Mapping of agricultural subsurface drainage systems using unmanned aerial vehicle imagery and ground penetrating radar

Authors: KOGANTI, TRIVEN - Aarhus University; GHANE, EHSAN - Michigan State University; Martinez, Luis; IVERSEN, BO - Aarhus University; Allred, Barry

Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/13/2021
Publication Date: 4/15/2021
Citation: Koganti, T., Ghane, E., Martinez, L.R., Iversen, B.V., Allred, B.J. 2021. Mapping of agricultural subsurface drainage systems using unmanned aerial vehicle imagery and ground penetrating radar. Sensors. 21(8). Article 2800. https://doi.org/10.3390/s21082800.
DOI: https://doi.org/10.3390/s21082800

Interpretive Summary: Due to economic and environmental concerns, there is a need for better methods to locate farm field drainage pipes. Unmanned aerial vehicle (UAV) imagery has proved to be a feasible and cost-effective solution for mapping agricultural subsurface drainage systems, because large agricultural areas can be surveyed in a short time. However, its inability to find the linear patterns (e.g., drainage pipes) under certain circumstances and lack of distinction with other similar linear features (e.g., harvest or tillage tracks) remain caveats for this technique. While ground penetrating radar (GPR) was proven effective in previous studies, the technique can be inefficient and cost-intensive to cover large farm field areas and has limited applicability in soils having high electrical conductivity. Thereby, given these constraints, when used appropriately, collecting GPR data along a limited spatial extent in combination with the UAV imagery not only provided depth information of the drainage pipes, but was also useful for setting apart the linear signatures caused by drain lines from those due to field operations. At the study sites visited, where the UAV imagery was unsuccessful or only partly successful, a few parallel GPR transects along the edge and in the center of the field, randomly fashioned transects, and the use of spiral and serpentine transects all assisted in mapping drain line locations, determine their orientation, and provide insight on the drainage network pattern, at least to a certain extent. Therefore, GPR provided complementary information and proved suitable both as a mapping and validation technique. Hence, the use of UAV imagery in combination with GPR across limited transects proved to be the most optimal approach for mapping agricultural subsurface drainage systems.

Technical Abstract: Agricultural subsurface drainage systems are commonly installed on farmland to remove the excess water from poorly drained soils. Conventional methods for drainage mapping like tile probes and trenching equipment are laborious, cause pipe damage, and are often inefficient to apply at large spatial scales. Knowledge of locations for an existing drainage network is crucial to assessing the potential for increased leaching and offsite release of nitrate/phosphate and to retrofit the new drain lines within the existing drainage system for water removal efficiency improvement. Recent technological developments in non-destructive techniques provide a potential solution. This study explored 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 these techniques are complementary in terms of their usage, applicability, and the properties they measured. In this investigation, both UAV and GPR surveys were conducted at four different sites in the Midwest U.S.A. 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, 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 the 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 UAV imagery as both a mapping and validation technique.