A New Drone-Borne GPR for Soil Moisture Mapping

Wu, K.; Rodriguez, G.; Zajc, Marjana; Jacquemin, E.; Clement, M.A.; Coster, Albéric De; Lambot, Sébastien

doi:10.3997/2214-4609.201902587

Cited by 17 publications

(23 citation statements)

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“…The proposed method characterizes RZSM of the deep subsoil, for example, up to 0.9 m deep in this study (Figure 12), whereas previous GPR‐based methods commonly obtain the average soil moisture only to the depth of 0.2 m (e.g., Qin et al, 2013; Weihermüller et al, 2007; Wu et al, 2019). The proposed method is effective in quantifying RZSM to the depth of the deepest coarse roots that can be detected in GPR images.…”

Section: Discussionmentioning

confidence: 86%

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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Section: Discussionmentioning

confidence: 86%

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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“…When performing calculations according to expressions (4), (5) for each transition from one layer to another, it is necessary to take into account that the angle of incidence for the next layer will be equal to the angle of refraction of the previous one. The calculation of the angle of refraction value is performed on the basis of Snell's second law (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Depending on the area of application and the tasks to be solved, different frequencies, polarizations, and structures of the probing signal are used [3; 4]. Depending on the location of the radar equipment (georadar, GPR), ground-based, satellite, and aviation (including unmanned) radar sounding systems are distinguished [5].…”

Section: Introductionmentioning

confidence: 99%

Bistatic system for radar sensing of soil moisture

Bazhenov

Sagdeev

Goncharov

et al. 2021

20th International Scientific Conference Engineering for Rural Development Proceedings

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The article is devoted to the development of a method for determining the moisture content of the upper layers of agricultural lands on the basis of mathematical modeling of a radar signal reflected from the soil. For the radar study of the moisture content of agricultural lands, it is proposed to use remote sensing using relatively inexpensive unmanned aerial vehicles (UAVs) of a multi-rotor type (a bistatic system consisting of a radar transmitter and a radar receiver placed on two UAVs). At the same time, it is necessary to ensure a high probability of correct determination of soil moisture to a depth exceeding the groundwater level. In remote sensing, the reflection coefficient from the air-soil interface depends on the dielectric properties of the top soil layer and the polarization of the sounding signal. When modeling the reflected signal, it is advisable to represent the earth's surface as a flat-layered structure, the upper part of which will be represented by vegetation and heterogeneities followed by the area with a relatively constant dielectric constant value to the groundwater level and then the area with a linearly increasing conductivity. Studies have shown that the Brewster effect in soils manifests itself when the probe signal is perpendicularly polarized. When the soil surface is irradiated with a perpendicularly polarized radar signal at Brewster's angle, the required penetration depth of the probing signal is ensured, at which the reflected signal will contain enough information about the percentage of moisture in the upper soil layers. The information contained in changes in the amplitude and phase of the reflected radio signal is related to the structure and dielectric properties of the upper soil layers. On the basis of this information, rational agro-technical measures can be justified, both operational (watering, fertilization) and long-term (reclamation, drainage).

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“…Previously reported methods to estimate soil moisture content include in situ measurements (gravimetric, dielectric measurements, etc.) [12][13][14][15][16], as well as remote sensing approaches, at radar [17][18][19][20][21][22][23][24][25], infrared [26,27] and optical [18,[28][29][30][31][32][33][34][35] wavelengths that are known to probe water absorption bands. Unfortunately, most in situ methods either lack the sensitivity or selectivity towards the surface layer, or cannot be easily deployed for continuous real-time monitoring and field applications [13,[36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Monitoring moisture content and evaporation kinetics from mine slurries through albedo measurements to help predict and prevent dust emissions

2021

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The prediction and prevention of fugitive dust emissions from mine tailings surfaces depend largely on our ability to monitor and monitor and predict the evolution of tailings moisture content (TMC). Albedo measurements are demonstrated here to be valuable tools to quantify TMC in bauxite residue samples under controlled conditions in the laboratory. The difference in albedo between 1.30 and 1.55 µm obtained through the infrared integrating sphere method shows good correlations with those acquired with a field spectroradiometer while both are strongly correlated with TMC. Additionally, continuous spectroscopic characterization of evaporating residues is shown to reveal the evolution in their surface drying rates. These optical methods could help predict surface drying state, thereby improving the accuracy of dust emissions risk assessment protocols that support mining industries intervention and mitigation strategies.

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A New Drone-Borne GPR for Soil Moisture Mapping

Cited by 17 publications

References 0 publications

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

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

Bistatic system for radar sensing of soil moisture

Monitoring moisture content and evaporation kinetics from mine slurries through albedo measurements to help predict and prevent dust emissions

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