessed for vineyard management purposes? JournalInternational des Sciences de la Vigne et du Vin 43 (3), 121–134.