Chapter

6

By Viacheslav Adamchuk, McGill University; Barry Allred, USDA–

ARS; James Doolittle, USDA–NRCS; Katherine Grote, Missouri 

University of Science and Technology; and Raphael Viscarra Rossel, 

CSIRO Land and Water

Tools for Proximal Soil 

Sensing

Introduction

Proximal soil sensing is a collection of technologies that employ 

a sensor close to, or in direct contact with, the soil. The sensor 

measures a soil property directly or indirectly. Viscarra Rossel 

et al. (2011) provide a description of proximal soil sensing, sensing 

technologies, and the soil properties these technologies can measure. 

This chapter describes different types of proximal sensing tools that can 

be used to map soil attributes of importance for agriculture and natural 

resource management.1

Soil properties vary in space and over time. As a consequence, 

they are seldom adequately described at field and landscape scales 

by traditional soil survey tools. Traditional methods of soil sampling 

and analyses provide detailed information at specific locations. This 

information, however, is limited in number, volume, and spatial 

coverage. See chapter 3 for a discussion of the standards and protocols 

used to examine and describe soils at the pedon scale in the field. 

At field and landscape scales, the characterization of the spatial and 

temporal variations is prohibitively time-consuming, expensive, and 

impractical using traditional point-sampling methods alone. Remote 

sensing (e.g., satellite images and aerial photos) can provide excellent 

spatial coverage, but measurements are mostly indirect and typically 

limited to the top 5–6 cm of soil. In addition, resolution is generally 

1 Trade or company names used in this chapter are provided as examples for 

informational purposes only. This use does not constitute an endorsement by USDA 

or the contributing authors of this chapter.
356 Chapter 6

too coarse to characterize the spatial variability of soil properties at 

intermediate field and landscape scales. Because of these limitations, 

proximal soil sensing is becoming increasingly popular as a way to 

fill in the data gap between high-resolution point data and the lower 

resolution remote-sensing data (Adamchuk et al., 2011; Adamchuk and 

Viscarra Rossel, 2011).

Data from proximal soil sensing technologies can be used in soil 

surveys of order 1, 2, or 3. They can be used to show how one or more soil 

properties vary over a portion of the landscape, to help estimate the range 

in property values for a particular soil series or map unit component, 

to refine the boundaries of soil map unit delineations, and to identify 

the location and extent of contrasting soil components within soil map 

unit delineations. Some of the methods can be used to document soil 

properties at specific locations (point data) when describing soil profiles. 

Table 6-1 shows the general application of various proximal soil sensing 

methods to soil survey activities. Definitions of soil survey orders are 

given in chapter 4.

This chapter is divided into two major parts. The first part discusses 

three geophysical methods: ground-penetrating radar, electromagnetic 

induction, and electrical resistivity. These methods have been used 

widely in the United States by the National Cooperative Soil Survey 

(NCSS) to document soil property variability in specific landscape 

settings and to identify the locations of contrasting soil components 

within map units. The second part discusses nine other proximal soil 

sensing methods that, to date, have had limited application by the 

NCSS. These technologies are included in this chapter because they 

have potential for expanded future use, especially in high-intensity 

surveys (i.e., order 1) and in recording properties when describing soil 

profiles.

Common Geophysical Methods

The three geophysical methods most commonly used for soils 

and agriculture are ground-penetrating radar (GPR), electromagnetic 

induction (EMI), and electrical resistivity (ER) (Allred et al., 2008a and 

2010).

Geophysical methods exploit contrasts in physical properties to 

indirectly measure, profile, and monitor differences in physico-chemical 

soil properties; locate soil, lithologic, and stratigraphic boundaries; 

and characterize soil patterns and features. Examples of the physical 
Soil Survey Manual 357

Table 6-1

Methods of Proximal Soil Sensing and Their Primary

Application in Soil Survey

[Order 1 surveys are high-intensity or special use surveys. Applications for 

order 1, 2, and 3 surveys include map unit boundaries, component composition, and/

or spatial distribution of properties (see chapter 4). Applications for point data include 

documentation of static or temporal soil properties.]

Method

Primary soil survey application

Map unit (spatial) data Point 

Order 1 Orders 2 & 3 data

Ground-penetrating radar X X

Electromagnetic induction X X

Electrical resistivity X X

Magnetometry X

Magnetic susceptibility X

Portable X-ray fluorescence X

Time domain reflectometry X

Optical reflectance X X X

Gamma-ray spectroscopy X X X

Mechanical interactions X X

Ion-selective potentiometry X X

Seismic X X

properties include dielectric permittivity, apparent electrical conductivity 

or resistivity, and magnetic susceptibility.

ER and EMI methods were initially used to assess soil salinity, but 

their use greatly expanded with the development of precision agriculture 

in the 1990s. Since the late 1970s, GPR has been used extensively 

by the National Cooperative Soil Survey as a quality-control tool to 

improve soil interpretations. Recent technological improvements have 

increased the use of these and other geophysical methods in soils. 

Improvements include instrumentation, computational capabilities, data 

processing, interpretative and display methods, and integration with 

other technologies (e.g., global positioning systems).
358 Chapter 6

Ground-Penetrating Radar (GPR)

Ground-penetrating radar is an impulse radar system. It transmits 

short pulses of very high and ultra-high frequency (from about 30 

MHz to 1.2 GHz) electromagnetic energy into the soil and underlying 

strata from an antenna. When these pulses contact an interface between 

layers with contrasting dielectric permittivity, a portion of the energy is 

reflected back to a receiving antenna. The more abrupt and contrasting 

the difference in dielectric permittivity, the greater the amount of energy 

that is reflected back to the receiving antenna. The receiving antenna 

records the amplitude of the reflected energy as a function of time, and 

the variation in amplitude is displayed on a video screen and stored 

for playback and processing. Interpretation of GPR data is generally 

performed by noting the arrival time of a reflection from a subsurface 

interface and associating the reflection with a known or suspected soil 

interface. To interpret the depth to an interface, the velocity of the pulse 

through the soil must be determined or the interface depth must be 

obtained by ground-truth measurements.

Ground-penetrating radar is most effective at sharp interfaces 

between materials of contrasting dielectric permittivity. Although 

influenced by bulk density and mineralogy, dielectric permittivity in soil 

is primarily controlled by water content. Thus, GPR is useful for imaging 

the interfaces between layers that contain different amounts of water. It is 

also very effective in determining the location of air-filled or water-filled 

voids (such as pipes) and metallic objects. GPR works best in coarse 

grained soils because electrically conductive materials (i.e., soils with 

high clay content and saline soils) weaken the signal.

A disadvantage of GPR is that resolution decreases with increasing 

depth of investigation and decreasing antenna frequency. Although 

higher frequency antennas provide higher resolution, they also provide 

lesser depth of investigation. Penetration depth is inversely proportional 

to the sounding frequency. In general, penetration with low-frequency 

antennas is less than 30 cm in saline soils and less than 1 m in wet, 

clayey soils (Daniels, 2004). In dry, sandy and gravelly soils, however, 

GPR penetration can exceed 50 m with low-frequency antennas (Smith 

and Jol, 1995). Profiling depths as great as 10 m have been recorded in 

organic soil materials that have very low electrical conductivity.

