ISSN 1678-0892
Agosto, 2023

Empresa Brasileira de Pesquisa Agropecuária
Embrapa Solos
Ministério da Agricultura Pecuária e Abastecimento

BOLETIM DE PESQUISA E DESENVOLVIMENTO
285

Identifying Soil Horizons Transitions for Ground-Truthing and Interpreting Ground Penetrating Radar (GPR) Imagery of Planosols

Carlos Wagner Rodrigues do Nascimento
Marcos Bacis Ceddia
Gustavo Mattos Vasques
Hugo Machado Rodrigues
Saulo Siqueira Martins
Ronaldo Pereira de Oliveira
Sílvio Roberto Lucena Tavares

Embrapa Solos
Rio de Janeiro, RJ
2023

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Identifying soils horizons transitions for Ground-Truthing and interpreting Ground Penetrating Radar (GPR) imagery of planosols / Carlos Wagner Rodrigues do Nascimento ... [et al.]. – Rio de Janeiro : Embrapa Solos, 2023.

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1. Física do solo. 2. Horizontes. 3. Geofísica. 4. Sensor proximal. 5. Radar de Penetração. 6. GPR. 7. Planossolo. I. Nascimento, Carlos Wagner Rodrigues do. II. Ceddia, Marcos Bacis. III. Vasques, Gustavo Mattos. IV. Rodrigues, Hugo Machado. V. Martins, Saulo Siqueira. VI. Oliveira, Ronaldo Pereira de. VII. Tavares, Sílvio Roberto Lucena. VIII. Embrapa Solos. IX. Série.

CDD 631.4

Enyomara Lourenço Silva (CRB – 4/1569)

© Embrapa, 2023

Authors

Carlos Wagner Rodrigues do Nascimento

Geologist, master in Agronomy at Federal Rural University of Rio de Janeiro, Seropédica, RJ

Marcos Bacis Ceddia

Agronomist, PhD in Agronomy, professor at Federal Rural University of Rio de Janeiro, Seropédica, RJ

Gustavo Mattos Vasques

Forestry engineer, PhD in Soils and Plant Nutrition, researcher at Embrapa Soils, Rio de Janeiro, RJ

Hugo Machado Rodrigues

Geographer,master in Agronomy at Federal Rural University of Rio de Janeiro, Seropédica, RJ

Saulo Siqueira Martins

Physicist, PhD in Geophysics, professor at the Federal University of Pará, Belém, PA

Ronaldo Pereira de Oliveira

Electrical engineer, PhD in Precision Agriculture, researcher at Embrapa Soils, Rio de Janeiro, RJ

Sílvio Roberto Lucena Tavares

Agronomist-engineer, PhD in Environmental Geotechnics, researcher at Embrapa Soils, Rio de Janeiro, RJ

Identifying Soil Horizons Transitions for Ground-Truthing and Interpreting Ground Penetrating Radar (GPR) Imagery of Planosols

ABSTRACT – The Ground Penetrating Radar (GPR) has potential to characterize soils both vertically and horizontally. The effectiveness of using GPR relies on the interpretation of soil targets in the radargrams. For identifying the targets in their correct depths, a velocity model of the terrain is required. However, a velocity model cannot be directly obtained from GPR models using a monostatic antenna. An alternative is the generation of a depth model by measuring the velocity of the electromagnetic pulse from hyperbolas created in the radargram from the interaction of the pulse with point scatterers. On the other hand, the radargram does not always present these point scatterers for the estimation of the pulse velocity and generation of an accurate depth model. Thus, this work aimed to assess the feasibility of using iron rods as markers of the depths of soil horizons transitions in GPR images (750 MHz antenna) of Planosols, and as point scatterers to generate hyperbolas for deriving depth models for converting the Y-axis of the radargram from time to depth units. The experimental area is located in Seropédica municipality, southeastern Brazil. Three soil profiles were described and classified as Planosols, which were located at the footslope position. Iron rods (80-cm long by 8-mm in diameter) were inserted horizontally in the transitions of the soil horizons. Images (radargrams) were obtained using a GPR with a monostatic shielded antenna of 750 MHz frequency. The radargrams were pre-processed using static correction and dewow to remove noise. The pulse velocity was estimated by fitting the hyperbolas generated by the buried iron rods. As results, the 750 MHz antenna allowed visualizing the iron rods features (hyperbolas), especially in sandier horizons in the radargrams. Pulse velocity estimation was possible, which allowed the time-to-depth conversion of the Y-axis, and thus, positioning vertically the horizons transitions in the radargrams. In Planosols, the vertical and horizontal distribution of the E-to-B horizon transitions, as imaged by the GPR, is important information for irrigation planning, land zoning and soil survey.

