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8 Soil strength, bulk density, and related properties

8 Soil strength, bulk density, and related properties

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R.A. Viscarra Rossel et al.



measurement error. The development of proximal sensors to measure

bulk density is important because it makes more sense to provide measurements of the soil profile in a volumetric rather than gravimetric basis,

for example, for reporting organic carbon, water content, and lime

requirements. Bulk density and compaction can be inferred using active

γ-ray attenuation measurements (Oliveira et al., 1998) and mechanically

by measuring draught, depth, and soil water content (Mouazen and

Ramon, 2006).



4. Summary

Table 2 provides a summary of this review, showing the approximate

frequency, energy, and wavelengths at which these sensors operate and

whether the measurement is direct or indirect. For most soil properties,

multiple sensing options can be used. For example, soil pH can be measured directly using ISFETs or indirectly using visÀNIR spectroscopy.

There is widespread interest in the use of diffuse reflectance spectroscopy

for PSS because several soil properties can be measured from a spectrum.

Largely, however, the techniques are indirect (Table 2) and to be useful

quantitatively, spectra must be related to a set of known reference samples

through calibration. Successful generalization of indirect proximal

soil sensor calibrations will depend on the type of soil: its mineralogy,

particle-size distribution, presence of segregations (e.g., iron oxides and

oxyhydroxides), soluble salts, water content, and the abundance and

composition of organic matter. Inference using indirect techniques may

be strong or weak (Table 2), but their measurements are invariably less

accurate than direct methods. However, indirect methods are generally

less expensive, technologically and methodologically better developed and

more readily available to users.

The different proximal soil sensors described in this article are in

various stages of development, with some relying on expensive instruments designed for the laboratory, and others on purpose-built, lowercost, portable sensors designed for field application. Table 3 indicates the

developmental status of various proximal soil sensors and their approximate costs.



5. General Discussion and Future Aspects

PSS is not entirely new, although its development and that of new

technologies is ongoing. The earliest reported use of a proximal soil



Table 2



Proximal soil sensors used to measure soil attributes



EM Range



γ-rays



X-rays



Frequency (Hz)



1022



10218



1016 1015 1013



Wavelength (m)



10212



10210



1028

XRD UV



INS TNM Active γ



Passive γ



XRF



Total carbon



D



i



D



Organic carbon



I



Technique



UVÀvisibleÀinfrared



Micro and radio waves



1012



1010



108



107



106



102



1026



1025



1024



1022



101



102



103



106



Vis



NIR



mid-IR



LIBS Micro WSN TDR



FDR Capac



GPR



EMI



ER



ECh Mech



I



D



D



D



I



D



D



I



D



D



I



I



D



D



i



I



i



i



D



Biochemical



Inorganic carbon I

Total nitrogen



D



D



D



NitrateÀnitrogen

Total phosphorus D



D



I



Extractable



I



I



D



phosphorus

Total potassium



D



Extractable



D



D



I



I



I



I



I



I



D



D



i



I



D



D



i



I



I



D



D



D



D



i



I



I



I



I



I



D

D



potassium

Other major



D



D



D



D



nutrients

Micronutrients,



D



elements

Total iron



D



Iron oxides

Heavy metals

CEC



i



D



i

D



D

D



I



D

i



i



(Continued)



Table 2



(Continued )



EM Range



γ-rays



X-rays



Frequency (Hz)



1022



10218



1016 1015 1013



Wavelength (m)



10212



10210



1028



XRF



XRD UV



Technique



INS TNM Active γ



Soil pH



Passive γ



UVÀvisibleÀinfrared



Micro and radio waves



1012



1010



108



107



106



102



1026



1025



1024



1022



101



102



103



106



Vis



NIR



mid-IR



FDR Capac



GPR



EMI



I



Buffering



I



I



I



I



LIBS Micro WSN TDR



ER



D



ECh Mech



D

I



capacity

and LR

Salinity and



D



D



D



sodicity

Physical

Color

Water content



D

D



D



D



I



i



D



D



D



D



D



D



D



D



I



Matric potential

Clay



I



Silt



I



Sand



I



Clay minerals



I



i



I



D



i



I



I



I



I



I



I



i



i



I



D



I



I



D



D



i



i



I



I



I



Soil strength

Bulk density



D/I



D

I



I



D



I



I



I



Porosity

Rooting depth



I

D



D



We denote the measurement as either physically based and direct (D) or correlative and indirect (I). Lower case “i” indicates weak inference.

