Measurement of soil water content using ground-penetrating radar: a review of current methods

Liu, Xinbo; Chen, Jin; Cui, Xihong; Liu, Qixin; Cao, Xin; Chen, Xuehong

doi:10.1080/17538947.2017.1412520

Cited by 51 publications

(38 citation statements)

References 102 publications

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“…The ECa is not only influenced by SWC, but also by other parameters, e.g., texture, organic matter content, size and distribution of pores, salinity, and temperature [10][11][12]. Monitoring and evaluating shallow SWC by GPR methods have been shown to be an alternative technique [13][14][15], even if originally it has been used to determine soil horizons depth, extent, lateral variations, and properties [16]. GPR allows non-invasive measures of large volumes of soil (about cubic decimeters to cubic meters).…”

Section: Introductionmentioning

confidence: 99%

Mapping an Agricultural Field Experiment by Electromagnetic Induction and Ground Penetrating Radar to Improve Soil Water Content Estimation

Benedetto

Montemurro²,

Diacono

2019

Agronomy

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A growing interest in proximal sensing technologies for estimating soil water content (SWC) will be highlighted. On this matter the objectives of this study were: (1) to use both the combined electromagnetic induction (EMI) sensor and Ground Penetrating Radar (GPR) to characterize an innovative field experiment located in southern Italy, in which different agricultural practices are tested, including a soil hydraulic arrangement; (2) to implement a geostatistical approach in order to merge different geophysical sensor data as auxiliary variables for SWC estimation. The multi-sensor recorded data were: (1) SWC data measured by gravimetric method; (2) Differential Global Positioning System height; (3) apparent electrical conductivity measured by an EMI sensor; (4) depths of soil discontinuities individuated by GPR radargrams interpretation; and (5) amplitude of GPR signal data at two different frequencies. Geostatistical techniques were used both to map all variables and improve the SWC estimation. The findings of this research indicate that: (1) the GPR radargrams identified four reflection events as a consequence of interfaces; (2) the EMI and GPR mapping provided identification of areas with high potential for water stagnation; and (3) the outputs of geophysical sensors can be effectively used as auxiliary tools to supplement the sampling of the target variable and to improve water content estimation.

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Section: Introductionmentioning

confidence: 99%

Mapping an Agricultural Field Experiment by Electromagnetic Induction and Ground Penetrating Radar to Improve Soil Water Content Estimation

Benedetto

Montemurro²,

Diacono

2019

Agronomy

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“…Instead of using the traditional hyperbola fitting method in commercially available software, a modified algorithm such as multiresolution monogenic signal analysis would increase the accuracy and efficiency of the proposed GPR method. As noted in the recent review of current GPR‐based SM measurement methods by Liu et al (), the automated recognition of hyperbolic signals should not only improve the efficiency of the method but also eliminate the need to know the depth of the reflector. To avoid the dependability of the hyperbola fitting on different analysts (Sham & Lai, ), we followed a systematic guideline for GPR data processing.…”

Section: Introductionmentioning

confidence: 99%

Evaluating soil moisture estimation from ground‐penetrating radar hyperbola fitting with respect to a systematic time‐domain reflectometry data collection in a boreal podzolic agricultural field

Illawathure

Parkin

Lambot

et al. 2020

Hydrological Processes

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The upper 30 cm of the soil profile, which hosts the majority of the root biomass, can be considered as the shallow agricultural root zone of most temperate crops. The electromagnetic wave velocity in the soil obtained from reflection hyperbolas in ground‐penetrating radar (GPR) data can be used to estimate soil moisture (SM). Finding shallow hyperbolas in a radargram and minimizing the subjective error associated with the hyperbola fitting are the main challenges in this approach. Nevertheless, we were motivated by the recent improvements of hyperbola fitting algorithms, which can reduce the subjective error and processing time. To overcome the difficulty of finding very shallow hyperbolas, we applied the hyperbola fitting method to reflections ranging from 27‐ to 50‐cm depth using a 500‐MHz centre‐frequency GPR and compared the estimated moisture with vertically installed, 30‐cm‐long time‐domain reflectometry (TDR) sensors. We also compared TDR and GPR sample areas in a 2‐D plane using different GPR survey types and different hyperbola depths. SM measured with TDR and GPR were not significantly different according to Mann–Whitney's test. Our analyses showed that a root mean square error of 0.03 m3 m−3 was found between the two methods. In conclusion, the proposed method might be suitable to estimate SM with an acceptable accuracy within the root zone if the soil profile is fairly uniform within the application depth range.

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“…To date, a series of GPR‐based methods, including the reflected wave, ground wave, surface reflection, full waveform inversion, average envelope amplitude, and frequency shift methods, have been developed for various GPR systems (e.g., ground‐coupled, air‐coupled, and borehole systems) to map soil moisture (e.g., Jonard et al, 2017; Klotzsche et al, 2018; Liu, Chen, et al, 2019; Tosti & Slob, 2015; Vereecken et al, 2014). Borehole GPR requires the excavation of access tubes and has a limited survey area (Slob et al, 2010), while air‐coupled GPR is most effective for measuring the soil moisture of the top few centimeters of the soil (Huisman et al, 2003; Qin et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Noninvasive 2D and 3D Mapping of Root Zone Soil Moisture Through the Detection of Coarse Roots With Ground‐Penetrating Radar

Liu

Chen

Butnor

et al. 2020

Water Resources Research

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Root zone soil moisture (RZSM) is important in sustaining terrestrial ecosystems and water cycling. However, noninvasive mapping of RZSM remains challenging in the field, especially its spatial variability at local scales. We propose a novel method of applying ground-penetrating radar (GPR) for noninvasive determination of RZSM. First, this method identified coarse root reflections in GPR images to obtain the reflected wave velocity and soil water storage at various locations. Then, the horizontal distributions of soil water storage to different depths were reconstructed using the inverse distance weighted interpolation. The difference in soil water storage between two depths was converted into 2D RZSM distribution for a specific depth interval. Finally, 2D RZSM distributions from a range of depths were combined to produce a 3D visualization of RZSM. The proposed method was validated in two field plots (30 m by 30 m) in shrublands. The comparisons with 2D RZSM distributions from soil cores sampled at 0.2-m depth intervals suggested a reasonable correspondence in both spatial patterns and absolute values, with the average root-mean-square error less than 0.017 m 3 ·m −3 and correlation coefficients ranging from 0.502 to 0.798 in both plots. Furthermore, 3D visualizations of RZSM were generated based on GPR estimates to demonstrate the spatial variability of soil moisture at the study scale. The proposed method enhances noninvasive characterization of soil moisture down to the root zone, which contributes to a better understanding of the local-scale variability of soil moisture in the subsoil as well as the ecohydrological processes in the soil-plant-atmosphere continuum.

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Measurement of soil water content using ground-penetrating radar: a review of current methods

Cited by 51 publications

References 102 publications

Mapping an Agricultural Field Experiment by Electromagnetic Induction and Ground Penetrating Radar to Improve Soil Water Content Estimation

Mapping an Agricultural Field Experiment by Electromagnetic Induction and Ground Penetrating Radar to Improve Soil Water Content Estimation

Evaluating soil moisture estimation from ground‐penetrating radar hyperbola fitting with respect to a systematic time‐domain reflectometry data collection in a boreal podzolic agricultural field

Noninvasive 2D and 3D Mapping of Root Zone Soil Moisture Through the Detection of Coarse Roots With Ground‐Penetrating Radar

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