The speed, field economy, high resolution, and continuous 

measurement of GPR are assets in soil investigations. Modern GPR 

systems are self-contained and portable and have integrated GPS and 

real-time data visualization capabilities, which allow greater mobility 

and more effective use (fig. 6-1).
Soil Survey Manual 359

Figure 6-1

Modern GPR systems are light-weight, highly mobile, and integrated. A typical GPR 

system consists of a control unit (located beneath blue visor on the cart) with an 

antenna (orange box beneath the cart).

Examples of GPR Use in Soil Survey

Ground-penetrating radar has been used by soil scientists principally 

in order 1, 2, and 3 soil surveys. It serves as a quality control tool in 

documenting the taxonomic compositions and improving the interpretations of soil map units (Doolittle and Butnor, 2008). In these 

applications, GPR documents the presence, depth, lateral extent, and 

variability of diagnostic subsurface horizons. Typically, strong radar 

reflections are produced by abrupt interfaces between highly contrasting 

soil materials. Where soil conditions are suitable, GPR can determine 

the depth to contrasting master (B, C, and R) subsurface horizons and 

layers. Other soil horizons and layers have also been identified with 

GPR. Examples include buried genetic horizons, dense root-restricting 

layers, frozen soil layers, illuvial accumulations of organic matter, and 

cemented or indurated horizons. Ground-penetrating radar generally 

is unable to detect subtle changes in soil properties (e.g., structure, 

porosity, and texture), transitional horizons (e.g., AB, AC, and BC), or 
360 Chapter 6

vertical divisions of master horizons. However, GPR has been used to 

infer distinct vertical changes in soil color associated with abrupt and 

contrasting changes in organic carbon content.

Figure 6-2 is a radar record from an area of Pomona soils (sandy, 

siliceous, hyperthermic Ultic Alaquods) in north-central Florida. 

The upper boundaries of the spodic and argillic horizons are abrupt 

and separate contrasting soil materials. They therefore produce highamplitude reflections. On this radar record, the spodic horizon provides 

a continuous reflector that varies in depth from about 20 to 60 cm. The 

upper boundary of the argillic horizon is highly irregular and varies in 

depth from about 60 to 150 cm. Generally, argillic horizons provide 

smooth, continuous reflectors at more uniform depths than those shown 

in this example. The irregular topography of the upper boundary of this 

argillic horizon is attributed to underlying dissolution features associated 

with karst. The presence and varying depths to these two subsurface soil 

horizons were used to distinguish different soils along the radar traverse 

line.

Figure 6-2

A radar record showing well expressed spodic and argillic horizons in a Pomona soil in 

north-central Florida.

The radar record in figure 6-3 shows an abrupt and contrasting 

discontinuity that separates a silty eolian mantle from underlying sandy 

outwash. This stratigraphic discontinuity is an easily identified, laterally 
Soil Survey Manual 361

continuous, high-amplitude reflector that ranges in depth from about 85 

to 150 cm across the radar record. In southern Rhode Island, the depth to 

the discontinuity was used to distinguish areas of Bridgehampton soils 

(coarse-silty, mixed, active, mesic Typic Dystrudepts) and Enfield soils 

(coarse-silty over sandy or sandy-skeletal, mixed, active, mesic Typic 

Dystrudepts). Soil materials on different sides of this discontinuity differ 

from each other substantially in particle-size distribution, bulk density, 

and pore-size distribution. In addition, linear reflections in the lower 

material helped to confirm that the material is glacial outwash rather 

than till. Typically, till has a chaotic radar signature characterized by 

an abundance of point reflectors (from cobbles and boulders) and an 

absence of linear reflectors (which are typical for layered deposits). On 

this radar record, a dense Bw horizon appears as a weakly expressed 

linear reflector at a depth of about 35 cm.

Figure 6-3

A radar record showing a discontinuity separating a loamy eolian mantle from sandy 

glacial outwash in southern Rhode Island.

Hydropedological modeling requires detailed information on the 

depth and movement of water beneath soil landscapes. Sandy soils have 

a narrow capillary fringe, resulting in a relatively sharp interface between 

unsaturated and saturated soil materials. As a result, water tables are 

often distinguishable on radar records from sandy soils. 

Figure 6-4 is a surface normalized (i.e., elevation data were used to 

show topographic changes) radar record. It shows a low dune composed 

of very deep, excessively drained Oakville soils (mixed, mesic Typic 
362 Chapter 6

Udipsamments) in northwestern Indiana. On this radar record, the water 

table can be seen as a continuous, high-amplitude reflector between 

depths of about 2.5 and 4.0 m. Repetitive GPR measurements throughout 

the year can increase the level of confidence in hydropedological site 

assessments and reduce the number of wells needed for studies of water 

tables and ground-water flow.

Figure 6-4

A terrain-corrected radar record in which a water table provides a high-amplitude 

reflector in a dune field in Indiana.

Ground-penetrating radar has been used extensively on peatlands. 

GPR applications in peatlands include estimating the thickness and 

volume of peat deposits; distinguishing layers that differ in degree of 

humification, bulk density, and volumetric water content; characterizing 

underlying mineral sediments, stratigraphy, and hydrology and their 

relationships to present vegetation; and classifying and mapping organic 

soils.

Figure 6-5 is a radar record from a fen in a kettle depression in 

southeastern Massachusetts. The fen is an area of very deep, very poorly 

drained Freetown soils (dysic, mesic Typic Haplosaprists). Abrupt and 

strongly contrasting changes in water content make the interface between 

organic and mineral material distinguishable on the radar record. This 

interface forms a conspicuous reflection that varies in depth from about 

0.36 meter to 5.4 meters.

In addition to detecting subsurface interfaces, GPR can be used as a 

tool for quantitatively mapping soil water content (Huisman et al., 2003). 

This mapping can be done because of the strong dependence of dielectric 

permittivity on soil water content. The dielectric permittivities of air and 
Soil Survey Manual 363

Figure 6-5

The thickness of organic soil materials that overlie coarse textured glacial outwash 

is evident on this radar record from an area of Freetown soils in southwestern 

Massachusetts.

water are 1 and ~80, respectively. The permittivity of most mineral soils 

ranges from ~3 to 40, depending on soil water content. The permittivity 

of dry mineral soils ranges from 3 to 5. Several petrophysical models are 

available to convert measurements of dielectric permittivity to estimates 

of soil water content. One of the most commonly used models was 

developed by Topp et al. (1980). This empirical model was developed 

using a range of agricultural soils. Because the dielectric permittivity 

is the only input, the model can be easily applied to sites that have 

significant soil heterogeneity or limited soil characterization. Topp’s 

empirical model for estimating soil water content (θ) from dielectric 

permittivity (K) is expressed as:

θ=(5.3×10–2)+(2.29×10–2)K–(5.5×10–4)K2+(4.3×10–6)K3 [1]

Other empirical relationships have been developed for different soil 

textures. Soil-specific empirical relationships can also be developed 

using data from GPR or a time domain reflectometer (TDR). Another 

type of petrophysical relationship uses the volume fraction and measured 

permittivity of each soil component (soil solids, air, and water). However, 

these volume-averaging relationships typically require porosity as an 

input, which may vary widely across a site and is often unknown (Roth 

et al., 1990).