Index Terms: Iron rods; Poximal soil sensing; Exploration geophysics; Dielectric constant; Soil horizons.

INTRODUCTION

Traditional soil survey involves fieldwork that is commonly expensive and time consuming. In order to reduce costs and work time, research on the development and application of proximal sensors has been increasing to qualitatively and quantitatively describe soil types and their attributes. Among the available proximal sensors, the Ground Penetrating Radar (GPR) has been considered to potentially identify and map soil features both horizontally and vertically. For instance, GPR has been used in soil studies to: separate soil horizons or layers, helping to determine different soil classes (Zhang et al., 2014); determine the acidity of organic matter in organic soils (Doolittle; Butnor, 2009); and map soils (Ucha et al., 2002) and soil attributes (De Benedetto et al., 2010). It is also used in other areas of knowledge, including geology (Davis; Annan, 1989), and archaeology (Quarto et al., 2007).

The GPR uses time-domain reflectometry to image shallow terrain (soils and other media) using antennas with frequencies ranging from 10 MHz to 1 GHz (Annan, 2009). The data collected by the GPR is stored in an image called radargram. The sharpness of the radargram depends on the physical properties of the soil constituents, among which the relative permittivity, or dielectric constant (k) – the extent to which a material concentrates electric flux – is the most important. The values ​​of this property vary among the constituents of the soil, and depend on the moisture (Table 1), with higher k indicating higher ability to concentrate electricity. As such, one limitation of the GPR for soil studies is the difficulty to visualize and separate soil features of interest in the radargram (for example, transitions among soil horizons), because these features usually appear very similar in the radargram as they are formed by the interaction and mixing-up of soil constituents with various k values.

Table 1. Ground Penetrating Radar-related physical properties of some soil and rock constituent materials. Adapted from Annan (2001).

Material

Dieletric constant (k)

Electrical conductivity
(mS m⁻¹)

Pulse velocity
(m ns⁻¹)

Air

1

0

0.3

Distilled water

80

0.01

0.033

Fresh water

80

0.5

0.033

Dry sand

3–5

0.01

0.15

Saturated sand

20–30

0.1–1

0.06

Clay

5–40

2–1000

0.06

Granite

4–6

0.01–1

0.13

According to Doolittle and Collins (1995), the main soil factors that control the sharpness of the radargram are the porosity, the water content, the presence of salts, and the clay content. These factors control the electrical conductivity of the soil, influencing the electric pulse behavior in the soil (Doolittle; Collins, 1995; Loewer et al., 2015). In more conductive soils, the radargrams have lower resolution and higher noise compared with radargrams of more resistive soils. In detail, the soil constituents that present conductive properties, such as clays, soil water (Cezar et al., 2012), and ions and salts in high concentrations, cause signal attenuation, and thus, a less sharp radargram (Hagrey; Mueller, 2000).

One of the requisites for using the GPR and interpreting the radargram is the determination of the depth of the targets of interest in the soil. To obtain the depth, it is necessary to know the GPR pulse velocity in the terrain, which is used to derive a depth model to convert the Y-axis of the radargram from time to depth units. A proper method for deriving the depth model is the Common Midpoint (CMP), which requires a GPR having a bistatic antenna, that is, an antenna in which the transmitter coil is physically separated from the receiver coil. Using the CMP, it is possible to create a depth model to allow the conversion of the Y-axis of the radargram from time to distance (depth) units (Annan, 2003), so that the physical phenomena under study or targets of interest are visualized in the correct depth position in the radargram.

