Mapping subsurface drainage in agricultural areas using a frequency-domain ground penetrating radar

Artificial subsurface drainage systems are installed in agricultural areas to remove excess water and convert poorly naturally drained soils into productive cropland. Some of the most productive agricultural regions in the world are a result of subsurface drainage practices. Drain lines provide a shortened pathway for the release of nutrients and pesticides into the environment, which presents a potentially increased risk for eutrophication and contamination of surface water bodies. Knowledge of drain line locations is often lacking. This complicates the understanding of the local hydrology and solute dynamics and the consequent planning of mitigation strategies such as constructed wetlands, saturated buffers, bioreactors, and nitrate and phosphate filters. In addition, accurate knowledge of the existing subsurface drainage system is required in designing the installation of a new set of drain lines to enhance soil water removal efficiency. The traditional methods of drainage mapping involve the use of tile probes and trenching equipment which are time-consuming, tiresome, and invasive, thereby carrying an inherent risk of damaging the drain pipes. Non-invasive geophysical sensors provide a potential alternative solution to the problem. Previous research has focused on the use of time-domain ground penetrating radar (GPR) with variable success depending on local soil and hydrological conditions and the center frequency of the specific equipment used. For example, 250 MHz antennas proved to be more suitable for drain line mapping. Recent technological advancements enabled the collection of high-resolution spatially exhaustive data. In this study, we present the use of a stepped-frequency continuous wave (SFCW) 3D-GPR (GeoScope Mk IV 3D-Radar with DXG1820 antenna array) mounted in a motorized survey configuration with real-time georeferencing for subsurface drainage mapping. The 3D-GPR system offers more flexibility for application to different (sub)surface conditions due to the coverage of wide frequency bandwidth (60-3000 MHz). In addition, the wide array swathe of the antenna array (1.5 m covered by 20 measurement channels) enables effective coverage of three-dimensional (3D) space. The surveys were performed on twelve different study sites with various soil types with textures ranging from sand to clay till. While we achieved good success in finding the drainage pipes at five sites with sandy, sandy loam, loamy sand and organic topsoils, the results at the other seven sites with more clay-rich soils were less successful. The high attenuation of electromagnetic waves in highly conductive clay-rich soils, which limits the penetration depth of the 3D-GPR system, can explain our findings obtained in this research

Mapping of agricultural subsurface drainage systems using a frequency-domain ground penetrating radar and evaluating its performance using a single-frequency multi-receiver electromagnetic induction instrument

Subsurface drainage systems are commonly used to remove surplus water from the soil profile of a poorly drained farmland. Traditional methods for drainage mapping involve the use of tile probes and trenching equipment that are time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Effective and efficient methods are needed in order to map the buried drain lines: (1) to comprehend the processes of leaching and offsite release of nutrients and pesticides and (2) for the installation of a new set of drain lines between the old ones to enhance the soil water removal. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has mainly showcased the use of time-domain ground penetrating radar, with variable success, depending on local soil and hydrological conditions and the central frequency of the specific equipment used. The objectives of this study were: (1) to test the use of a stepped-frequency continuous wave three-dimensional ground penetrating radar (3D-GPR) with a wide antenna array for subsurface drainage mapping and (2) to evaluate its performance with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor in-combination. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3D-GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to the limited penetration depth of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of apparent electrical conductivity data measured by the EMI sensor could be a useful proxy for explaining the success achieved by the 3D-GPR in finding the drain lines

Mapping of Peat Thickness Using a Multi-Receiver Electromagnetic Induction Instrument

Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (ECa) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range from 3 to 730 cm. Simple and multiple linear regressions with soil observations from 110 sites were used to predict peat thickness from (a) raw ECa measurements (i.e., single and multiple-coil predictions), (b) true electrical conductivity (σ) estimates calculated using a quasi-three-dimensional inversion algorithm and (c) different combinations of ECa data with environmental covariates (i.e., light detection and ranging (LiDAR)-based elevation and derived terrain attributes). The results indicated that raw ECa data can already constitute relevant predictors for peat thickness in the study area, with single-coil predictions yielding substantial accuracies with coefficients of determination (R2) ranging from 0.63 to 0.86 and root mean square error (RMSE) values between 74 and 122 cm, depending on the measuring DUALEM-421S coil configuration. While the combinations of ECa data (both single and multiple-coil) with elevation generally provided slightly higher accuracies, the uncertainty estimates for single-coil predictions were smaller (i.e., smaller 95% confidence intervals). The present study demonstrates a high potential for EMI data to be used for peat thickness mapping

