What is the impact of roots on GPR data? A synthetic study of the soil-plant continuum

The soil-plant continuum of agricultural crops is regulating key processes that affect plant performance and agricultural productivity. As climate change impacts agricultural systems, understanding these processes will become increasingly important, especially when increasing yield productivity, while minimizing the environmental footprint are key aspects. Quantifying the impact of climate change and management practices on crop growth requires understanding about the dynamics of the root systems of crops. Ground penetrating radar (GPR) combined with root imaging and modeling techniques offers a unique opportunity to study these dynamics in function of soil, climate and management. As a first step, this study examined the relationship between root development and soil dielectric permittivity variability using root images and 200 MHz time-lapse horizontal crosshole GPR at two field minirhizotron (MR) facilities in Selhausen, Germany. The data was acquired over three maize growing seasons, in 7-m long rhizotubes at six different depths, ranging between 0.1 m - 1.2 m and for three different plots with varying agricultural treatments. We calculated trend-corrected spatial permittivity deviations to isolate root-related effects by removing static and dynamic influences caused by soil heterogeneity and changing weather conditions. This permittivity deviation increased during the growing season, correlating with root presence. Cross-correlation analysis between permittivity variability and root volume fraction yielded in coefficients of determination above 0.5 for half of the data pairs. From this study some questions remained unanswered, such as identifying individual roots or quantifying the influence of roots and above-ground shoot on the GPR signal. Subsequently, synthetic forward modeling was conducted using the data acquisition of the previous study as a template and the open-source electromagnetic simulation software gprMax. GPR traces were modeled and analyzed for scenarios with varying soil-plant continuum compositions, including soil, roots, and above-ground shoots in two- or three dimensions. The models incorporated realistic root contributions based on trench wall counts. We found that the presence of roots, which resulted in a permittivity increase on one hand, had a higher influence on the GPR signal than the above-ground shoot and on the other hand the roots affected the first arrival time and amplitudes of the GPR signal. Hence more sophisticated analysis techniques such as full-waveform inversion are necessary. Furthermore, we introduced an approach to derive the soil water content within the soil-plant continuum, where the CRIM petrophysical model was extended with the root phase. This showed that neglecting the root phase leads to overestimation of soil water contents.

Soil water content (SWC) estimation is important for many areas including hydrology, agriculture, soil science, and environmental science. Ground penetrating radar (GPR) is a promising geophysical method for SWC estimation. However, at present, most of the studies are based on partial information of GPR, like travel time or amplitude information, to invert the SWC. Full waveform inversion (FWI) can use the information of the entire waveform, which can improve the accuracy of parameter estimation. This study proposes a novel SWC estimation scheme by using the FWI of GPR, optimized by the grey wolf optimizer (GWO) algorithm. The proposed scheme includes a petrophysical relationship to link the SWC with the relative dielectric permittivity, 1D GPR forward modeling, and a GWO optimization algorithm. First, numerical modeling was carried out, and the proposed scheme was applied to both noise‐free and noisy data to verify its applicability. Then, the proposed method was applied to data collected from a field experimental site. These results, derived from both synthetic and real datasets, show that the proposed inversion scheme resulted in a good match between the observed and calculated GPR data. In the numerical modeling, it was observed that the SWC could be inverted accurately, even when noise was present in the data. These demonstrate that the GWO method can be applied for the quantitative interpretation of GPR data. The proposed scheme shows potential for SWC estimation by using GPR full waveform data.

Accurate characterization of near-surface soil water content is vital for guiding agricultural management decisions and for reducing the potential negative environmental impacts of agriculture. Characterizing the near-surface soil water content can be difficult, as this parameter is often both spatially and temporally variable, and obtaining sufficient measurements to describe the heterogeneity can be prohibitively expensive. Understanding the spatial correlation of near-surface soil water content can help optimize data acquisition and improve understanding of the processes controlling soil water content at the field scale. In this study, ground penetrating radar (GPR) methods were used to characterize the spatial correlation of water content in a three acre field as a function of sampling depth, season, vegetation, and soil texture. GPR data were acquired with 450 MHz and 900 MHz antennas, and measurements of the GPR groundwave were used to estimate soil water content at four different times. Additional water content estimates were obtained using time domain reflectometry measurements, and soil texture measurements were also acquired. Variograms were calculated for each set of measurements, and comparison of these variograms showed that the horizontal spatial correlation was greater for deeper water content measurements than for shallower measurements. Precipitation and irrigation were both shown to increase the spatial variability of water content, while shallowly-rooted vegetation decreased the variability. Comparison of the variograms of water content and soil texture showed that soil texture generally had greater small-scale spatial correlation than water content, and that the variability of water content in deeper soil layers was more closely correlated to soil texture than were shallower water content measurements. Lastly, cross-variograms of soil texture and water content were calculated, and co-kriging of water content estimates and soil texture measurements showed that geophysically-derived estimates of soil water content could be used to improve spatial estimation of soil texture.