Dielectric permittivity can be estimated from measurements of the 

electromagnetic velocity in most earthen materials. Unless the material 

is very electrically conductive, the dielectric permittivity depends only 

upon the velocity of the radar signal. In materials that have moderate 
364 Chapter 6

to low electrical conductivity, the relationship between the radar signal 

velocity (v) and dielectric permittivity (K) is:

K=(c/v)2 [2]

In equation 2, c is the speed of light (Conyers, 2004). Several methods 

are available for measuring velocity. The most common method uses 

reflected energy from a subsurface interface. If the depth to a subsurface 

reflector is known, the velocity may be calculated using the time needed 

for the energy to travel from the transmitter to the reflector and then 

back to the receiver. This travel time can be determined by the arrival 

time of a reflection viewed on a radar record. If the depth to a reflector 

is not known, the velocity can be obtained by performing a variableoffset survey. This method requires separate transmitting and receiving 

antennas. In a variable-offset survey, the transmitting and receiving 

antennas are initially placed close together and then incrementally moved 

further apart with each measurement. The velocity can be measured by 

analyzing the travel time of the reflected signal as a function of distance 

as the antennas are moved apart. Although variable-offset surveys 

provide important information on velocity and reflector depth, they are 

time-consuming and thus cannot be used to monitor large areas.

GPR reflection techniques can also be used to provide noncontinuous measurements of velocity and thus soil water content. These 

measurements can be taken when a reflection hyperbola is created in 

the GPR record by isolated subsurface objects (e.g., stones and metal 

fragments) or by buried pipes that trend perpendicular to the GPR traverse. 

Reflection hyperbolas appear on GPR records as upside-down U shapes. 

Curve fitting procedures for reflection hyperbola can be employed to 

estimate the velocity. These procedures adjust a modeled shape to match 

the shape of the reflection hyperbola on a radar record. This fitting yields 

an estimate of the bulk soil radar velocity from the surface down to the 

isolated object or pipe. The depth to an isolated object or pipe does not 

need to be known in order to use this method. However, the visual fitting 

of the best curve to a reflection hyperbola is somewhat subjective and can 

lead to inaccuracies in velocity determination.

Another technique for estimating velocity uses the GPR groundwave. 

Groundwaves travel in the shallow subsurface (0 to ~30 cm) directly 

between the transmitting and receiving antennas. By noting the antenna 

separation and measuring the time needed for energy to travel between 

antennas, the velocity can be calculated. Groundwaves do not require 

a reflective interface and so can be applied in many soil environments. 

Because water content is commonly influenced by soil texture, 

groundwave measurements have also been used to map variations in 
Soil Survey Manual 365

soil texture at the field scale (Grote et al., 2003). Some researchers (van 

Overmeeren et al., 1997; Galagedara et al., 2005; Grote et al., 2010) have 

also found that the groundwave sampling depth is frequency dependent. 

Multi-frequency groundwave data could therefore be used to map the 

shallow, three-dimensional distribution of water content.

In addition to reflection hyperbola and GPR groundwave, a third 

GPR technique for estimating water content in soil uses air-launched 

GPR to obtain reflections from the soil surface. In this technique, the 

magnitude of the reflection from the ground surface is used to measure the 

dielectric permittivity. Air-launched data can be acquired and processed 

quickly. However, the technique has a sampling depth of less than 5 cm 

and the accuracy of the data is greatly diminished by vegetation, uneven 

soil surfaces, and vertical variations in water content. As a result, this 

technique has limited applications (Serbin and Or, 2003).

Electromagnetic Induction (EMI)

Electromagnetic induction methods use ground conductivity meters 

(GCM). These meters consist of a transmitter coil and either a single 

receiver coil or multiple receiver coils that are spaced at prescribed 

distances. Ground conductivity meters generate alternating electrical 

currents that are passed through the transmitter coil. These alternating 

electrical currents generate a time-varying, primary electromagnetic 

field. This primary field induces eddy currents to flow through the soil and 

thereby generate a secondary electromagnetic field. The amplitude and 

phase of the primary and secondary electromagnetic fields are measured 

by the receiver coil(s). Under conditions known as “operating at low 

induction numbers” (McNeill, 1980), the secondary field is proportional 

to the ground current and is used to calculate the “apparent” or “bulk” 

electrical conductivity (ECa ) of the soil, which is commonly expressed in 

units of millisiemens per meter (mS/m).

Apparent electrical conductivity is a depth-weighted average 

measurement for a column of earthen materials to a specific depth 

(Greenhouse and Slaine, 1983). Variations in ECa

 are produced 

by changes in the electrical conductivity of earthen materials. The 

electrical conductivity of soils is principally affected by the type and 

concentration of ions in solution, the amount and type of clays in the 

soil matrix, water content, and the temperature and phase of the soil 

water (McNeill, 1980). Apparent electrical conductivity increases with 

increases in concentration of soluble salts, content of water or clay, and 

temperature (McNeill, 1980). Although EMI has been principally used 

to map variations in ECa , GCMs have also been used to map variations 
366 Chapter 6

in magnetic susceptibility—a property useful in delineating hydric soils 

and differences in some lithologies (Allred et al., 2010).

Modern GCMs are well suited to soil studies. Each GCM is 

fairly lightweight and can be operated in pedestrian or mobile modes 

(fig. 6-6). Because EMI does not require direct contact with the ground, 

data collection is relatively easy, rapid, and inexpensive. EMI therefore 

allows a larger number of measurements than traditional soil survey tools 

and more comprehensive coverage of sites. Electromagnetic induction 

has been used in order 1, 2, and 3 soil surveys to indirectly measure 

the spatial and temporal variability of soil properties. Examples include 

salinity, texture, cation-exchange capacity, ionic composition, CaCO3 

content, moisture content, organic carbon content, plant-available 

nutrients, pH, bulk density, and structure (Doolittle and Brevik, 2014).

The effectiveness of EMI depends on the degree to which differences 

in ECa

 correspond to differences in the property under investigation. In 

general, stronger correlations are obtained where large differences in 

measured soil property and ECa

 occur and other soil properties that affect 

ECa

 remain relatively invariable. Differences can be horizontal, vertical, 

or both. Weaker correlations and lower predictive accuracies occur 

where the measured soil property and ECa

 display low variability in 

relation to other interacting and more variable soil properties that affect 

ECa

. ECa mapping is recognized as one of the most valuable methods in 

agriculture for measuring the spatial variability of soil properties at field 

and landscape scales (Corwin, 2008; Lück et al., 2009).

The depth of investigation (DOI) for ECa

 measurements made with 

GCM is generally taken as the depth of 70 percent cumulative response. 

The DOI is dependent on the conductivity of the soil and the frequency, 

dipole orientation, and intercoil spacing of the GCM. For the GCMs most 

commonly used in soil investigations, the DOI can range from about 30 

to 300 cm. DOIs from 3 to 60 m are possible with other commercially 

available GCMs.

Interpretations are commonly based on the identification of 

spatial patterns within EMI data sets. EMI was initially used to assess 

soil salinity, but its use has expanded to include mapping soil types; 

characterizing soil water content and flow patterns; assessing variations 

in soil texture, compaction, and organic matter content; and determining 

the depth to subsurface horizons, stratigraphic layers, or bedrock surfaces. 

Electromagnetic induction has also been used to assess differences in 

lithology and mineralogy, pH, field-scale leaching rates of solutes, 

herbicide partition coefficients, cation-exchange capacity, available 

nitrogen, and exchangeable Ca, Mg, and CaCO3

 (Doolittle and Brevik, 

2014).
Soil Survey Manual 367

Figure 6-6

Three of the commercially available ground conductivity meters used in soil 

investigations. Each has its own strengths and weaknesses. Pedestrian (left images) or 

mobile (right images) surveys can be conducted with each.