Three dimensional (3-D) mapping of soil properties using geophysical instruments

Information about soil fertility and salinity are important to sustain the ever-growing human population and understand the implications of soil security of diminishing nutrient status and salinization. This is the case in developed and developing countries. The former because of the need to understand the sustainability of cereal cropping and the use of soil nutrients such as the exchangeable cations which is measured as cation exchange capacity (CEC – cmol(+)/kg). The second because of the use of inefficient irrigation practices which causes soil salinity and is measured as electrical conductivity of the saturated soil-past extract (ECe – dS/m). In both cases, and owing to the expense of soil sampling and laboratory analysis it is difficult to map these soil properties across any given field let along at various depth. In this regard, geophysical methods such as direct current resistivity (DCR) and electromagnetic (EM) induction are appropriate because they can provide rapid, repeatable and reliable ancillary information which can be correlated with CEC and ECe. However, in most cases the data has been used to make soil maps of properties at individual or average depths. In this thesis, quasi-3d inversion algorithms were used to make models from a DCR (Veris-3100) and EM (DUALEM-21S) instruments. Firstly, a Veris-3100 instrument (DCR) was used to develop a three-dimensional map of CEC across a study field in south-west Spain. The CEC is one of the most important soil properties as it influences soil’s ability to hold essential nutrients. It also acts as an index of structural resilience. In this study, a linear regression (LR) was developed between the calculated true electrical conductivity (σ – mS/m) and measured CEC at various depths (i.e. 0-0.3 m, 0.3-0.6 m & 0.6-0.9 m). The estimates of σ were made by inverting Veris-3100 data (ECa – mS/m) using a quasi-3d inversion algorithm (invVeris V1.1). The best LR between σ and CEC was achieved using S2 inversion algorithm using a damping factor (λ) = 18. The LR (CEC = 1.77 + 0.33 × σ) had a large coefficient of determination (R2 = 0.89). To determine the predictive capability of the LR, a cross-validation technique was used to validate the model. Given the high accuracy (root-mean-square-error [RMSE] = 1.69 cmol(+)/kg), small bias (mean-error [ME] = -0.00 cmol(+)/kg) and large coefficient of determination (R2 = 0.88) and Lin’s concordance (0.94), between measured and predicted CEC and at various depths, good predictions of CEC distribution were made in topsoil and the subsurface. However, the predictions made in the subsoil were poor due to limited data availability in areas where ECa changed rapidly from low to high values. Secondly, a DUALEM-21S instrument was used for three-dimensional mapping of the ECe across a study field near Mokhra Kheri, in central Haryana, India. The study field was degraded due to secondary salinization caused because of poor soil, crop and water management. An LR was developed between σ and laboratory measured ECe at various depths (i.e. 0-0.3 m, 0.3-0.6 m, 0.6-0.9 m & 0.9-1.2 m). The σ was estimated by inverting DUALEM-21S ECa data using a quasi-3d inversion algorithm (EM4Soil V302). The best LR between σ and ECe was achieved using full solution (FS) forward modelling, S1 inversion algorithm and a damping factor (λ) of 0.6. The LR (ECe = -11.81 + 0.043 × σ) had a large R2 = 0.84. A similar cross validation technique was used to determine predictive capability of the LR. Given the high accuracy (RMSE = 8.31 dS/m), small bias (ME = -0.0628 dS/m) and large R2 = 0.82 and Lin’s concordance (0.93), between measured and predicted ECe and at various depths, good predictions of ECe distribution were made at all the four depths of interest. However the predictions made in the topsoil (0-0.3 m) at a few locations were poor due to limited data availability in areas where ECa changed rapidly from low to high values. Also, equivalent results can be achieved using smaller combinations of ECa data (i.e. DAULEM-1, DUALEM-2) although with some loss in precision, bias and concordance. In both the scenarios, improvements in prediction can be achieved by collection of ECa in more closely spaced transects, particularly in areas where ECa varies rapidly over short spatial scales

Delineation of Nitrate Reduction Hotspots in Artificially Drained Areas through Assessment of Small-Scale Spatial Variability of Electrical Conductivity Data

Identification of nitrate reduction hotspots (NRH) can be instrumental in implementing targeted strategies for reducing nitrate loading from agriculture. In this study, we aimed to delineate possible NRH areas from soil depths of 80 to 180 cm in an artificially drained catchment by utilizing electrical conductivity (EC) values derived by the inversion of apparent electrical conductivity data measured by an electromagnetic induction instrument. The NRH areas were derived from the subzones generated from clustering the EC values via two methods, unsupervised ISODATA clustering and the Optimized Hot Spot Analysis, that highly complement each other. The clustering of EC values generated three classes, wherein the classes with high EC values correspond to NRH areas as indicated by their low redox potential values and nitrate (NO3−) concentrations. Nitrate concentrations in the NRH were equal to 13 to 17% of the concentrations in non-NRH areas and occupied 26% of the total area of the drainage catchments in the study. It is likely that, with the identification of NRH areas, the degree of nitrogen reduction in the vadose zone may be higher than initially estimated at the subcatchment scale

