Non-invasive estimation of root zone soil moisture from coarse root reflections in ground-penetrating radar images

  • Xinbo Liu
  • Xihong CuiEmail author
  • Li Guo
  • Jin Chen
  • Wentao Li
  • Dedi Yang
  • Xin Cao
  • Xuehong Chen
  • Qixin Liu
  • Henry Lin
Methods Paper


Background and aims

Root zone soil moisture is an important component in water cycling through the soil-plant-atmosphere continuum. However, its measurement in the field remains a challenge, especially non-invasively and repeatedly. Here, we developed a new method that uses ground-penetrating radar (GPR) to quantify root zone soil moisture.


Coarse roots were chosen as reflectors to collect GPR radargrams. An automatic hyperbola detection algorithm identified coarse root reflections in GPR radargrams and determined the velocity of GPR wave, which then was used to calculate the average soil water content of a soil profile (ASWC) and soil water content in a depth interval (ISWC). In total, GPR reflection data of 55 root samples from three computer simulation scenarios and two field experiments in sandy shrubland, one burying roots at known depths and the other under the undisturbed condition, were used to evaluate the proposed method.


Both the simulated and the field collected data demonstrated the effectiveness of the proposed method for measuring root zone soil moisture with high accuracy. Even in the two field experiments, the root-mean-square errors of the estimated ASWC and ISWC relative to measurements from soil cores were as low as 0.003 and 0.012 m3·m−3, respectively.


The proposed method offers a new way of quantifying root zone soil moisture non-invasively that allows repeated measurements. This study expands the application of GPR in root and soil study and enhances our ability to monitor plant-soil-water interactions.


Ecohydrology Near-surface geophysics Plant-soil-water interactions Sandy soil Soil water content Subsurface imaging 



ground-penetrating radar


average soil water content of a soil profile


soil water content of a depth interval


region of interest


root-mean-square error



This study was supported by the National Natural Science Foundation of China (Grant No. 41571404) on project of State Key Laboratory of Earth Surface Processes and Resource Ecology.

Supplementary material

11104_2018_3919_MOESM1_ESM.docx (129 kb)
ESM 1 (DOCX 128 kb)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xinbo Liu
    • 1
    • 2
  • Xihong Cui
    • 1
    • 2
  • Li Guo
    • 3
  • Jin Chen
    • 1
    • 2
  • Wentao Li
    • 1
    • 2
  • Dedi Yang
    • 4
  • Xin Cao
    • 1
    • 2
  • Xuehong Chen
    • 1
    • 2
  • Qixin Liu
    • 1
    • 2
  • Henry Lin
    • 3
  1. 1.State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  2. 2.Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  3. 3.Department of Ecosystem Science and ManagementThe Pennsylvania State UniversityState CollegeUSA
  4. 4.Department of Ecology and EvolutionStony Brook UniversityStony BrookUSA

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