An Automatic Processing Framework for In Situ Determination of Ecohydrological Root Water Content by Ground-Penetrating Radar

  • Authors: Liu, Xinbo; Guo, Li; Cui, Xihong; Butnor, John R.; Boyer, Elizabeth W.; Yang, Dedi; Chen, Jin; Fan, Bihang
  • Publication Year: 2021
  • Publication Series: Scientific Journal (JRNL)
  • Source: IEEE Transactions on Geoscience and Remote Sensing
  • DOI: 10.1109/TGRS.2021.3065066

Abstract

Root water content (RWC) is a vital component in water flux in soil–plant–atmosphere continuum. Knowledge
of RWC helps to better understand the root function and the soil–root interaction and improves water cycle modeling.
However, due to the lack of appropriate methods, field monitoring of RWC is seriously constrained. In this study,
we used ground-penetrating radar (GPR), a common geophysical technique, to characterize RWC of coarse roots noninvasively.
An automatic GPR data processing framework was proposed to (1) identify hyperbolic root reflections and locate roots in GPR
images and (2) extract waveform parameters from the reflected wave of identified roots. These waveform parameters were then
used to establish an empirical model and a semiempirical model to determine RWC. We validated the developed models using
GPR root data at three antenna center frequencies (500 MHz, 900 MHz, and 2 GHz) that were produced from simulation
experiments (with RWC ranging from 70% to 150%) and field experiments in sandy soils (with RWC ranging from 66%
to 144%). Our results show that both the empirical and the semiempirical models achieved a good performance in estimating
RWC with similar accuracy, i.e., the prediction error [rootmean-square error (RMSE)] was less than 8% for the simulation
data and 12% for the field data. For both models, the accuracy of RWC estimation was the highest when applied to 2-GHz data.
This study renders a new opportunity to determine RWC under field conditions that enhances the application of GPR for root
study and the understanding and modeling of ecohydrology in the rhizosphere.

  • Citation: Liu, Xinbo; Guo, Li; Cui, Xihong; Butnor, John R.; Boyer, Elizabeth W.; Yang, Dedi; Chen, Jin; Fan, Bihang. 2021. An Automatic Processing Framework for In Situ Determination of Ecohydrological Root Water Content by Ground-Penetrating Radar. IEEE Transactions on Geoscience and Remote Sensing. : 1-15. https://doi.org/10.1109/TGRS.2021.3065066.
  • Keywords: Geophysics, ground-penetrating radar, GPR, model fitting, noninvasive, root ecology, waveform parameters,
  • Posted Date: March 30, 2021
  • Modified Date: March 30, 2021
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