Advantages of EMI include its noninvasiveness, fast operating speed, 

and continuous recording of georeferenced data. The large amounts 

of georeferenced data that can be rapidly and inexpensively collected 

with EMI provide more complete characterization of the variability in 

soil properties at intermediate scales than traditional point-sampling 

methods. Electromagnetic induction does have limitations: results are 
368 Chapter 6

indirect, semi-quantitative, and site specific and vary depending on the 

complexity of the interactions that occur among multiple and varying soil 

properties. In addition, sferics (magnetic impulses from lightning) and 

nearby power sources and metal objects can interfere with and degrade 

the quality of EMI measurements. Limited ground-truth information and 

knowledge of the soils and the sources of ECa

 variation are required to 

properly interpret data.

Examples of EMI Use in Soil Survey

Figure 6-7 shows the spatial variability of ECa

 across a 7.7-ha range 

site that includes a portion of a dried-up playa bed in northern Texas. The 

very deep, poorly drained Randall soils (very-fine, smectitic, thermic 

Ustic Epiaquerts) formed in clayey lacustrine sediments on the playa 

floor. The very deep, well drained Olton soils (fine, mixed, superactive, 

thermic Aridic Paleustolls) formed in loamy, calcareous, eolian sediments 

on the slopes that surround the playa. At this site, variations in ECa

 are 

principally associated with differences in soil moisture and clay content. 

Areas of higher ECa

 (> 36 mS/m) were associated with the finer textured 

(> 50% clay), more imperfectly drained Randall soils.

On the ECa

 map in figure 6-7, soil variability and the transition from 

one soil type to another are well expressed. The soil map unit boundary 

line was imported from Web Soil Survey (Soil Survey Staff, 2015). This 

boundary has a fixed width and cannot accurately portray the spatial rate 

of change or the complex spatial variability of soils and soil properties 

along the transition between playa and upland. As evident on this map, 

spatial ECa

 data can improve the placement of the soil boundary line and 

the representation of soil variability.

Figure 6-8 is an ECa

 map of a 4.5-ha pasture in northeastern Iowa. 

Across this field, the surface slopes down to the north and northwest. The 

highest elevation is in the southeast corner of the field. The soil boundary 

lines were imported from the Web Soil Survey (Soil Survey Staff, 2015). 

In figure 6-8, the names of the dominant soil for each consociation are 

shown. These very deep soils all formed in loamy sediments overlying 

loamy till but belong to different soil drainage classes. Ostrander soils 

(fine-loamy, mixed, superactive, mesic Typic Hapludolls) are well 

drained; Kasson soils (fine-loamy, mixed, superactive, mesic Oxyaquic 

Hapludalfs) and Marquis soils (fine-loamy, mixed, superactive, mesic 

Oxyaquic Hapludolls) are moderately well drained; Floyd soils (fineloamy, mixed, superactive, mesic Aquic Pachic Hapludolls) are somewhat 

poorly drained; and Clyde soils (fine-loamy, mixed, superactive, mesic 

Typic Endoaquolls) are poorly and very poorly drained.
Soil Survey Manual 369

Figure 6-7

Spatial variations in ECa within the upper 150 cm of the soil profiles at a site in 

northern Texas. This information was used to improve the placement of boundary lines 

and the characterization of soils. The map unit names and the soil boundary line were 

imported from the Web Soil Survey.

Figure 6-8

Spatial variations in ECa within the upper 150 cm of five soils in northern Iowa. 

These variations are attributed principally to differences in soil drainage class and 

moisture content. Soil names and boundary lines were imported from the Web Soil 

Survey.
370 Chapter 6

The complex spatial patterns evident on the high-intensity ECa

map in figure 6-8 principally reflect differences in soil drainage class 

and moisture content. In the northern portion of the field, areas of high 

conductivity (> 24 mS/m) closely mimic the distribution of the wetter, 

more imperfectly drained Clyde and Floyd soils. Areas of lower ECa

(< 20 mS/m) correspond with the higher-lying, better drained Ostrander 

soils, which are on convex surfaces that dominate the southeastern 

portion of the field. Areas of higher conductivity that extend northwest 

to southeast are associated with draws situated between higher-lying 

ridgelines. Apparent conductivity maps, such as figure 6-8, help reveal 

the complexity of soil-landscape architectures and their impact on 

subsurface flow and soil moisture patterns at field scales.

Figure 6-9 shows the spatial variability of ECa

 within the upper 150 

cm of a soil that contains saline seeps. The 64.7-ha field is in northcentral Montana. The soil map unit boundary lines were imported from 

the Web Soil Survey (Soil Survey Staff, 2015). The soils are Megonot 

(fine, smectitic, frigid Torrertic Haplustepts), Pylon (fine, smectitic, 

frigid Torrertic Haplustalfs), and Tanna (fine, smectitic, frigid Aridic 

Argiustolls). These moderately deep, well drained soils formed in 

residuum weathered from semi-consolidated shale and siltstone. The 

presence of saline seeps is largely controlled by surface geology, abovenormal periods of precipitation, and farming practices that help water 

to move beyond the root zone. As excess water moves through the soil, 

it dissolves water-soluble minerals. When an impermeable layer is 

encountered, the downward flow of water is restricted and redirected 

laterally along the restricting layer into lower-lying slope positions. 

Saline seeps develop wherever the saline ground water comes within 

about 1.5 m of the surface (Daniels, 1987).

In figure 6-9, the saline seeps are identified by their high ECa

 (> 170 

mS/m). These seeps are arranged in a discontinuous, sinuous pattern. 

They meander across the field from the southwest to the northeast along 

the base of slopes. This plot also shows lines of moderate ECa

 that extend 

upslope away from the seeps. The areas of high ECa

 represent discharge 

areas for subsurface flow where dissolved salts concentrate when water 

is lost by evapotranspiration. Recharge areas for the subsurface flow are 

located upslope from the saline seeps (to the west and north) and have 

relatively low ECa

 (< 85 mS/m).

Electrical Resistivity (ER)

Soil electrical resistivity represents the capacity of soil materials to 

resist the flow of electrical current. Methods that calculate the apparent 
Soil Survey Manual 371

Figure 6-9

Spatial distribution of ECa across a cultivated field in north-central Montana. Spatial 

ECa patterns provide inferences about flow of subsurface water and soluble salts 

across this landscape and about the distribution of recharge, discharge, and flowthrough areas that contribute to the development of saline seeps. Soil names, surface 

textures, and boundary lines were imported from the Web Soil Survey.

electrical resistivity use Ohm’s law and the measured injected current, 

the measured potential difference, and a geometric factor. The geometric 

factor is a function of the electrode spacing or configuration (Samouёlian 

et al., 2005). Apparent resistivity is commonly expressed in units of 

ohm-meters (Ωm). The apparent resistivity is a complex function of the 

composition and arrangement of solid soil constituents, porosity, porewater saturation, pore-water conductivity, and temperature (Samouёlian 

et al., 2005). Electrical resistivity methods can be divided into those that 

inject currents into the ground through direct coupling and those that 

inject through capacitively induced coupling. Typically, both types of 

methods measure the apparent electrical resistivity, which is subsequently 

converted to its inverse, the apparent electrical conductivity of the soil.

