Table 3. Summary of all equations of dielectric permittivity models.
Figure 1. Experimental setups and calibration results for TDR: (
a) simultaneous measurement of electrical conductivity and SWC (the time distance between two reflections (
a) and (
b) are calculated using successive reflections. The reflectograms show voltage changes in soil dielectric permittivity, water content, and electric conductivity. The time (∆
t) required for the pulse to cover the double length of metal rods increases with soil dielectric permittivity, resulting in a decrease in pulse amplitude); (
b) a TDR probe, equipped with electronics, is used to monitor soil temperature and electrical conductivity; (
c–
e) TDR calibration curves for three different soil temperatures, where S stands for soil specific surface area and θeq for equilibrium moisture, which accounts for temperature variations
[65][64].
Based on
Figure 1a, a fast-sampling oscilloscope records the first pulse from the generator to the sensor in real time, analyzing the electromagnetic wave travel path and calculating the time distance between reflections. Three reflectograms are created for each scenario, representing voltage as a function of time at the selected feeder point. To assess soil electrical conductivity, the study used two 10 cm long TDR probes that were equipped with an analog-to-digital converter, a digital output temperature sensor, a microcontroller, and a serial interface (
Figure 1b). The electrical conductivity of the soil sample was determined using a low-frequency conductivity formula and a voltage drop on a reference resistor. The microcontroller produced a square wave at 100 kHz without polarizing the electrode–soil system, distinguishing between higher frequency TDR signals and lower frequencies
[65][64]. The study also found that the bulk dielectric permittivity (
εb) decreases when the water content is below θeq (water content value) and increases when the water concentrations are above θeq (
Figure 1c–e). The temperature-induced exchange of water particles explains the temperature effect. All soils, except soil 562, show values of bulk dielectric permittivity at 5 °C compared to 55 °C with high water content, with medium-value soils showing the largest difference
[65,66][64][65].
Many variables, such as temperature, salinity, density, and clay content in the case of SWC dielectric sensors, can affect how accurately SWC is measured
[12,16][10][15]. These variables impact the soil’s dielectric permittivity spectrum, as do dielectric dispersion, bound water relaxation, and interphase events
[44,67,68][44][66][67]. Consequently, low-frequency device manufacturers frequently offer several calibrations suitable for different types of soil, typically distinguishing between mineral and organic soils or focusing on soil texture
[69,70,71][68][69][70]. However, the user can also perform customized calibrations based on the experimental procedures or layout. Park et al.
[58][57] revealed that SWC and the bound water and moisture content, influenced by soil particle composition, affect the dielectric permittivity of the soil. Although TDR and GPR use temperature and texture data to determine refractive index, effective dielectric permittivity, and soil water content, remote sensing assesses brightness temperature (
Figure 2a,b). Our study uses laboratory experiments and compares the results with widely used models, validating new approaches in the C band (
Figure 2c–e). A logarithmic model is developed to consider the composition of the mesoscopic particles and the bound water content, enhancing the accuracy of calculating the dielectric permittivity of cohesive soils.
Figure 2. Dielectric constant of the moist soil experiment: (
a) TDR and GPR sensing setup, (
b) dielectric constant connection between targeted soil properties and the estimated sensors parameters, (
c) sandy loam soil of the C band at 5 GHz, (
d) silt loam at 4 GHz, and (
e) silt clay at 6 GHz
[58][57].
Not all soil types can be accurately estimated by factory-generic calibrations for SWC sensors, especially those that rely on dielectric permittivity sensing
[10][9]. This study revealed that high-electric conductivity soils have a greater relative inaccuracy in SWC due to the spatial heterogeneity of farmland soils, and laboratory calibration is required. According to soil-specific calibration, accurate estimations with 0.05 m3m−3 errors are possible at certain locations. Therefore, it is recommended that the accuracy of the SWC be verified using factory-calibrated commercial sensors before conducting studies on extractable soil water, microbial processes, greenhouse gas fluxes, and spatial variability.
According to Xu et al.
[47], the fundamental structure of the soil is affected by the relative dielectric permittivity, which increases with increasing water content (
Figure 3a). With increased water content, free water also becomes more polarized, increasing the dielectric permittivity (
Figure 3b). The dielectric properties of the soil particles are also influenced by their compaction since the soil's dry density influences the particles' spacing. The dry density also increases the contact area between the soil particles (
Figure 3c,d). Large pores and a low dielectric permittivity characterize laterite, which has significant water absorption. The dielectric permittivity impacts Pore water and water film thickness, which rises with temperature and water content. Because temperature enhances the thermal movement of water molecules, altering density, viscosity, and polarizability, it substantially impacts the dielectric characteristics of the soil. The polarization ability of soil pore water accelerates as temperature rises due to an increase in the relative dielectric permittivity (
Figure 3e,f). This growth continues at a dry density of 1.15 g/cm−3, particularly when the water content is greater than 28% (
Figure 3e). At 15 °C, the relative dielectric permittivity increases with temperature (
Figure 3f).