Note: INS, inelastic neutron scattering; TNM, thermalized neutron methods; XRF, X-ray fluorescence; XRD, X-ray diffractometry; UV, ultraviolet; vis, visible; NIR, near infrared;

mid-IR, mid infrared; LIBS, laser-induced breakdown spectroscopy; Micro, microwaves; WSN, wireless sensor networks; TDR, time-domain reflectometry; FDR, frequency-domain

reflectometry; Capac, capacitance; GPR, ground-penetrating radar; EMI, electromagnetic induction; ER, electrical resistivity; ECh, electrochemical; Mech, mechanical.



Table 3



Current development status of proximal soil sensors and approximate costs in US dollars (USD)



EM range wavelength (m)

212



γ-rays (10



)



X-rays (10210 m)

UVÀvisÀR (1028 to 1024)



Microwave (1022)

Radiowave (101 to 106)



Technique



Development status

a



Approximate costs (USD)



INS

TNM

Active γ

Passive γ



Research

Commercial/research

Commercial/research

Commercial



XRF



Commercial/research



XRD

UV

Vis

NIR



Commercial/research

Commercial

Commercial

Commercial/research



MIR

LIBS



Commercial/research

Commercial/research



Microwave

TDR

FDR and

capacitance

GPR

NMR

EMI



Research

Commercial

Commercial



800,000À1,500,00

10,000À15,000

10,000

10,000À70,000 (depends on crystal size and

sensitivity)

8000À40,000 (OEM to commercial handheld

units)

75,000 (portable combined XRF/XRD)

3000 (combined UVÀvisÀNIR 250À900 nm)

1000À5000 (combined visÀNIR 400À1000 nm)

10,000À100,000 (visÀNIR depends on range and

portability)

10,000À75,000 (field and laboratory instruments)

15,000À40,000 (dependent on the number

of channels)

À

500À1500 (sensor with display)

100À500 (sensor only)



Commercial

Research

Commercial



80,000

À

10,000À40,000 (depends on number of coils)

(Continued)



Table 3



(Continued )



EM range wavelength (m)



Technique



Development status



Approximate costs (USD)



Electrical resistivity



ER



Commercial



Gypsum



Commercial



Electrochemical



Electrochemical



Commercial/research



Mechanical



Tillage

Penetrometers



Research

Commercial



Acoustic

Pneumatic



Research

Research



200À3000 (for handheld/portable sensor); 7000

(for on-the-go sensor)

5À100 (single sensor—dependent on type and

quality)

50À1000 (with data logger—depends on sensor

capabilities)

100À1000 (single sensor—depends on ion,

quality, reference electrode)

200À10,000 (with logger/interface, e.g.,

interfacing multiple sensors to a computer)

À

1500À5000 (hand-operated device with digital

data storage)

À

À



Note: INS, inelastic neutron scattering; TNM, thermalized neutron methods; XRF, X-ray fluorescence; XRD, X-ray diffractometry; UV, ultraviolet; vis, visible;

NIR, near infrared; MIR, mid infrared; LIBS, laser-induced breakdown spectroscopy; TDR, time-domain reflectometry; FDR, frequency-domain reflectometry;

GPR, ground-penetrating radar; NMR, nuclear magnetic resonance; EMI, electromagnetic induction; ER, electrical resistivity; ISE, ion-selective electrode; ISFET,

ion-sensitive field effect transistor.

a

Commercial INS systems are likely to appear in 2À3 years. Their cost will be determined largely by the cost of the neutron generator and the number of

detectors used.



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sensor was in the 1920s when an instrumented drawbar dynamometer

was used to discern spatial variation in soil compaction (reported in

McBratney and Minasny, 2010). PSS gained prominence in soil science in

around the past 30 years because of the realization that sensed data could

provide good quality soil information more efficiently than laboratory

methods of soil analysis, which can be expensive and time consuming.

Some of the earlier reports using sensors to measure soil properties were

given by Bowers and Bowen (1975), who measured electrical resistance

to detect drying fronts; Rhoades and Corwin (1981), who used EMI to

detect soil salinity; Perumpral (1987), who used standardized penetrometers for measuring soil compaction; and Dean et al. (1987), who used

capacitance for measuring soil water.