However, when GPR models use a monostatic antenna (with the transmitter and receiver coils together in one device), the CMP cannot be executed. An alternative is the generation of a depth model by measuring the velocity of the electromagnetic pulse from hyperbolas created in the radargram from the interaction of the pulse with point scatterers (Jacob; Urban, 2015). However, the radargram does not always present clear point scatterers for the estimation of the pulse velocity and generation of an accurate depth model.

To overcome this issue, it is possible to insert artificial point scatterers in the soil at known depths to generate clear hyperbolas in the radargram. Ideally, these point scatterers have a k value that contrasts with those from the soil, however, not all materials are hard enough to be inserted in the soil without breaking. This work carried out a GPR survey using a 750 MHz antenna in an area of Planosols and used iron rods inserted horizontally at the soil horizons transitions, aiming to assess the feasibility of using iron rods as markers of the depths of soil horizons transitions, and as point scatterers to generate hyperbolas and accurately derive depth models for converting the Y-axis of the radargram from time to depth units.

MATERIAL AND METHODS

The work was carried out in an area under pasture in an experimental farm (Fazendinha Agroecológica do km 47), which is located in the Seropédica municipality (Rio de Janeiro State), southeastern Brazil (Figure 1). A typical topossequence of the study site includes Lixisols, Planosols, Cambisols and Gleysols, in the downslope direction (Nascimento, 2019). Three trenches (2-m long by 1.5-m wide by 1.20 to 1.72-m deep) were opened at different positions at the footslope (Figure 1), where soil profiles were described according to Santos et al. (2015), and classified according to the Brazilian Soil Classification System (Santos et al., 2018). The soils were classified as T1 (Trench 1): Planossolo Háplico Distrófico arênico (Dystric Albic Planosol (Arenic), T2 (Trench 2): Planossolo Háplico Distrófico espessarênico (Dystric Hyperalbic Planosol (Arenic)), and T3 (Trench 3): Planossolo Háplico Distrófico arênico gleissólico (Dystric Albic Gleyic Planosol (Arenic)). Disturbed soil samples were collected at each horizon and analyzed in the laboratory to measure physical, and chemical attributes, including particle size fractions, exchangeable bases (Ca, Mg, K, and Na), and exchangeable acidity (H+Al), according to Teixeira et al. (2017). The sum of exchangeable bases (SB = Na + K + Ca + Mg) and the cation exchange capacity (CEC = SB + H+Al) were calculated from the measured bases.

After the delimitation of the horizons in the soil profiles, iron rods with dimensions of 80 cm in length and 8 mm in diameter were inserted in the transitions among all soil horizons. Then, the three soil profiles were imaged using the GPR MALÅ GroundExplorer (Guideline Geo AB, Sundbyberg, Sweden), by taking one measurement at every 5 cm using an odometer along transects close to the soil trenches, respectively. Separate images (radargrams) were acquired close to each soil profile using a monostatic shielded antenna of 750 MHz. The spatial positions of the soil profiles were recorded in the field, as well as the distances of the iron rods from the top and the side of the trenches in each profile.

Figure 1. Study site in Brazil, digital terrain model and the locations of the three soil trenches.

The radargrams obtained in the field were processed using the ReflexW software (Sandmeier, 2009), where two procedures were done in sequence: static correction and dewow. The use of other filters available in the software degraded the quality of the radargrams, making it more difficult to identify the hyperbolas generated by the iron rods (point scatterers) and to interpret the radargrams.

