Noninvasive 2D and 3D Mapping of Root Zone Soil Moisture Through the Detection of Coarse Roots With Ground‐Penetrating Radar

Abstract

Root zone soil moisture (RZSM) is important in sustaining terrestrial ecosystems and water cycling. However, noninvasive mapping of RZSM remains challenging in the field, especially its spatial variability at local scales. We propose a novel method of applying ground-penetrating radar (GPR) for noninvasive determination of RZSM. First, this method identified coarse root reflections in GPR images to obtain the reflected wave velocity and soil water storage at various locations. Then, the horizontal distributions of soil water storage to different depths were reconstructed using the inverse distance weighted interpolation. The difference in soil water storage between two depths was converted into 2D RZSM distribution for a specific depth interval. Finally, 2D RZSM distributions from a range of depths were combined to produce a 3D visualization of RZSM. The proposed method was validated in two field plots (30 m by 30 m) in shrublands. The comparisons with 2D RZSM distributions from soil cores sampled at 0.2-m depth intervals suggested a reasonable correspondence in both spatial patterns and absolute values, with the average root-mean-square error less than 0.017 m 3 ·m −3 and correlation coefficients ranging from 0.502 to 0.798 in both plots. Furthermore, 3D visualizations of RZSM were generated based on GPR estimates to demonstrate the spatial variability of soil moisture at the study scale. The proposed method enhances noninvasive characterization of soil moisture down to the root zone, which contributes to a better understanding of the local-scale variability of soil moisture in the subsoil as well as the ecohydrological processes in the soil-plant-atmosphere continuum.

Machine learning applications in vadose zone hydrology: A review

“…Mapping RZSM is another RF application (Liu et al, 2020) where spatially varying RZSM is constructed from spatially distributed RZSM measurements. With the advent of an automatic ML implementation framework (Cai, 2020), Babaeian et al (2021) applied RF to generate high-spatial-resolution RZSM maps that capture the spatial variability of SM from physical and hydraulic soil properties and unmanned aerial system observations.…”

Linking horizontal crosshole GPR variability with root image information for maize crops

“…In contrast, using crosshole GPR allows a direct link from the EM wave velocity to ε r (for more details, see Klotzsche et al, 2018). In recent years, surface GPR has been used to investigate the soil-plant continuum with large root systems such as tree roots (reviewed in L. Guo et al, 2013 andRodríguez-Robles et al, 2017) and shrub roots (e.g., Cui et al, 2021;Liu et al, 2020Liu et al, , 2019Parsekian et al, 2012). Although some studies investigated the possibility of estimating agricultural root systems (Delgado et al, 2017;Klotzsche, Lärm, et al, 2019;Liu et al, 2017;Wijewardana & Galagedara, 2010;Galagedara et al, 2002Galagedara et al, , 2003, the detection and mapping of finer root systems found in crops are still challenging using surface GPR.…”

Responses of soil water dynamics to precipitation events in an alpine meadow ecosystem of the Qinghai Lake Basin based on high‐precision lysimeter measurements

“…Moreover, the characteristics of soil water change are not only controlled by internal factors such as soil texture and soil structure but also associated with external factors such as vegetation cover (Gao et al, 2020;Liu et al, 2020). Alpine meadow is one of the most important alpine ecosystem types on the Qinghai-Tibetan Plateau, covering approximately 700 000 km 2 .…”

“…Hence, observations of lysimeters in cold areas with freeze–thaw cycles, such as the Qinghai‐Tibet Plateau, will provide scientific data for soil water dynamics and their relationship with precipitation. Moreover, the characteristics of soil water change are not only controlled by internal factors such as soil texture and soil structure but also associated with external factors such as vegetation cover (Gao et al, 2020; Liu et al, 2020). Alpine meadow is one of the most important alpine ecosystem types on the Qinghai‐Tibetan Plateau, covering approximately 700 000 km 2 .…”