Direct-Coupling ER

The traditional direct-coupling electrical resistivity method, also 

known as the galvanic source method, injects electrical current into the 

soil using an array of electrodes that are in contact with the ground. In a 

common four-electrode array, an electrical current is applied between two 

“current” electrodes and the voltage (the electric potential difference) is 

measured between two “potential” electrodes. For field surveys, current 
372 Chapter 6

and potential electrodes are maintained at a fixed distance from each other. 

The array is moved along a survey line to successive measurement points. 

Horizontal and vertical resolution, depth of investigation, and signal-tonoise ratio vary with the configuration of the electrode array (Samouёlian 

et al., 2005). The depth of investigation and volume of soil materials 

measured increase with increasing electrode spacing. Conversely, 

resolution decreases with increasing electrode spacing. Depending on 

the relative positioning of the potential and current electrodes, several 

different array configurations are possible. The three most common 

configurations are the Schlumberger, Wenner, and dipole-dipole (Allred 

et al., 2008b). The Wenner array is more sensitive to mapping lateral 

changes in electrical resistivity. The Schlumberger and dipole-dipole 

arrays are often preferred for vertical soundings that measure variations 

in apparent resistivity with depth (Allred et al., 2008b; Samouёlian et al., 

2005).

In many investigations, ER data are inverted. Inversion is an iterative 

process that results in a 2D or 3D model of the subsurface that best fits 

the acquired data. However, models constructed from inverted data 

provide nonunique solutions. Models are nonunique because, based on 

the constraints applied during the inversion process, several solutions or 

representations of the same data set are possible.

Apparent electrical resistivity has been used in order 1, 2, and 3 

soil surveys to indirectly measure and characterize variations in soil 

structure and physico-chemical properties, detect preferential flow paths, 

and monitor temporal changes in soil water distributions. As noted by 

Samouёlian et al. (2005), electrical resistivity allows the delineation 

of soil types and, when performed repeatedly over time, provides 

information on soil functioning.

Standard ER surveys, which require the repetitive insertion and 

removal of electrodes from the soil, are relatively labor-intensive and 

time-consuming. To reduce survey time, computer-controlled, multielectrode systems with tens to hundreds of electrodes have been developed 

(Allred et al., 2008b). These systems, however, have had limited use in 

soil studies.

Highly mobile, continuously recording, towed-array ER systems 

have been developed to expedite fieldwork and facilitate the collection 

of spatially dense data sets at field scales. In the United States, towed 

electrode-array ER systems have been used in precision agriculture 

and soil research (fig. 6-10). A commonly used system has six coulterelectrodes (two current and four potential electrodes) with nonadjustable 

spacing (Veris Technologies, 2016). It is configured in a modified Wenner 

array (Sudduth et al., 2005) and programed to simultaneously map ECa
Soil Survey Manual 373

over two soil depth intervals (i.e., 0 to 30 cm and 0 to 90 cm) (Lund et al., 

2000). Other systems use a single adjustable array to map ECa

 within the 

top 45 to 90 cm of the soil profile. Both systems are preprogrammed and 

do not need calibration. In addition, unlike EMI sensors, measurements 

are not affected by sferics (electromagnetic pulses caused by atmospheric 

phenomena) or by nearby metallic objects, utility wires, or engines. 

However, towed-electrode arrays are invasive so their field use is 

commonly restricted by plant growth and cover and soil wetness. As soil 

contact must be maintained at all times during mapping, these systems 

should be operated neither on frozen or rocky soils nor in some bedded 

or furrowed cultivated fields.

Figure 6-10

A towed electrode-array (six coulter-electrodes) soil ECa mapping system behind a 

utility vehicle in a field of corn stubble.

Example of Direct-Coupling ER Use in Soil Survey

Figure 6-11 shows the results of a high-intensity survey conducted 

across a 32.4-ha field in western Illinois. Soil names, map unit symbols, 

and boundary lines from a high-intensity soil survey are shown on the 

plot of the deep (0 to 90 cm) data (image on right). Only the boundary 

lines are shown on the plot of the shallow (0 to 30 cm) data (image on 

left). The soils are very deep Mollisols that formed in thick loess deposits 
374 Chapter 6

and belong to the fine-silty and fine particle-size classes. Although 

they belong to different particle-size classes, the soils do not vary 

appreciably in clay content. They range from poorly drained Aquolls to 

somewhat poorly drained and moderately well drained Udolls. Major 

soils identified within the study site are Ipava, Buckhart, Edinburg, and 

Sable soils. The somewhat poorly drained Ipava soils (fine, smectitic, 

mesic Aquic Argiudolls) and the moderately well drained Buckhart soils 

(fine-silty, mixed, superactive, mesic Oxyaquic Argiudolls) are in upland 

areas. The poorly drained Sable (fine-silty, mixed, superactive, mesic 

Typic Endoaquolls) and Edinburg soils (fine, smectitic, mesic Vertic 

Argiaquolls) are along intermittent drainageways and in broad summit 

areas, respectively.

Figure 6-11

Maps of apparent conductivity prepared from shallow and deep data collected in westcentral Illinois.

In figure 6-11, the ECa is noticeably lower in the shallow (0 to 30 

cm) map than in the deep (0 to 90 cm) map. This is due to the increase in 

clay and water contents in deeper horizons. For the deep measurements, 

areas with lower ECa

 represent better drained, higher-lying areas of 

Ipava and Buckhart soils. Higher ECa

 values were measured in the more 
Soil Survey Manual 375

sloping and eroded areas of Ipava soils (43B2) where the argillic horizon 

is shallower and seepage was observed. Lower-lying areas of Sable soils 

are wetter and have a higher ECa

. In the southern portion of the field, 

on the deep map, faint patterns of three parallel, essentially east-westtrending terraces can be identified by their higher ECa

.

Capacitively Induced Coupling

Capacitively induced coupling resistivity (CCR) systems use 

capacitive coupling rather than galvanic contact to introduce electric 

current into the ground. They measure voltage at the surface in order 

to determine apparent soil electrical resistivity. The capacitive coupling 

uses coaxial cables to form a large capacitor. The metal shield of the 

coaxial cable is one of the capacitor plates and the soil surface is the 

other. The outer insulation of the coaxial cable acts as the dielectric 

material separating the two plates. The system transmitter applies an 

alternating current (AC) to the coaxial cable side of the capacitor, which 

in turn generates AC in the soil on the other side of the capacitor. With 

regard to the receiver, a similar phenomenon occurs, except in reverse. 

The AC in the soil charges the receiver coaxial cable capacitor, and the 

measured capacitance is then used to determine the potential difference 

(voltage) generated by the flow of electric current within the soil.

One of the more common CCR systems has two coaxial cables 

attached to the transmitter, one on each side, to form a current dipole, 

and it has two coaxial cables attached to the receiver, one on each side, 

to form a potential dipole (Geometrics, 2001). The depth of investigation 

for the system is 0.1 to 20 m, depending on dipole cable and tow-link 

length. This set-up, along with some initial data processing, allows this 

CCR system (fig. 6-12) to mimic a conventional galvanic contact dipoledipole electrode array. A conventional array consists of one pair of current 

electrodes (current dipole) and one pair of potential electrodes (potential 

dipole). By increasing the distance between the receiver and transmitter 

dipoles, the depth of investigation and volume of soil measured are 

increased (Walker and Houser, 2002). Inverse modeling methods can be 

employed to produce depth profiles of electrical conductivity (fig. 6-13) 

if CCR data are collected along a transect line using several different 

spacing distances between transmitter and receiver dipoles.