After pre-processing the radargrams, the hyperbolas from the iron rods were identified in the radargrams, and the velocity-estimation hyperbolas were fit manually to them in the software. Then, the GPR electromagnetic pulse velocities in the soil were estimated at each hyperbola, followed by the conversion of the Y-axis of the radargram from time (ns) to depth units (m) (Sandmeier, 2009). This procedure allowed identifying the depths of the soil features observed in the field with those observed in the radargram. The accuracy of the estimated pulse velocity, and consequently, the conversion of the Y-axis to depth units, was checked by comparing the depths of the iron rods (horizons transitions) in the field with the estimated depths of the corresponding hyperbolas in the radargram after depth conversion.

RESULTS AND DISCUSSION

The first three horizons of the T1 profile have sandy texture up to 69 cm (Table 2), where there is an abrupt textural change from the E horizon to the Bt1 horizon. From the E to the Bt1 horizon, the clay content increases about four times (from 92 g kg⁻¹ to 388 g kg⁻¹). The exchangeable H+Al also increase considerably in Bt1.

Table 2. Physical and chemical attributes of the soils.

Profile

Horizon

Depth
(cm)

Sand

Clay

H+Al

SB

CEC

(g kg⁻¹)

(cmolc dm⁻³)

T1

A

0-8

809

99

5.6

1.47

7.07

AE

8-22

876

43

3.0

1.90

4.90

E

22-69

831

92

1.5

1.16

2.66

Bt1

69-92

564

388

4.7

2.50

7.20

Bt2

92-133

441

443

5.6

2.10

7.70

Bt3

133-155⁺

513

367

4.7

3.20

7.90

T2

A

0-9

913

31

2.8

2.37

5.17

E1

9-24

910

18

1.5

1.57

3.07

E2

24-38

922

17

1.1

2.09

3.19

E3

38-116

930

17

0.6

1.44

2.04

E4

116-145

875

40

0.7

1.36

2.06

E5

145-158

717

20

4.7

2.02

6.72

Bt

158-172⁺

621

267

6.2

2.83

9.03

T3

A

0-13

762

50

0.2

4.28

12.68

AE

13-32

884

49

0.4

2.42

5.76

E

32-44

878

45

0.5

2.15

4.25

Btg1

44-66

703

216

1.7

2.56

8.36

Btg2

66-110

376

398

5.4

10.13

24.53

Cg

110-120⁺

508

365

6.2

13.81

28.01

H+Al = Exchangeable acidity; SB = Sum of exchangeable bases; CEC = Cation exchange capacity.

The T1 radargram obtained by the 750 MHz antenna allowed a clear visualization of the hyperbolas representing the iron rods placed in the first three horizon transitions, including that from the E to the Bt1 horizon (Figure 2). Observing the iron rods hyperbolas in the radargram was expected for the sandy horizons, which are more resistive (lower k), and thus contrast with the iron rods that are more conductive (higher k) in the radargram. Similar results were found by Juliano et al. (2013) studying pipe detection in horizons with predominance of the sand fraction.

In the E-to-Bt1 horizon transition, at 69 cm, an approximately horizontal feature appears in the radargram, which can represent the base of the E horizon (Figure 2, in yellow). Mapping the base of the E horizon in Planosols, that is, the depth to the argillic horizon, is important for estimating the plant-exploitable soil volume, the soil water and nutrient storage capacity, the depth of the light-textured horizons for soil tillage purposes, etc. This feature could also be used to estimate the spatial boundaries among soil types in the field, possibly expediting soil survey. For instance, the place where the feature disappears from the radargram possibly indicates where the Planosol to Cambisol, or Planosol to Lixisol transitions occur in the field (Nascimento, 2019).

In T1, the hyperbolas from the iron rods inserted in the subdivisions of the B horizon (Bt1, Bt2, and Bt3) were not identified. This may have been caused by both the higher clay contents (Table 2) and, maybe, the higher water contents in the B horizons. These higher values may cause less differentiation in the k value between the iron rod and the surrounding soil, which may lead to the suppression of the iron rods hyperbolas in the radargram (Table 1) (De Benedetto et al., 2010; Metwaly, 2007). Soil depth may also play a role, because the GPR pulse gets weaker downwards as a consequence of the pulse interacting with the soil constituents. The higher the k value of the soil constituents, the more the GPR electromagnetic pulse is scattered, and thus weakened, in the soil, hampering imaging deeper soil horizons.

