Rights statement: Accepted for publication in Water Resources Research. Copyright 2023 American Geophysical Union. Further reproduction or electronic distribution is not permitted
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Estimation of surface soil moisture by a multi-elevation UAV-based ground penetrating radar. / Cheng, Qinbo; Su, Qiuju; Binley, Andrew et al.
In: Water Resources Research, Vol. 59, No. 2, e2022WR032621, 28.02.2023.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Estimation of surface soil moisture by a multi-elevation UAV-based ground penetrating radar
AU - Cheng, Qinbo
AU - Su, Qiuju
AU - Binley, Andrew
AU - Liu, Jintao
AU - Zhang, Zhicai
AU - Chen, Xi
N1 - Accepted for publication in Water Resources Research. Copyright 2023 American Geophysical Union. Further reproduction or electronic distribution is not permitted
PY - 2023/2/28
Y1 - 2023/2/28
N2 - The measurement of soil moisture is important for a wide range of applications, including ecosystem conservation and agricultural management. However, most traditional measurement methods, e.g., time-domain reflectometry (TDR), are unsuitable for mapping field scale variability. In this study, we propose a method that uses an unmanned aerial vehicle (UAV) to support a ground penetrating radar (GPR) system for spatial scanning investigation at different elevations above ground level. This method measures the surface reflectivity to estimate the soil moisture, exploiting the linear relationship between the ratio of the reflected and the direct wave amplitudes along with the reciprocal of GPR antenna height. This relationship is deduced in this study based on the point source assumptions of a transmitter antenna and ground reflections, which is confirmed by numerical simulation results using the gprMax software. Unlike previous air-launched GPR methods, the UAV-GPR method presented here removes the limitations of a steady transmitter power and a fixed GPR survey height and the need for calibration of antenna transfer functions and geophysical inversion calculations, and thus is simpler and more convenient for field applications. We test the method at field sites within the riparian zone and a river-island grassland adjacent to the Yangtze River. The results from the field study illustrate comparable measured soil moisture to those obtained invasively using TDR. The root mean square error (RMSE) of surface reflectivity and soil moisture values between UAV-GPR with 8 antenna height investigations and TDR in the grassland are 0.03 and 0.05 cm3/cm3, respectively.
AB - The measurement of soil moisture is important for a wide range of applications, including ecosystem conservation and agricultural management. However, most traditional measurement methods, e.g., time-domain reflectometry (TDR), are unsuitable for mapping field scale variability. In this study, we propose a method that uses an unmanned aerial vehicle (UAV) to support a ground penetrating radar (GPR) system for spatial scanning investigation at different elevations above ground level. This method measures the surface reflectivity to estimate the soil moisture, exploiting the linear relationship between the ratio of the reflected and the direct wave amplitudes along with the reciprocal of GPR antenna height. This relationship is deduced in this study based on the point source assumptions of a transmitter antenna and ground reflections, which is confirmed by numerical simulation results using the gprMax software. Unlike previous air-launched GPR methods, the UAV-GPR method presented here removes the limitations of a steady transmitter power and a fixed GPR survey height and the need for calibration of antenna transfer functions and geophysical inversion calculations, and thus is simpler and more convenient for field applications. We test the method at field sites within the riparian zone and a river-island grassland adjacent to the Yangtze River. The results from the field study illustrate comparable measured soil moisture to those obtained invasively using TDR. The root mean square error (RMSE) of surface reflectivity and soil moisture values between UAV-GPR with 8 antenna height investigations and TDR in the grassland are 0.03 and 0.05 cm3/cm3, respectively.
KW - UAV
KW - GPR
KW - reflectivity
KW - permittivity
KW - soil moisture
U2 - 10.1029/2022WR032621
DO - 10.1029/2022WR032621
M3 - Journal article
VL - 59
JO - Water Resources Research
JF - Water Resources Research
SN - 0043-1397
IS - 2
M1 - e2022WR032621
ER -