Capacitively induced coupling resistivity systems are rarely used 

in soil studies. In the field, the lines are easily snared on obstacles and 

broken off (Gebbers et al., 2009). CCR systems work exceedingly well 

in high resistivity soils, where it is often difficult to transfer sufficient 

current into the ground with towed-electrode array systems. In highly 

conductive soils, however, these systems provide little signal penetration 

and the resulting data are noisy (Gebbers et al., 2009).
376 Chapter 6

Figure 6-12

A common capacitively induced coupling resistivity system. (Photo courtesy of 

Geometrics, Inc.)

Figure 6-13

A soil electrical conductivity depth profile from an agricultural test plot at the Ohio 

State University in Columbus, Ohio. The data for this profile were collected using 

spacing distances of 0.625 m, 1.25 m, 2.5 m, and 5 m between receiver and transmitter 

dipoles. To generate the soil electrical conductivity profile shown, data were input to 

a two-dimensional, least-squares optimization, inverse computer modeling program 

developed by Loke (2014).

Less Common Proximal Sensing Methods

The proximal sensing methods that are less commonly used by the 

National Cooperative Soil Survey include magnetometry, magnetic 

susceptibility, portable X-ray fluorescence, time domain reflectometry, 
Soil Survey Manual 377

optical reflectance, gamma-ray spectroscopy, mechanical interactions, 

ion-selective potentiometry, and seismic.

Magnetometry (MT)

Magnetometry is a passive remote sensing method that records 

the magnitude of the Earth’s local magnetic field. Its sensors, called 

magnetometers, may be placed on the ground surface, in the air, in satellites, 

or in boreholes beneath the surface of the Earth. For measurements 

in agricultural fields, magnetometers are typically positioned within a 

couple of meters of the ground surface. Gradiometers, which are better 

adapted to emphasize magnetic field anomalies from shallow sources, 

are set up with two magnetometers mounted a short distance (< 1 m) 

apart. This arrangement allows the magnetic field gradient between them 

to be measured (fig. 6-14). Gradiometers have the added advantage of 

eliminating the need to make corrections for diurnal fluctuations in the 

magnetic field. Magnetic surveys using gradiometers have successfully 

found disturbances (e.g., backfilled trenches and excavated areas) in 

Figure 6-14

Magnetic surveying with a cesium vapor gradiometer (Geometrics, 2016) integrated 

with a global positioning system receiver (Trimble, 2016).
378 Chapter 6

iron-rich soils (Rogers et al., 2005). This suggests the potential use of 

this technology to identify the extent and location of some anthropogenic 

soils, particularly in order 1 soil survey applications.

Magnetic Susceptibility (MS)

Magnetic susceptibility is a measure of the degree to which a material 

can be magnetized when subjected to an applied magnetic field. The 

magnetic susceptibility of soil depends on the concentration, size, and 

shape of strongly magnetic minerals as well as the method of measurement 

(Mullins, 1977). Strongly magnetic minerals include ferromagnetic 

minerals, such as magnetite, maghemite, titanomagnetite, and pyrrhotite. 

Sources of MS can be lithogenic, pedogenic, or anthropogenic (Grimley et 

al., 2004). In soils, MS is influenced by differences in parent material, soil 

age, texture, mixing, firing, weathering, additions to the soil (commonly 

anthropogenic), pH, organic matter content, and soil moisture content 

(Maier et al., 2006; Grimley et al., 2004; Mullins, 1977).

Handheld susceptibility meters allow MS measurement across soil 

surfaces, down small-diameter holes, and on exposed sections (e.g., 

Bartington, 2016). These single-coil sensors require direct contact with 

the soil, and their depth of investigation is related to the diameter of 

the coil. The effective penetration depth of most handheld susceptibility 

sensors is limited to about 1 to 10 cm. Borehole sensors, however, 

can document vertical contrasts in susceptibility to depths as great as 

20 m (Dalan, 2006). Unlike GPR, EMI, and ER, magnetic susceptibility 

surveys are not significantly affected by variations in soil moisture 

content. Because the volumes that are measured by MS sensors are 

small, high spatial resolution can be achieved. However, the accuracy of 

handheld, single-coil MS sensors is diminished by thermal drift and in 

areas that have rough, rocky surfaces or thick vegetation.

Magnetic susceptibility can also be measured with ground 

conductivity meters (GCM). The inphase component of the secondary 

electromagnetic field in a GCM is considered proportional to, and has 

been used to map, variations in magnetic susceptibility. However, the 

inphase response of an EMI sensor is more restricted by depth than 

the quadrature phase response (apparent conductivity). The inphase 

response of a commonly used meter measures only the top 50 cm of soil 

(Dalan, 2006). Interpretations of magnetic susceptibility from EMI data 

are also challenging. Variations are caused by differences in instrument 

configuration, instrument height and orientation, surface topography and 

roughness, depth to target, and changes in the sign (±) of the response in 

relation to target depth (Shamatava et al., 2007; Tabbagh, 1986). Other 
Soil Survey Manual 379

drawbacks of EMI sensors include instrument drift and the use of an 

arbitrary zero level.

Results of MS surveys are displayed as individual profiles or contour 

plots. Typically, field measurements of MS are reported in dimensionless 

volume units, e.g., 10-5 (SI) (Mullins, 1977).

Where sufficient contrast in magnetic properties exists, MS has been 

associated with pedogenesis (Fine et al., 1989), gleying (Vadyunina 

and Babanin, 1972), slope position (De Jong et al., 2000), soil drainage 

class and texture (Grimley et al., 2004), human disturbances (Dalan and 

Banerjee, 1996), and industrial pollutants (Fialová et al., 2006; Magiera 

et al., 2006). Where the concentration of magnetic minerals is sufficiently 

high, MS has been used to delineate boundaries of hydric soils (Lobred 

and Simms, 2009; Zwanka et al., 2007; Grimley et al., 2004; Arruda and 

Grimley, 2002; Grimley and Vepraskas, 2000) and to differentiate soil 

types (Hanesch and Scholger, 2005; Dearing et al., 1996; Vadyunina and 

Smirnov, 1978). Magnetic susceptibility is most applicable to some order 

1 soil surveys.

Portable X-Ray Fluorescence (P-XRF)

Portable X-ray fluorescence spectrometers use high-energy incident 

X-ray photons to forcibly eject electrons from the inner shell of atoms. 

The resulting electron holes cause instability, which causes electrons from 

the outer shell to drop into the inner shell and fill the voids. This process 

results in the emission of X-ray energy, which is referred to as X-ray 

fluorescence. Because the energy emitted as fluorescence is element 

specific, different elements can be identified and quantified (Weindorf 

et al., 2012a). A comprehensive discussion of P-XRF is provided by 

Kalnicky and Singhvi (2001). Soil samples and exposed surfaces can be 

readily scanned with P-XRF spectrometers (fig. 6-15).

X-ray fluorescence has been principally used to assess metals in 

contaminated soils (Dao et al., 2012; Schwarz et al., 2012; Weindorf et 

al., 2012b; Kalnicky and Singhvi, 2001). Weindorf et al. (2012a) used 

P-XRF to improve descriptions of soil morphology and differentiate soil 

horizons based on the concentration of different metals. In gypsiferous 

soils of west Texas, Weindorf et al. (2009) used P-XRF to quantify the 

calcium content and determine the percent of gypsum. Beaudette et al. 