The estimated GPR pulse velocity at profile T1 was 0.1030 m ns⁻¹. After time-to-depth conversion of the radargram Y-axis, the depths of the horizons transitions that were visible in the radargram matched closely their corresponding depths recorded in the field (Figure 2). This confirms that the iron rods were appropriate to generate hyperbolas for pulse velocity estimation. In other words, the visible iron rods hyperbolas were properly fit and the pulse velocity was properly estimated that allowed accurate vertical positioning of the soil features in the GPR image.  

Figure 2. On the left, picture of the T1 soil profile (Planossolo Háplico Distrófico arênico), with the black numbers indicating the positions of the iron rods, which mark the soil horizons transitions. On the right, radargram of the T1 soil profile showing the GPR pulse velocity estimated from the fitted hyperbolas (in black). The red lines indicate the approximate side limits of the soil trench and the yellow line the E-to-Bt1 horizon transition.

The T2 soil profile was described and sampled down to 1.72 m (Table 2, Figure 3). This soil presents an expressive 13 times increase of clay content, from 20 to 267 g kg⁻¹, from the E5 to the Bt horizon at 1.58 m. There was also an increase in H+Al at E5 relative to the upper E horizons.

In the T2 radargram, only the first three horizons transitions could be identified, but the E5-to-Bt transition where an abrupt textural transition occurs (at 1.58 ​m) could not. This suggests that, at this location, the maximum depth achieved by the 750 MHz antenna pulse to contrast the iron rod form the soil matrix lies between 0.38 (last visible hyperbola) and 1.16 m (next one, not visible).

From the first hyperbola, the estimated pulse velocity in the soil was 0.1100 m ns⁻¹, at the second hyperbola it was 0.0980 m ns⁻¹ and at the third 0.1070 m ns⁻¹ (Figure 3). The difference in pulse velocity along the soil depth is not unusual and may result from the great interaction of the 750 MHz pulse with the soil constituents that change with depth (Forte et al., 2014), even though this soil is mostly constituted by sand particles. The second pulse velocity, at the middle point vertically, was chosen to create the depth model. In this case, the chosen velocity led to positioning errors at the depth transitions where the pulse velocities were different from 0.0980 m ns⁻¹.

Figure 3. On the left, picture of the T2 soil profile (Planossolo Háplico Distrófico espessarênico), with the black numbers indicating the positions of the iron rods, which mark the soil horizons transitions. On the right, radargram of the T1 soil profile showing the GPR pulse velocity estimated from the fitted hyperbolas (in black). The red lines indicate the approximate side limits of the soil trench.

Overall, the T3 radargram had lower clarity compared to the other radargrams. It only captured the iron rod of the E-to-Btg1 horizon transition at 0.44 m. This is the transition where supposedly the greatest difference in k values among adjacent horizons is seen in the profile. Thus, it makes sense that this transition is visible when the other ones are not. On the other hand, the horizontal feature marking the depth to the argillic horizon was not visible (Figure 4).

The T3 soil profile has an abrupt textural change from the E to the Bt1 horizon at 0.44 m, with a five times increase in the clay content, from 45 to 216 g kg⁻¹ (Table 2). Considering the chemical attributes, the sum of bases (SB) and the cation exchange capacity (CEC) both increase dramatically from the Btg1 to the Btg2 horizon. Along with the larger depth, the higher exchangeable cation content at Btg2 increases the electrical conductivity of the soil, and thus, its ability to scatter and weaken the GPR signal, compromising the clarity of the radargram. Moreover, both the Btg1 and Btg2 horizons present gleyic redoximorphic features that are related to higher water contents that also disperses the GPR pulse.