Figure 3. Correlation between the relative dielectric constant and water content at (
a) ρd = 1.20 gcm
−3 and (
b) T = 20 °C; dry density at (
c) T = 15 °C and (
d) ω = 28%, and temperature at (
e) ρd = 1.15 gcm
−3 and (
f) ω = 28%
[47].
According to a study by
[38], there are variations in the data obtained from five temperature probes due to the various locations and refrigeration effects. The dielectric permittivity alters with temperature and may be classified into linear and non-linear stages (
Figure 4b). The use of five probes improves the accuracy of soil sample temperature measurement. According to the study, during freezing, the dielectric permittivity drops linearly with the increase in temperature, whereas at lower temperatures, it decreases rapidly and slowly. Volumetric fractions and soil components affect how the soil dielectric permittivity varies. After 10 h, a silty clay sample containing 0.1% K
2SO
4 stabilized, suggesting a 12 h hold period (
Figure 4a). However, a sudden increase in temperature and a significant decrease in the dielectric permittivity was observed due to latent heat release during the transformation of water into ice
[72,73][71][72].
Figure 4. Dielectric constant modelling and measurement. (
a) The time-temperature dielectric curve of silty clay samples; (
b) soil temperature changes (the temperature probes coincided with each other in the a–b and g–h stages but showed a visible difference in the c–d and e–f stages, especially during periods of decreasing temperature); (
c–
f) the dielectric constants of silty clay samples are subject to temperature variations and alter with varied water concentrations
[38].
3. Remote Sensing Based on Dielectric Properties of Soil Moisture
Soil moisture measurements are typically point measurements acquired using various techniques or embedded sensors such as TDR, FDR, and capacitance
[74][73]. These measurements are considered truthful on the ground because of their close contact with the soil. However, they have limited spatial coverage, necessitating the installation of a large or dense sensor network to monitor large field areas, which can lead to operational and maintenance costs. Improved remote sensing methods for precise soil moisture evaluation have been made possible by developments in dielectric property-based soil water content measurements
[28,31,49,75][28][31][49][74]. This connection facilitates important information on the dynamics of soil moisture, allowing for effective monitoring in wide regions and better decision-making in environmental management, hydrology, and agriculture
[76,77][75][76]. Based on the dielectric characteristics of the soil, microwave remote sensing techniques determine the moisture content of the soil using electromagnetic radiation in the microwave area
[78][77]. While passive sensors record electromagnetic radiation that is present in the environment, active sensors—such as synthetic aperture radar and ground-penetrating radar—measure electromagnetic radiation that they produce.
Understanding soil variability may be greatly aided by RS, particularly in regions with little soil sample availability or difficult topography
[28,58][28][57]. It is useful for mapping soil characteristics, identifying erosion, and providing high-resolution data on soil parameters such as moisture content and organic carbon concentration
[18,19,79][17][18][78]. The ability to see through clouds and vegetation, sensitivity to changes in soil moisture content, and a spatial resolution that can range from a few meters to several kilometres, depending on the sensors used and its altitude above the Earth’s surface, are just a few of the advantages that microwave remote sensing has over other methods
[75,80,81][74][79][80]. This makes it possible to accurately estimate soil moisture content, even in heavily forested or overcast areas. However, remote soil moisture detection using dielectric characteristics has several challenges and drawbacks. For example, it is challenging to create precise models because of the intricate link between soil moisture content and dielectric permittivity. The dielectric permittivity of the soil can be influenced by several factors, such as salinity, temperature, and texture, which can result in inaccurate soil moisture estimations
[17,82][16][81]. Due to the attenuation of electromagnetic radiation, the penetration depth of remote sensing methods is restricted, and their spatial resolution might not be adequate for applications that require precise information on small-scale variations in soil moisture content.
The most recent databases, modelling strategies, ground, near-surface, and satellite remote sensing techniques have been created to quantify surface, near-surface, and root zone soil moisture at different temporal and geographical resolutions
[78,83,84][77][82][83]. Spatial soil moisture networks and spatiotemporal SM data are being used more and more to increase our knowledge of hydrological processes, identify trends in the hydrological cycle, test hydrological models, define spatial soil moisture dynamics, and validate satellite RS observations
[78,85,86][77][84][85]. However, disparities in the scaling between in situ measurements and satellite sensor resolution and disconnects between the detecting depth of ground and distant sensors pose difficulties for the validation testing of coarse-scale SM products. Although increasing numbers of soil moisture networks are in situ, they do not always indicate the broader surrounding region.
Furthermore, studies have demonstrated the possibility of employing optical and thermal satellite measurements to estimate soil moisture at a high spatial resolution, such as the study of Alexandridis et al.
[87][86], who estimated root zone soil moisture using straightforward ancillary data and energy balance fluxes. The variation in precision was explained by factors such as the types of land cover, the class of soil texture, the time difference between the data sets, and the presence of rain events. With an eight-day time step and a spatial resolution of 250 m, the approach can estimate SM maps at the catchment scale. The possibility of employing optical and thermal band data to estimate soil moisture is demonstrated by both investigations in which surface soil moisture was obtained to give more information on the fusion of microwaves to acquire soil moisture at spatial and temporal resolutions. Using GPS sensor readings, Koch et al.