(2009) conducted P-XRF surveys in two watersheds, one formed over 

metavolcanic rocks and the other over granite. They used the resulting 

geochemical data to infer differences in soil development weathering 

indices, mineralogy, and geologic signatures. Doolittle et al. (2013) 

used EMI and P-XRF data to characterize differences in the mineralogy 
380 Chapter 6

and lithologies of serpentinite- and non-serpentinite-derived soils in the 

Northern Piedmont of Pennsylvania. In soil survey, P-XRF is primarily 

applicable to point data documentation.

Figure 6-15

A portable XRF spectrometer, which can be attached to a monitoring bench in an office 

to scan collected samples (left) or can be used in the field to scan exposed faces of soil 

pits or surfaces (right).

Time Domain Reflectometry (TDR)

Time domain reflectometry (TDR) measures soil water content 

and, with some sensors, electrical conductivity. The use of TDR in soil 

science was pioneered by Topp, Davis, and Annan (1980). TDR infers 

water content and electrical conductivity from the measured dielectric 

permittivity and signal attenuation, respectively (Jones et al., 2002).

With TDR, a waveguide, or probe, of known length is inserted 

into the soil and the travel time for a generated electromagnetic pulse 

to traverse this length is measured. Using empirical (Topp et al., 1980; 

equation 1) or dielectric mixing models, the travel time is converted into 

a velocity of pulse propagation. The velocity of propagation is used to 

determine the soil’s bulk dielectric permittivity, which is used to infer the 

volumetric water content. The dielectric permittivity is directly related to 

soil volumetric water content.

According to Jones et al. (2002), some of the advantages of TDR are: 

(1) accurate estimations of soil volumetric water content (to within ± 2% 

without soil-specific calibration), (2) minimal calibration requirements 
Soil Survey Manual 381

in most soils, (3) absence of radiation hazards that are associated with 

neutron probe or gamma-attenuation techniques, (4) excellent spatial 

and temporal resolution, and (5) ease of measurements. Some of the 

disadvantages of TDR are: (1) measurement errors can occur if there are 

gaps between the soil and probe, (2) TDR is limited in highly saline and 

frozen soils (Ferrara and Flore, 2003), (3) special calibrations are required 

in soils that have a high content of clay or organic matter content, and (4) 

probes are difficult to insert in some soils.

A variety of TDR sensors are available for determining water content 

in soil. Depending on the length of the waveguide, TDR sensors can 

provide bulk soil moisture measurements over different soil depths. In 

soil survey, time domain reflectometry is primarily applicable to pointdata documentation.

Optical Reflectance (UV, vis-NIR, mid-IR)

Optical sensors are used to determine the soil’s ability to reflect light in 

different parts of the electromagnetic spectrum. Proximal optical sensors 

are fundamentally the same as remote sensing systems. The advantage 

of proximal sensors is that they can be applied at the surface and below 

ground (fig. 6-16). In soil survey, optical reflectance is applicable to point 

data documentation. It can be used for on-the-go measurements during 

different soil survey practices. In addition, both near and mid infrared 

diffuse reflectance spectroscopy are being used in the laboratory for 

rapid determination of some soil properties. Optical sensing systems 

cover the ultraviolet (100–400 nm), visible (400–750 nm), near infrared 

(750–2,500 nm), or mid infrared (2,500–25,000 nm) wavelengths or a 

combination of these wavelengths. Typically, instruments used for soil 

measurements include their own light source (e.g., a light bulb or lightemitting diode). Photodiodes or array detectors are used to estimate the 

intensity of reflected light and relate this measure to the light reflected 

from a given set of standards. Both source and reflected light can be 

transmitted through the air, via fiber optics, or when feasible, through 

a contact window fabricated from highly resistive material, such as 

sapphire or quartz.

Measurements obtained using optical sensors can be related to a 

number of soil attributes, such as soil mineral composition, clay content, 

soil color, moisture, organic carbon content, pH, and cation-exchange 

capacity (Christy, 2008; Viscarra Rossel et al., 2009; Mouazen et al., 

2010). Measurements can be direct or indirect. For direct measurements, 

relationships are based on a physical phenomenon that affects light 

reflectance in a specific part of the spectrum (e.g., soil mineralogy or 
382 Chapter 6

Figure 6-16

A probe equipped with insertion load sensors and two spectrometers, which cover 

visible and near infrared parts of the spectrum as well as electrical conductivity.

water content is predicted using water absorption bands). For indirect 

measurements, relationships are deterministic for a finite domain 

and the combined effects of several soil attributes can be related to a 

given soil characteristic (e.g., soil organic matter). Sensor calibration 

strategies range from a simple linear regression to multivariate methods, 

chemometrics, and data mining (Viscarra Rossel et al., 2006). Although 

some of these models may be applied to large geographic areas, most are 

currently associated with a specific range of soils.

Ultraviolet (UV) radiation has been used in combination with visible 

or infrared spectra (e.g., Islam et al., 2003). Ultraviolet and visible spectra 

have been used to characterize inorganic minerals, such as iron oxides 

(Schwertmann and Taylor, 1989). An extensive range of reports is available 

on the use of visible near infrared (vis-NIR) and mid infrared (mid-IR) 

spectra for soil analysis. Both laboratory conditions and proximal soil 

sensing have been investigated. The mid-IR contains more information on 

soil mineral and organic composition than the vis-NIR, and its multivariate 
Soil Survey Manual 383

calibrations are generally more robust. The mid-IR has these advantages 

because fundamental molecular vibrations of soil components occur in 

the mid-IR while only their overtones and combinations are detected in 

the vis-NIR. Thus, soil vis-NIR spectra display fewer and much broader 

absorption features compared to mid-IR spectra.

Gamma-Ray Spectroscopy

Gamma rays contain a very large amount of energy and are the 

most penetrating radiation from natural or artificial sources. Gammaray spectrometers measure the distribution of the intensity of gamma (γ) 

radiation versus the energy of each photon. Sensors may be either active or 

passive. Active γ-ray sensors use a radioactive source (e.g., cesium-137) 

to emit photons of energy that can then be detected using a γ-ray 

spectrometer (e.g., Wang et al., 1975). Passive γ-ray sensors measure the 

energy of photons emitted from naturally occurring radioactive isotopes 

of the element from which they originate (e.g., Viscarra Rossel et al., 

2007). Soil elemental isotopes can be mapped by a γ-ray sensor on a 

vehicle (fig. 6-17). Data interpretation may include analysis of measures 

related to the isotopes of potassium, thorium, and uranium or the total 

count. Such mapping can be a useful tool for predicting soil properties 

in different soil landscapes. A significant amount of preprocessing, 

however, is commonly required to reveal relationships between the γ-ray 

spectra and the soil data (Viscarra Rossel et al., 2007). In soil survey, 

gamma-ray spectroscopy is primarily applicable to order 1 surveys (and 

possibly some order 2 or 3) as well as to point-based measurements.

Figure 6-17

A vehicle-mounted passive gamma-ray sensor.
384 Chapter 6

Inelastic neutron scattering (INS) spectroscopy (Schrader and 

Stinner, 1961) relies on the detection of γ-rays that are emitted following 

the capture and reemission of fast neutrons as a sample is bombarded 

with neutrons from a pulsed neutron generator. The emitted γ-rays are 

characteristic of the excited nuclide, and the intensity of γ-rays is directly 

related to the elemental content of the sample. The detectors used are 

the same as those used in γ-ray spectroscopy. Wielopolski et al. (2008) 

proposed the use of INS spectroscopy for the measurement of carbon and 

other elements in soils.

