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

Journal Article · Thu Mar 25 00:00:00 UTC 2021 · IEEE Transactions on Geoscience and Remote Sensing

DOI: https://doi.org/10.1109/tgrs.2021.3065066 · OSTI ID:1842790

Liu, Xinbo  ^[1]; Guo, Li  ^[2]; Cui, Xihong ^[1]; Butnor, John R.  ^[3]; Boyer, Elizabeth W.  ^[4]; Yang, Dedi  ^[5]; Chen, Jin ^[1]; Fan, Bihang ^[2]

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1. Beijing Normal University (China)
2. Sichuan University, Chengdu (China)
3. USDA Forest Service, Burlington, VT (United States)
4. Pennsylvania State Univ., University Park, PA (United States)
5. Brookhaven National Lab. (BNL), Upton, NY (United States); Stony Brook Univ., NY (United States)

This report concerns Root water content (RWC) as 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 [root-mean-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.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States)
• Sponsoring Organization: National Natural Science Foundation of China; USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: SC0012704
• OSTI ID: 1842790
• Report Number(s): BNL--222645-2022-JAAM
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Vol. 60; ISSN 0196-2892
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English