Dielectric Permittivity and Moisture Content

Mohamed, Abdel‐Mohsen O.; Paleologos, Evan K.

doi:10.1016/b978-0-12-804830-6.00016-8

Cited by 11 publications

(6 citation statements)

References 167 publications

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“…The dielectric methods have been extensively studied and constitute an accepted measurement technique as it does not disorder the ground and is suitable for automation and remote sensing. Furthermore, the improvement of θ estimation has been attempted, taking into account the effect of (σ b ) on apparent dielectric permittivity [7,[13][14][15][16].…”

Section: Background Work and Motivationsmentioning

confidence: 99%

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity

Fragkos,

Loukatos,

Kargas

et al. 2024

Sensors

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In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (ECw) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (εa) readings were lower than that of Topp’s permittivity–water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θm-εa0.5 decreases with the increase in bulk EC (σb) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εa-σb, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) and the soil type.

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Section: Background Work and Motivationsmentioning

confidence: 99%

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity

Fragkos,

Loukatos,

Kargas

et al. 2024

Sensors

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“…When the thickness of the ice layer is 15 cm, the amplitude decreases by 53%. However, various parameters also affect the amplitude obtained from the detector in addition to the layer thickness, such as conditions (temperature, moisture, and salt content), marker (size, shape, conductivity, and magnetic susceptibility), and device (size of the coil, and measuring frequency) [40], [41]. Furthermore, Fig.…”

Section: A Performance Testing Of the Metal Detector Systemmentioning

confidence: 99%

Non-Visual Sensing of Metallic Pavement Markers From a Moving Vehicle

Niskanen

Kolli

Immonen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Snow and ice covering paved surfaces cause problems for roads and other vehicles operating in cold regions. There are notable risks for road accidents and reduced performance of vehicle movements, e.g., in industrial facilities such as ports, when road markings and marked pathways are not visually observable. In this study, metallic paved surface markers identified by metal sensors are tested. The sound signals captured by a metal detector attached to the vehicle are used to detect the markings.Results indicate that the tested method for reading markings through snow is effective at lower speeds. The main advantages of the tested technology are effectiveness in snowy and icy surface conditions, low upfront cost detection technology in some environments, robust system without moving parts, high sampling rate, and low operating costs. The procedure is not restricted to specific environmental conditions, such as snow and ice; it also has the potential to detect markings through layers of mud, tree leaves or sand. The detectors have application prospects in intelligent transportation system applications at airports, logistics terminals and industrial facilities.

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“…, where j is imaginary (i.e., j 2 = −1), and and are the real and imaginary parts of the dielectric permittivity, respectively [12,34]. The dielectric permittivity is a frequency-dependent vector called the complex dielectric permittivity due to both and being dependent on the frequency.…”

Section: Gpr Backgroundmentioning

confidence: 99%

“…Soil pH was measured GPR is a valuable tool for SWC studies because the water content of a soil has a significant effect on the relative soil dielectric permittivity. The dielectric permittivity is a complex function with real and imaginary components, and it is usually defined as ε * = ε + jε , where j is imaginary (i.e., j 2 = −1), and ε and ε are the real and imaginary parts of the dielectric permittivity, respectively [12,34]. The dielectric permittivity is a frequency-dependent vector called the complex dielectric permittivity due to both ε and ε being dependent on the frequency.…”

Section: Experimental Materialsmentioning

confidence: 99%

“…Because of the real permittivities of dry soils, which range from 2 to 7, soil dielectric permittivity measurements will be highly dependent on the moisture content. According to [8,34,40], authors investigated the sensitivity of the soil dielectric permittivity to soil type and SWC, and the results showed that the real and imaginary parts of the soil dielectric permittivity (ε and ε ) increased with increasing SWC in sandy soils.…”

Section: Effects Of Soil Properties On the Soil Dielectric Permittivitymentioning

confidence: 99%

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Soil Water Content Estimation Using High-Frequency Ground Penetrating Radar

Zhou

Wang

et al. 2019

Water

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The rapid high-precision and nondestructive determination of shallow soil water content (SWC) is of vital importance to precision agriculture and water resource management. However, the low-frequency ground penetrating radar (GPR) technology currently in use is insufficient for precisely determining the shallow SWC. Therefore, it is essential to develop and use a high-precision detection technology to determine SWC. In this paper, a laboratory study was conducted to evaluate the use of a high-frequency GPR antenna to determine the SWC of loamy sand, clay, and silty loam. We collected soil samples (0–20 cm) of six soil types of loamy sand, clay, and silty loam and used a high-frequency (2-GHz) GPR antenna to determine the SWC. In addition, we obtained GPR data and images as well as characteristic parameters of the electromagnetic spectrum and analyzed the quantitative relationship with SWC. The GPR reflection two-way travel times and the known depths of reflectors were used to calculate the average soil dielectric permittivities above the reflectors and establish a spatial relationship between the soil dielectric permittivity ( ε ) and SWC ( θ ), which was used to estimate the depth-averaged SWC. The results show that the SWC, which affects the attenuation of wave energy and the wave velocity of the GPR signal, is a dominant factor affecting the soil dielectric permittivity. In addition, the conductivity, magnetic soil, soil texture, soil organic matter, and soil temperature have substantial effects on the soil dielectric permittivity, which consequentially affects the prediction of SWC. The correlation coefficients R2 of the “ θ ~ ε ” cubic curve models, which were used to fit the relationships between the soil dielectric permittivity ( ε ) and SWC ( θ ), were greater than 0.89, and the root-mean-square errors were less than 2.9%, which demonstrate that high-frequency GPR technology can be applied to determine shallow SWC under variable hydrological conditions.

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Dielectric Permittivity and Moisture Content

Cited by 11 publications

References 167 publications

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity

Non-Visual Sensing of Metallic Pavement Markers From a Moving Vehicle

Soil Water Content Estimation Using High-Frequency Ground Penetrating Radar

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