Mechanical Interactions

Simple mechanical sensors can be used to estimate soil mechanical 

impedance (resistance). By nature, these soil strength sensors measure 

resistance to soil failure (Hemmat and Adamchuk, 2008). As a mechanical 

resistance sensor moves through the soil, it registers resistance forces 

arising from the cutting, breakage, and displacement of soil, as well 

as from the parasitic (frictional and adhesive) forces that develop at 

the interface between the sensor’s surface and the surrounding soil. 

Normally, soil mechanical resistance is expressed in units of pressure 

and represents the ratio of the force required to penetrate the soil media 

and the frontal (normal to the direction of penetration) area of the tool 

engaged with the soil.

The first step toward soil mechanical resistance sensing is to map 

the total horizontal (draft) force and, in some cases, the total vertical 

force applied to a traditional fixed-depth implement engaged with the 

soil. Recorded measurements represent surrogate values affected by a 

variety of factors, including the type and shape of the tool working the 

soil, the speed and depth of the operation, and the surface conditions. 

In addition to vertically operated cone penetrometers, horizontal sensors 

have been designed to generate high-resolution maps of horizontal soil 

penetration resistance obtained at a specific depth. Multiple tips can 

be simultaneously deployed at different depths. Such an arrangement 

allows researchers to determine the spatial variability of soil mechanical 

resistance at any available depth as well as vertical variability in each 

location of the field.

To avoid the expense of adding direct load-sensing tips, a single-tip 

horizontal sensor can be actuated vertically in a way similar to a bulk 

soil strength sensor. In addition to using a tip-based method, the vertical 

distribution of soil mechanical resistance can be measured using an 

instrumented tine. This distribution is measured by sensing the direct load 

applied to the tine at discrete depths and/or by measuring the degree of 
Soil Survey Manual 385

bending using strain gauge technology (i.e., a cantilever beam approach). 

Maps of soil mechanical resistance corresponding to a 20–30 cm depth 

layer can reveal the appearance of old infrastructure, such as roads. Soil 

mechanical impedance changes with soil water content and bulk density. 

On-line soil moisture sensors (typically capacitance or near-infrared 

reflectance probes) have been used to separate these two soil attributes.

Acoustic and pneumatic sensors can be alternatives to mechanical 

sensors for the study of the interaction between soil and an agricultural 

implement. Acoustic sensors have been used to determine soil texture, 

bulk density, or both by measuring the change in the level of noise caused 

by the interaction of a tool with soil particles. Pneumatic sensors have 

been used for on-the-go sensing of air permeability in soil. The pressure 

required to force a given volume of air into the soil at a fixed depth was 

compared to several soil properties, such as soil structure and compaction. 

In soil survey, mechanical interactions are primarily applicable to order 1 

surveys and point data documentation.

Ion-Selective Potentiometry

Ion-selective potentiometry sensor systems resemble a traditional 

wet-chemistry method to assess the content of certain chemical ions and 

compounds. They can provide the most important type of information 

needed for precision agriculture—soil nutrient availability and pH. The 

measurements are conducted using either an ion-selective electrode (ISE) 

or an ion-selective field effect transistor (ISFET). These sensors detect 

the activity of specific ions at the interface between sensitive membranes 

and the aquatic part of either a soil solution or a naturally moist sample. A 

common ISE system consists of a membrane that is sensitive to specific 

ions and a reference electrode. The difference in the potential between 

the sensitive membrane and the reference is measured and converted 

to the activity of specific ions in the tested solutions. The design of a 

combination ion-selective electrode allows both sensitive and reference 

parts to be assembled in one probe. Different electrode brands represent 

different designs of ion-selective membranes and reference junctions.

An ISFET integrates the ion-selectivity of an ISE with the small size 

and the robust nature of a field effect transistor. The current between 

two semiconductor electrodes (source and drain) is controlled by a gate 

electrode represented by an ion-selective membrane. As ions of interest 

affect the gate, their charge impacts the source-drain current, which 

provides an indication of ion activity. The main differences between an 

ISFET and an ISE are that an ISFET does not contain an internal solution 

and the ion selective membrane is affixed directly on the gate surface of 
386 Chapter 6

the ISFET. ISFET technology is attractive because of its compact size and 

theoretically high signal-to-noise ratio, especially when used for the flow 

injection analysis (FIA) method. However, the range of commercially 

available ISFETs remains relatively narrow. The sensitive membrane in 

both ISE and ISFET is made of glass (H+

, Na+), polyvinyl chloride (K+, 

NO3

-

, Ca2+, Mg2+), or metal (H+).

A range of approaches can be used to establish the interface between 

an ISE or ISFET and a soil solution. Some methods involve great detail; 

some are relatively simple. On one end of the range of possibilities is a 

complete sample preparation with a prescribed controlled ratio between 

soil particles and extracting solution. This method adds complexity to 

the measurement apparatus and often requires a longer sampling time 

and analysis cycle (Viscarra Rossel et al., 2005). On the other end is a 

direct, simple measurement (DSM) approach, which is relatively easy to 

implement (Adamchuk et al., 2005). The real-time chemical extraction 

of the ions mimics conventional soil analysis procedures. DSMbased measurements reveal specific ion content in a given soil state, 

which may not represent nutrient availability throughout the growing 

season. Because chemical processes in soil are frequently influenced 

by the physical composition of the soil, combining direct ion activity 

measurements with geophysical instruments (described earlier) can help 

predict conventional laboratory test values used to prescribe various soil 

amendments (fig. 6-18). In soil survey, ion-selective potentiometry is 

primarily applicable to order 1 surveys and point data documentation.

Figure 6-18

A sampling mechanism for a towed system that simultaneously maps soil pH and 

apparent electrical conductivity.
Soil Survey Manual 387

Seismic

Seismic waves are essentially elastic vibrations that propagate 

through soil and rock materials. Artificial energy sources can be used 

to introduce seismic waves into the ground for investigatations of 

subsurface conditions or features. Examples of energy types include 

explosive, impacting, vibratory, and acoustic. For seismic geophysical 

methods in which artificial energy is supplied, the seismic waves are 

timed as they travel through the subsurface from the energy source to the 

sensors, which are called geophones. Incoming seismic wave amplitudes, 

and hence energy, are also measured at the geophones. The energy source 

is ordinarily positioned on the surface or at a shallow depth, and the 

geophones are typically inserted at the ground surface. Data on the timed 

arrivals and amplitudes of the seismic waves measured by the geophones 

provide insight into belowground conditions or help to characterize and 

locate subsurface features.

Traditional seismic methods have rarely been used for agricultural 

purposes. However, laboratory studies employing 2 to 7 kHz acousticsourced seismic waves have shown that seismic wave velocities correlate 

significantly with soil compaction, soil porosity, and soil water content 

and that acoustic-sourced seismic wave absorption coefficients exhibit 

significant correlation with soil bulk density and soil water content 

(Oelze et al., 2002; Lu et al., 2004). In the Appalachian Highlands 

Physiographic Province of northwestern Virginia, Olson and Doolittle 

(1985) used seismic refraction to determine the elevation of the water 

table and depth to bedrock. They noted, however, that this geophysical 

method could not distinguish soil profile characteristics. Seismic tools 

are potentially applicable in order 1, 2, or 3 soil surveys.

References

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Adamchuk, V.I., E. Lund, B. Sethuramasamyraja, M.T. Morgan, A. 

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