[88][87] created a novel method for capturing soil moisture based on changes in GPS signal intensity caused by fluctuations in the soil’s dielectric permittivity. The utilization of L-band microwave remote sensing data is the approach's primary benefit, making it appealing for use as validation data for SM products. It also allows for continuous extrapolation at typical locations and complementing satellite data with high geographical coverage but poor temporal precision. The bulk SM of the top soil layer is measured by GPS antennas positioned at a certain soil depth, making it appropriate for global sensors.
Furthermore, Li, et al.
[89][88] introduced the GPR-SWC neural network architecture, which enables the rapid inversion of volumetric SWC at field size via the common offset GPR technique. The model correctly pinpoints various volumetric SWC borders regarding temporal depth, with a maximum error of less than 0.10 cm
3 × cm
−3. Furthermore, the expected values of the soil sample and field values exhibit minimal variation, which aligns with the general trend of changing TDR detection levels. The study uses farm GPR data to reveal that GPR-SWC can invert the soil’s water content.
Torres-Rua [74] also estimated surface soil moisture using meteorological data and Landsat 7 using a Bayesian machine learning technique. The study took advantage of the precision and uncertainty of conventional methodologies for Landsat vegetation indices and surface energy balance products. Because the relevant vector machine technique is based on statistical modelling, it does not incorporate embedded uncertainty into the suggested soil moisture model. It is recommended that quality control processes be used to validate spatial data, particularly for gap filling, spatial evapotranspiration, and component products used in energy balancing. In remote sensing applications, autocorrelation is anticipated in spatial data; however, statistical behaviour in the vector learning machine model is related to the surface soil moisture observed. Future research could anticipate spatial evapotranspiration rate and soil water content for irrigation water balancing operating systems and estimate soil moisture at deeper depths. Implementing procedures to measure and reduce the influence of data sources and model uncertainty on outcomes is necessary.
4. Applications of Dielectric Models in Soil Water Content Measurements
With the use of dielectric permittivity measurements, understanding SWC is crucial for many applications in agriculture, hydrology, environmental management, remote sensing, and soil salinity
[11]. By examining how water moves through various soil types, scientists may improve their models that forecast floods and droughts. By evaluating the effects of changing land use on the amount of water in the soil, environmental management can guarantee the sustainability and health of ecosystems. RS applications, such as tracking soil water content using satellite data, can enhance climate models and global water cycles
[90][89]. These measures may also be used to determine the salinity of the soil, which is crucial for agriculture and the health of the environment. This allows farmers to make informed decisions about crop selection and irrigation practices
[30]. The results of various experiments on measuring SWC using dielectric properties are extensively discussed in
Table 4.
Table 4. Results of various experiments in the measurement of SWC using dielectric properties. Results of various experiments in the measurement of SWC using dielectric properties.
Experiment | Objectives/Aim | Findings | References |
---|
Soil’s specific features and calibration | Focused on the FDR sensors on their factory calibration |
| [9] |
Calibration procedure for electromagnetic SWC sensors | To demonstrate the recent and effective calibration methods for low-cost EM sensors |
-
Sensor-specific calibration increases accuracy
-
Quickly completes large-scale calibration.
-
Minimizes errors and requires less work.
| [53] |
Laterite’s dielectric characteristics and constant model | To examine the mechanical and physical aspects of in situ laterite dielectric properties |
| [47] |
Measurement and modelling of the dielectric permittivity of soil | To suggest, locate, and demonstrate fresh approaches to determining the dielectric permittivity during freezing |
-
Linear and nonlinear trends.
-
Higher values during freezing/thawing.
-
Variations based on soil water content.
| [38] |
Dielectric analysis models for measurement of SWC | To presents a normalization-based calibration model. |
| [19] |
Saturated prediction model using TDR | To suggest the level of soil’s saturation with different control criterion for compaction quality |
| [90] |
Calibration of the dielectric permittivity model for agricultural soils | To investigates using three pre-established dielectric permittivity models |
| 8 | Zanies loam | 48.0 | 36.0 | 16.0 | 0.114 | 0.22 | 0.40 | 8 |
9 | Collinville loam | 45.0 | 39.0 | 16.0 | 0.115 | 0.23 | 0.40 | 8 |
10 | Kirkland silt loam | 26.0 | 56.0 | 18.0 | 0.137 | 0.20 | 0.40 | 8 |
11 | Vernon clay loam | 16.0 | 56.0 | 22.0 | 0.192 | 0.28 | 0.45 | 26 |
12 | Tabler silt loam | 22.0 | 56.0 | 22.0 | 0.159 | 0.19 | 0.40 | 8 |
13 | Long lake clay | 6.0 | 54.0 | 40.0 | 0.255 | 0.26 | 0.40 | 26 |
14 | Sand | 86.0 | 7.0 | 7.0 | 0.046 | 0.20 | 0.40 | 0 |
15 | Miller clay | 3.0 | 35.0 | 62.0 | 0.361 | 0.33 | 0.30 | 20 |