The low clarity of the radargram may also be due to the relatively high values ​​of SB and CEC along the T3 profile, since the exchangeable cations increase the conductivity promoting signal attenuation as the electromagnetic pulse reaches deeper in the soil (Aguiar, 2005; Moreira; Dourado, 2007). As such, the difference between the k value of the soil matrix and that of the iron rod diminishes, possibly suppressing the appearance of most hyperbolas in the radargram.

Figure 4. On the left, picture of the T3 soil profile (Planossolo Háplico Distrófico arênico gleissólico), with the black numbers indicating the positions of the iron rods, which mark the soil horizons transitions. On the right, radargram of the T1 soil profile showing the GPR pulse velocity estimated from the fitted hyperbolas (in black). The red lines indicate the approximate side limits of the soil trench.

The time-to-depth conversion model for T3 used a pulse velocity of 0.0990 m ns⁻¹, estimated from the single hyperbola at 0.44 m. Contrary to the previous cases, since there were no other visible hyperbolas in the T3 radargram, one can only assume that the pulse velocity estimated from the single hyperbola approximates the average pulse velocity in the profile, allowing to accurately convert the Y-axis to depth units. In other words, even though the accurate positioning of the single visible hyperbola can be verified from the true position of the corresponding iron rod in the field, the same is not possible for the other inserted iron rods, as their hyperbolas are not visible.

Similar to these results, metallic landmines buried in sandy, loamy or clayey soils have produced clearer hyperbolas in radargrams in dryer soils and poorer ones in saturated soils. In drier soils, the clearer radargrams are a consequence of the greater contrast in dielectric properties between the landmine and the soil, as well as the lower attenuation due to lower soil moisture (Miller et al., 2004). Considering that clay particles have charges on their surfaces and adsorb more water than sand particles, the water promotes more attenuation of the signal in clay-rich soils (Doolittle; Collins, 1995; Loewer et al., 2015).

CONCLUSIONS

The use of iron rods to mark horizons transitions of interest in soils (Planosols in this case) as well as to serve as point scatterers is recommended for the conversion of the Y-axis of the radargram from time to depth units, after which the radargram features can be directly compared to, and interpreted from, the real soil features observed in the field. Overall, the pulse velocity can be accurately estimated from the iron rods hyperbolas, allowing accurate vertical positioning of the soil features.

Iron rod hyperbolas are clearer in the radargrams in sandier horizons that are more resistive, and also closer to the surface where the GPR pulse is stronger. Soil horizons with higher clay contents are more conductive, dispersing the GPR pulse. The GPR pulse is also dispersed and weakened downwards along the soil, and thus, deeper soil horizon transitions are not visible.

ACKNOWLEDGMENTS

To the Laboratory of Water and Soil in Agroecosystems (LASA) at the Department of Agrotechnologies and Sustainability of the Federal Rural University of Rio de Janeiro (UFRRJ) for their technical and logistic support. To Embrapa Agrobiology, UFRRJ, and the Agricultural Research Corporation of the State of Rio de Janeiro (Pesagro) for providing the study area for this work. And to the following people that helped in different stages of the study: Suelen Marques de Oliveira Durão, Juliana Ferreira Nunes, Raquel Gomes Feijó, Fernanda Barbosa dos Santos, Martin de Oliveira Freire (in memoriam), Wadson de Menezes Santos, João Felipe da Silva Gonçalves, Hugo Felipe Vianna Figueira, Jorge Luiz Heraclito de Mattos, Fabiana Loureiro dos Reis, Júlio César Francisco Ferreira de Araujo Junior and Tércio da Silva Pacheco. The first author thanks the Graduate Program in Agronomy-Soil Science (CPGA-CS) at UFRRJ.

Funding was provided by the Brazilian Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES) finance code 001, and by the Brazilian Agricultural Research Corporation (Empresa Brasileira de Pesquisa Agropecuária – Embrapa) grant numbers 05.13.15.003.00.00 and 03.12.10.002.00.00.

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