: The objectives of this work were to: (1) evaluate the influence of hydrologic processes (i.e., changes in soil water content) on ground-penetrating radar (GPR) signals, particularly those associated with landmines, and (2) investigate the potential for developing contextual GPR classification systems by accounting for changes in environmental state (i.e., soil water content) using hydrologic modeling. The estimation of soil water content is a major focus of this work since this property is closely related to EM wave propagation in soils (i.e., dielectric constant, electrical conductivity, wave velocity), which control radar responses. The general research hypothesis guiding this work is that accounting for hydrologic state in classification systems will allow for improved generalization of landmine classification tools to a broader range of sites under varying operational conditions. The focus of the research was on two-dimensional imaging and simulation, though we also demonstrated the value of three-dimensional GPR imaging for improved object detection and characterization of flow processes in soils and around buried objects. Overall we found that accurate estimates of water content can be derived from GPR signals and that accounting for the water content of a soil within a contextual classification system is likely to improve classification results. The classification gains observed in this study were somewhat modest when comparing a contextual classifier to a non-contextual classifier that was trained over targets observed for a large set of hydrologic conditions. Both of these approaches significantly outperformed a classification strategy that first attempted to correct GPR signals observed at arbitrary conditions to a single hydrologic reference state. We are continuing to evaluate the significance of our results to scenarios representative of a broader range of conditions than those considered in this study.

Comparison of soil water content estimation equations using ground penetrating radar

The appropriate of petrophysical relationship is needed for Soil Water Content (SWC) estimation especially when using Ground Penetrating Radar (GPR). Ground penetrating radar is a geophysical tool that provides indirectly the parameter of SWC. This paper examines the performance of few published petrophysical relationships to obtain SWC estimates from in-situ GPR common-offset survey measurements with gravimetric measurements at peat soil area. Gravimetric measurements were conducted to support of GPR measurements for the accuracy assessment. Further, GPR with dual frequencies (250MHhz and 700MHz) were used in the survey measurements to obtain the dielectric permittivity. Three empirical equations (i.e. Roth’s equation, Schaap’s equation and Idi’s equation) were selected for the study, used to compute the soil water content from dielectric permittivity of the GPR profile. The results indicate that Schaap’s equation provides strong correlation with SWC as measured by GPR data sets and gravimetric measurements.

ABSTRACTTo investigate transient dynamics of soil water redistribution during infiltration, we conducted horizontal borehole and surface ground penetrating radar measurements during a 4‐day infiltration experiment at the rhizontron facility in Selhausen, Germany. Zero‐offset ground penetrating radar profiling in horizontal boreholes was used to obtain soil water content information at specific depths (0.2, 0.4, 0.6, 0.8 and 1.2 m). However, horizontal borehole ground penetrating radar measurements do not provide accurate soil water content estimates of the top soil (0–0.1 m depth) because of interference between direct and critically refracted waves. Therefore, surface ground penetrating radar data were additionally acquired to estimate soil water content of the top soil. Due to the generation of electromagnetic waveguides in the top soil caused by infiltration, a strong dispersion in the ground penetrating radar data was observed in 500 MHz surface ground penetrating radar data. A dispersion inversion was thus performed with these surface ground penetrating radar data to obtain soil water content information for the top 0.1 m of the soil. By combining the complementary borehole and surface ground penetrating radar data, vertical soil water content profiles were obtained, which were used to investigate vertical soil water redistribution. Reasonable consistency was found between the ground penetrating radar results and independent soil water content data derived from time domain reflectometry measurements. Because of the improved spatial representativeness of the ground penetrating radar measurements, the soil water content profiles obtained by ground penetrating radar better matched the known water storage changes during the infiltration experiment. It was concluded that the combined use of borehole and surface ground penetrating radar data convincingly revealed spatiotemporal soil water content variation during infiltration. In addition, this setup allowed a better quantification of water storage, which is a prerequisite for future applications, where, for example, the soil hydraulic properties will be estimated from ground penetrating radar data.

Mapping agricultural soil water content using multi-feature ensemble learning of GPR data

The determination of quantitative relationship between soil dielectric constant and water content is an important basis for measuring soil water content based on ground penetrating radar (GPR) technology. The calculation of soil volumetric water content using GPR technology is usually based on the classic Topp formula. However, there are large errors between measured values and calculated values when using the formula, and it cannot be flexibly applied to different media. To solve these problems, first, a combination of GPR and shallow drilling is used to calibrate the wave velocity to obtain an accurate dielectric constant. Then, combined with experimental moisture content, the intelligent group algorithm is applied to accurately build mathematical models of the relative dielectric constant and volumetric water content, and the Topp formula is revised for sand and clay media. Compared with the classic Topp formula, the average error rate of sand is decreased by nearly 15.8%, the average error rate of clay is decreased by 31.75%. The calculation accuracy of the formula has been greatly improved. It proves that the revised model is accurate, and at the same time, it proves the rationality of the method of using GPR wave velocity calibration method to accurately calculate the volumetric water content.