In the 1990s, the development and use of sensors for soil measurement

gained momentum and various technologies were being reported, for

example, GPR (Whalley et al., 1992), microwave sensing (Whalley, 1991),

visible and NIR reflectance (Ben-Dor and Banin, 1995; Shonk et al., 1991;

Sudduth and Hummel, 1993; Viscarra Rossel and McBratney, 1998), ISEs

(Adsett and Zoerb, 1991), ISFETs (Birrell and Hummel, 1997; Viscarra

Rossel and McBratney, 1997), mobile penetrometers (Alihamsyah and

Humphries, 1991), acoustic sensors (Sabatier et al., 1990), and odor sensors

to determine soil air composition (Persaud and Talou, 1996). Recognizing

the increasing interest in soil sensing, Viscarra Rossel and McBratney

(1998) used “proximal soil sensing” to describe measurement of soil properties with ground-based sensors. The development of PSS coincided with

that of precision agriculture, which for some time appeared to be the application most suited to the use of proximal soil sensors.

Interest in PSS is now more widespread (Viscarra Rossel et al.,

2010a), and currently a wide range of technologies can be used for it.

By its own merit, PSS is becoming a new discipline and is a topic of

considerable interest in the soil, agricultural and environmental sciences,

and engineering communities. The efficiency with which PSS can

obtain soil data makes it naturally suited to many situations that require

large amounts of quantitative data at fine spatial and/or temporal resolutions, for example, digital soil mapping, soil monitoring, precision

agriculture, the assessment of contaminated sites, and measurement of

subsurface hydrology.

Although the fundamental scientific principles of the sensors that were

reported early on remain the same, for the most part we understand them

better and therefore are better placed to use them. For some sensors,

technology has improved considerably, for example, with visÀNIR

array-based detectors that increase instrument portability and ruggedness.

Research has also refined our understanding of how we can best apply

these sensors to measure soils and their properties. Our ability to extract

useful information from the sensed data and to analyze large spatial



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datasets (Cressie and Kang, 2010) has improved because of advances in

mathematical and statistical methods. Improvements in electronics and

mobile computing, fueled by the consumer and automotive sectors, have

made it feasible (and often relatively inexpensive) to interface with sensors

in a user-friendly manner.

The recently established International Union of Soil Sciences (IUSS)

Working Group on Proximal Soil Sensing (www.proximalsoilsensing.org)

aims to provide the framework for greater interaction between scientists

and engineers with a common interest in developing proximal sensing technologies and mathematical and statistical techniques to better understand

soil processes and spatiotemporal soil variability. Two large ongoing multinational European projects—the iSoil (Werban et al., (2010)) and Digisoil

(Grandjean et al., 2010) projects that aim to develop PSS for digital soil

mapping—present a step in the right direction and are the largest current

investment in PSS research. The future of PSS lies in such interactions and

multidisciplinary collaborations. Below, we short-list general considerations

for future work.

Development of soil sampling (measuring) designs for PSS—considering both geographic and property spaces.

Research to define the most suitable technique or combination of techniques for measuring key soil properties, for example, bulk density,

plant-available water, soil carbon, and carbon fractions.

Research the often-complex interactions between the soil matrix and

sensor signals.

Research the underlying mechanisms that allow prediction of soil

properties from indirect proximal soil sensors to develop theoretical

calibrations that use soil knowledge. This will lead to improved accuracy,

robustness, and applicability.

Research the use of local versus global sensor calibrations. This might

be soil property specific.

Develop better signal processing and signal reconstruction methods.

Often the methods used to process data from a proximal soil sensor are

chosen ad hoc based on the experience of the particular investigator.

Better, more widely applicable methods that could lead to standardization would help advance collaborative research and PSS.

Develop data fusion methods that combine data from multiple sensors

to produce useful soil information.

Research the application of proximal soil sensors for diverse applications, for example, the use of multisensor platforms for digital soil

mapping, soil monitoring, assessment of soil carbon, contaminated site

assessment, and soilÀplant relationships.

PSS provides soil scientists with an effective approach that can be

used to learn more about soils. Proximal soil sensors allow rapid and



Proximal Soil Sensing for Measurements in Space and Time



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inexpensive collection of precise, quantitative data at fine (spatial and

temporal) resolutions, which can be used in more meaningful analyses

to better understand soils and the spatiotemporal variability of their

properties. Soil science needs PSS to device sustainable solutions to the

global issues that we face today: food, water, and energy security and

climate change. Our intent here is to raise awareness about PSS to further its research and development and to encourage the use of proximal

soil sensors in different applications.



ACKNOWLEDGMENTS

Dr. Viscarra Rossel would like to thank the CSIRO Division of Land and Water

Capability Development Fund—“Which soil sensors do we use where?” for supporting

this work.



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