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Better methods are needed for mapping agricultural drainage pipe systems. Prior research on small test plots indicates that ground penetrating radar (GPR) is oftentimes capable of detecting buried drainage pipes; however, the feasibility of employing this geophysical technique in larger field areas has not been adequately evaluated. Ground penetrating radar integrated with a Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) may be an effective and efficient means of mapping drain lines within agricultural fields. Therefore, GPR-RTK/GNSS was tested in three agricultural settings; with Site 1 and Site 2 located in Beltsville, MD, USA and Site 3 near Columbus, OH, USA. Soils at the three sites ranged from silty clay loam to loamy sand. A GPR unit with 250 MHz antennas was used to detect drainage pipes, and at Sites 1 and 2, a physical GNSS base station was utilized, while a virtual base station was employed at Site 3. The GPR-RTK/GNSS configurations used in this study delineated a complex rectangular drainage pipe system at Site 1, with one set of drainage pipes oriented southwest-northeast and a second oriented southeast-northwest. At Site 2, a herringbone drain line pattern was outlined, and at Site 3, random drain lines were found. When integrated with RTK/GNSS, spiral or serpentine GPR transects (or spiral/serpentine segments of a GPR transects) were utilized to provide insight on drain line directional trends. Consequently, given suitable field conditions, GPR integrated with RTK/GNSS can be a valuable tool for farmers and drainage contractors needing to map subsurface drainage systems.

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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.

Information on the spatial distribution of water content across the sand layer component of a golf course green can be important to golf course superintendents for evaluating drainage effectiveness and scheduling irrigation. To estimate the bulk volumetric water content of the sand layer at point locations across the green, a technique was developed that combined (1) depth (or thickness) of the sand layer measured with a steel shaft tile probe, (2) radar signal two-way travel time from the base of the sand layer obtained using a ground-penetrating radar (GPR) system with 900 MHz antennas, and (3) an empirical equation relating porous media dielectric constant to water content. To test this technique, two GPR surveys were conducted on the Nursery Green at the Double Eagle Golf Club near Galena, Ohio, and two additional GPR surveys were carried out on the 9th Hole Green at the Delaware Golf Club near Delaware, Ohio. For comparison, time-domain reflectometry (TDR) water content values for the sand layer near the ground surface were obtained concurrent with each of the four GPR surveys. Results of the four golf course green GPR/TDR surveys carried out on September 8 and 9, 2014 (Double Eagle Golf Club - before and after irrigation, respectively), and April 21 and 29, 2015 (Delaware Golf Club) show that the sand layer water contents determined with GPR respectively averaged, 18.8%, 25.2%, 12.2%, and 11.3%, which were quite similar to the respective TDR sand layer water content averages of 20.3%, 25.7%, 11.0%, and 14.1%. The spatial correlation coefficients (r) between the GPR-based sand layer water content values versus the TDR sand layer water content values for these four GPR/TDR surveys were 0.76 (September 8, 2014), 0.73 (September 9, 2014), 0.55 (April 21, 2015), and 0.70 (April 29, 2015). Sand layer water content was found to have moderate inverse spatial correlation with ground surface elevation (r = −0.44 to −0.56) and elevation at the base of the sand layer (r = −0.43 to −0.53). Consequently, the findings of this study clearly indicate that if sand layer depth values are available, then GPR can be utilized in a non-destructive manner to accurately map sand layer water content across a golf course green, and conversely, in cases where sand layer water content (hence, radar velocity) spatial patterns are already known, then this information can be employed to provide more accurate GPR-based sand layer depth values.