Mapping peat depth using a portable gamma-ray sensor and terrain attributes

Pristine peatlands being excellent storage for terrestrial Carbon (C) play a crucial role in regulating climate and water and provide several important ecosystem services. However, peatlands have been heavily altered (e.g., by draining the water table), increasing greenhouse gas (GHG) emissions. Restoring peatlands requires a comprehensive characterization, including knowledge of peat depth (PD; m). Traditionally, this requires the physical insertion of a push probe, which is time-consuming and labor-intensive. It has been shown that non-invasive proximal sensing techniques such as electromagnetic induction and ground penetrating radar can add value to PD data. In this research, we want to assess the potential of proximally sensed gamma-ray (γ-ray) spectrometry (i.e., potassium [K], thorium [Th], uranium [U], and the count rate [CR]) and terrain attributes data (i.e., elevation, slope, SAGAWI, and MRVBF) to map PD either alone or in combination across a small (10 ha) peatland area in ØBakker, Denmark. Here, the PD varies from 0.1 m in the south to 7.3 m in the north. We use various prediction models including ordinary kriging (OK) of PD, linear regression (LR), multiple LR (MLR), LR kriging (LRK), MLR kriging (MLRK) and empirical Bayesian kriging regression (EBKR). We also determine the minimum calibration sample size required by decreasing sample size in decrements (i.e., n = 100, 90, 80,…, 30). We compare these approaches using prediction agreement (Lin’s concordance correlation coefficient; LCCC) and accuracy (root mean square error; RMSE). The results show that OK using maximum calibration size (n = 108) had near perfect agreement (0.97) and accuracy (0.59 m), compared to LR (ln CR; 0.65 and 0.78 m, respectively) and MLR (ln K, Th, CR and elevation; 0.85 and 0.63 m). Improvements are achieved by adding residuals; LRK (0.95 and 0.71 m) and MLRK (0.96 and 0.51 m). The best results were obtained using EBKR (0.97 and 0.63 m) given all predictions were positive and no significant change in agreement and standard errors with the decrement of calibration sample size (e.g., n = 30). The results have implications towards C stocks assessment and improved land use planning to control GHG emissions and slow down global warming

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Not AvailableTo generate baseline data for the purpose of monitoring the efficacy of remediation of a degraded landscape, we demonstrate a method for 3‐dimensional mapping of electrical conductivity of saturated soil paste extract (ECe) across a study field in central Haryana, India. This is achieved by establishing a linear relationship between calculated true electrical conductivity (σ) and laboratory measured ECe at various depths (0–0.3, 0.3–0.6, 0.6–0.9, and 0.9–1.2 m). We estimate σ by inverting DUALEM‐21S apparent electrical conductivity (ECa) data using a quasi‐3‐dimensional inversion algorithm (EM4Soil‐V302). The best linear relationship (ECe = −11.814 + 0.043 × σ) was achieved using full solution (FS), S1 inversion algorithm, and a damping factor (λ) of 0.6 that had a large coefficient of determination (R2 = 0.84). A cross‐validation technique was used to validate the model, and given the high accuracy (RMSE = 8.31 dS m−1), small bias (mean error = −0.0628 dS m−1), large R2 = 0.82, and Lin's concordance (0.93), between measured and predicted ECe, we were well able to predict the ECe distribution at all the four depths. However, the predictions made in the topsoil (0– 0.3 m) at a few locations were poor due to limited data availability in areas where ECa changed rapidly. In this regard, improvements in prediction can be achieved by collection of ECa in more closely spaced transects, particularly in areas where ECa varies over short spatial scales. Also, equivalent results can be achieved using smaller combinations of ECa data (i.e., DAULEM‐1S, DUALEM‐2S), although with some loss in precision, bias, and concordance

Mapping of Agricultural Subsurface Drainage Systems Using Unmanned Aerial Vehicle Imagery and Ground Penetrating Radar

Agricultural subsurface drainage systems are commonly installed on farmland to remove the excess water from poorly drained soils. Conventional methods for drainage mapping such as tile probes and trenching equipment are laborious, cause pipe damage, and are often inefficient to apply at large spatial scales. Knowledge of locations of an existing drainage network is crucial to understand the increased leaching and offsite release of drainage discharge and to retrofit the new drain lines within the existing drainage system. Recent technological developments in non-destructive techniques might provide a potential alternative solution. The objective of this study was to determine the suitability of unmanned aerial vehicle (UAV) imagery collected using three different cameras (visible-color, multispectral, and thermal infrared) and ground penetrating radar (GPR) for subsurface drainage mapping. Both the techniques are complementary in terms of their usage, applicability, and the properties they measure and were applied at four different sites in the Midwest USA. At Site-1, both the UAV imagery and GPR were equally successful across the entire field, while at Site-2, the UAV imagery was successful in one section of the field, and GPR proved to be useful in the other section where the UAV imagery failed to capture the drainage pipes’ location. At Site-3, less to no success was observed in finding the drain lines using UAV imagery captured on bare ground conditions, whereas good success was achieved using GPR. Conversely, at Site-4, the UAV imagery was successful and GPR failed to capture the drainage pipes’ location. Although UAV imagery seems to be an attractive solution for mapping agricultural subsurface drainage systems as it is cost-effective and can cover large field areas, the results suggest the usefulness of GPR to complement the former as both a mapping and validation technique. Hence, this case study compares and contrasts the suitability of both the methods, provides guidance on the optimal survey timing, and recommends their combined usage given both the technologies are available to deploy for drainage mapping purposes