Combined Measurement of Soil Permittivity and Electrical Conductivity Using UAV‐Based Ground Penetrating Radar
Year: 2025
Type: article
Abstract: Measurements of soil water content and salinity are important for a wide range of topics, in particular those concerned with soil and plant health, and specific aspects of agricultural management. However, most traditional methods are unsuitable for simultaneously mapping the field scale variability of soil electrical properties. In this study, we propose a method that uses an unmanned aerial vehicle (UAV) to support ground penetrating radar (GPR) antennae with different frequencies, allowing spatial scanning of surface reflection coefficients, which is then used to estimate the soil relative permittivity ( ε r ) and electrical conductivity ( σ ). These parameters are then used to estimate soil water content and salinity using empirical transfer functions. Unlike other published approaches, the proposed method is relatively simple and does not rely on full‐waveform inversion. Field tests in the riparian zone of the Yangtze River and salinized land close to the Yellow Sea are used to demonstrate the effectiveness of the method. The surveys illustrate that the UAV‐GPR give results comparable to those measured in situ with a soil electrical property meter. These findings are supported by accuracy analysis using Monte Carlo simulation which reveal that the measurement error of ε r increases with σ , and the relative errors in σ measurements are generally less than those of ε r except in areas of high ε r and low σ . The study provides an approach for mapping soil electrical properties using UAV technology, thus opening up the possibility of remote sensing of spatial variability of these important properties at high spatial resolution.
Source: Water Resources Research
DOI: 10.1029/2024wr039519
Authors: Qinbo Cheng, Yu Cheng, Zhijin Ma, Andrew Binley, Jintao Liu, Zhicai Zhang, Feng Huang, Xi Chen
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W4414619274
