# Evaluating Environmental and Crop Factors Affecting Drone-Mounted GPR Performance in Agricultural Fields

## TL;DR

This paper studies how environmental and crop factors affect the performance of drone-mounted ground-penetrating radar in agricultural fields.

## Contribution

The study introduces a framework for evaluating how flight and surface conditions influence GPR signal quality in agricultural settings.

## Key findings

- Lower flight altitudes and smoother surfaces improve GPR signal coherence and subsurface penetration.
- Higher canopy water content and biomass reduce signal strength and clarity.
- Combining GPR with spectral and thermal data helps distinguish soil and vegetation effects.

## Abstract

Drone-mounted ground-penetrating radar (GPR) systems offer new opportunities for integrating subsurface characterization into remote sensing workflows. However, the interaction between flight parameters, surface conditions, and vegetation characteristics remains poorly understood. This study investigates the impact of flight altitude, surface topography, crop presence, and canopy water content on the stability and interpretability of GPR signals collected using a drone. Field experiments were conducted under controlled conditions using agricultural plots with variable canopy cover and soil moisture regimes. Radargrams were processed to evaluate signal amplitude, reflection continuity, and attenuation patterns in relation to terrain slope and vegetation structure derived from co-registered RGB drone imagery. The results reveal that lower flight altitudes and smoother surfaces yield higher signal coherence and greater subsurface penetration, while increased canopy water content and biomass reduce signal strength and clarity. Integrating drone-based GPR observations with surface spectral and thermal data improved discrimination between soil and vegetation-induced signal distortions. The findings highlight the potential of drone–GPR systems as a complementary layer in a multi-sensor remote sensing framework for precision agriculture, environmental monitoring, and 3D soil mapping.