Ground penetrating radar (GPR) is often used to detect buried objects and evaluate structural condition. However, the direct wave and random noise often influence the arrival-time detection and the target-position location. We present a new application of Shearlet transform (ShT) to GPR data processing for direct wave removal and random noise suppression. ShT is a non-adaptive geometric-analysis technique, which has the properties of multi-directions and multi-scale, so it can show the optimal representations of signals in higher dimensions. The original GPR data is transformed to the ShT domain. The direct wave and the remaining GPR signal are effectively separated. While we eliminate the direct wave, the GPR signal is not damaged. The Shearlet coefficients of the GPR signal are relatively large, whereas random noises are relatively small. So we can use the threshold algorithm depending on different scales and directions in the ShT domain to suppress random noise. The GPR signal can be preserved very well and SNR is enhanced.

Noise suppressing and direct wave arrivals removal in GPR data based on Shearlet transform

Soil hydraulic parameters (SHP) play a crucial role controlling the spatiotemporal distribution of water in the soil–plant continuum and thus affect water availability for crops. To provide reliable information on the SHP at different scales, measurement techniques with a good spatial resolution and low labor costs are required. In this study, we used crosshole ground penetrating radar (GPR)‐derived soil water contents (SWCs) measured along horizontal rhizotubes under a controlled experimental test site cropped with winter wheat to estimate the unimodal and dual‐porosity soil hydraulic characteristics with different soil layer setups. Therefore, sequential inversion of the GPR‐derived SWCs was performed using the hydrological model HYDRUS‐1D, whereby the SWC data were either averaged prior inversion or used in a spatially distributed way. To analyze if the time‐lapse gathered GPR data contain enough information to estimate the SHP, additional synthetic studies were performed increasing the data resolution to daily GPR measurements. The results showed that the time‐lapse data contained enough information to estimate the SHP accurately. Additionally, spatially distributed soil hydraulic characteristics differed from the one estimated based on averaged SWCs derived from spatially distributed GPR data. Finally, we derived spatially resolved SHP, which can be used for 3D process rhizosphere processes and root–soil interaction modeling.

ABSTRACTEvaporation is an important process in the global water cycle and its variation affects the near surface soil water content, which is crucial for surface hydrology and climate modelling. Soil evaporation rate is often characterized by two distinct phases, namely, the energy limited phase (stage‐I) and the soil hydraulic limited period (stage‐II). In this paper, a laboratory experiment was conducted using a sand box filled with fine sand, which was subject to evaporation for a period of twenty three days. The setup was equipped with a weighting system to record automatically the weight of the sand box with a constant time‐step. Furthermore, time‐lapse air‐launched ground penetrating radar (GPR) measurements were performed to monitor the evaporation process. The GPR model involves a full‐waveform frequency‐domain solution of Maxwell’s equations for wave propagation in three‐dimensional multilayered media. The accuracy of the full‐waveform GPR forward modelling with respect to three different petrophysical models was investigated. Moreover, full‐waveform inversion of the GPR data was used to estimate the quantitative information, such as near surface soil water content. The two stages of evaporation can be clearly observed in the radargram, which indicates qualitatively that enough information is contained in the GPR data. The full‐waveform GPR inversion allows for accurate estimation of the near surface soil water content during extended evaporation phases, when a wide frequency range of GPR (0.8–5.0 GHz) is taken into account. In addition, the results indicate that the CRIM model may constitute a relevant alternative in solving the frequency‐dependency issue for full waveform GPR modelling.

Monitoring underground fluid infiltration and estimating soil water content (SWC) is essential for hydrogeology and soil science research. Conventional hydrological survey methods, such as trenching, soil sampling, or time domain reflectometry (TDR), provide in-situ, static, and discrete measurements. However, these methods have limitations in capturing the spatiotemporal dynamics of fluid infiltration. Ground penetrating radar (GPR) offers a noninvasive and high spatiotemporal measurement approach for near-surface applications. Nevertheless, the complexity of GPR data and its weak response to small-scale fluid infiltration pose challenges in instantaneous attribute analysis and SWC estimation using the travel-time method. In this paper, we propose a time-lapse GPR data full waveform inversion (FWI) method to effectively monitor the spatial distribution of small-scale fluid transport and estimate SWC with improved accuracy. Firstly, we combine velocity spectral analysis and the structural similarity index method (SSIM) to construct a permittivity model that solves the dependence of FWI on the initial model. Subsequently, to simultaneously invert the permittivity and conductivity, we introduce gradient normalization to balance the convergence rate of the two parameters during the inversion process. The typical GPR time-lapse infiltration GPR field data example confirms that the proposed method can accurately characterize small-scale fluid infiltration distribution and estimate SWC parameters. The total estimated error in SWC is found to be less than 5%. The proposed time-lapse GPR data processing protocol provides a quantitative means for monitoring small-scale fluid infiltration.

Direct wave is one of the major interferences to ground penetrating radar (GPR) signal, and the elimination of direct wave is a key technology for shallow object identification. In view of the features of direct wave in the GPR signal, it can be eliminated by taking 2-D physical wavelet as the basic wavelet to conduct wavelet transform on the GPR signal and selecting proper wavelet scale to estimate direct wave interference. The method of 2-D physical wavelet frame has been verified to be effective through processing the actually-detected data of two GPRs. Compared to 2-D continuous directed wavelet transform, it embraces the advantages of small memory occupancy and fast calculation speed.