The objectives of this study were to detect coarse tree root and to estimate root biomass in the field by using an advanced 3D Ground Penetrating Radar (3D GPR) system. This study obtained full-resolution 3D imaging results of tree root system using 500 MHz and 800 MHz bow-tie antennas, respectively. The measurement site included two larch trees, and one of them was excavated after GPR measurements. In this paper, a searching algorithm, based on the continuity of pixel intensity along the root in 3D space, is proposed, and two coarse roots whose diameters are more than 5 cm were detected and delineated correctly. Based on the detection results and the measured root biomass, a linear regression model is proposed to estimate the total root biomass in different depth ranges, and the total error was less than 10%. Additionally, based on the detected root samples, a new index named “magnitude width” is proposed to estimate the root diameter that has good correlation with root diameter compared with other common GPR indexes. This index also provides direct measurement of the root diameter with 13%–16% error, providing reasonable and practical root diameter estimation especially in the field.

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR) data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP) processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE) on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR) soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I²EM) based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm.

Two design methods of a filter in a frequency-spatial frequency ( f - k ) domain have been developed for bistatic ground penetrating radar. The proposed methods suppress the direct wave, which causes significant artifacts in radar images, and are evaluated by laboratory measurements. Because the geometric positions of a transmitting antenna and a receiving antenna are not fixed, the suppression of a direct wave is an important issue. Then, we propose an f - k filtering approach for the solution, and present two methods to design the f - k filter. Both methods use a difference of an apparent horizontal velocity between a direct wave and a reflection from a target, and work automatically from position information of a transmitting antenna and a receiving antenna. One method is to mask an f - k spectrum in a region where a spectrum of the direct wave is distributed. The region is defined from the maximum and the minimum apparent horizontal velocity of the direct wave, which are calculated from the location of the transmitting antenna and the scanning area of the receiver. As for the other method, the most essential point is applying a time shift to eliminate a difference of an arrival time of a direct wave, where the time shift is calculated beforehand from the location of the transmitting antenna and the receiving antenna. Then, an apparent horizontal velocity of the direct wave becomes infinitely large due to the time shift. Thus, the f - k spectrum of the direct wave concentrates around a frequency axis because its slope is infinitely large. Then, a filter to reject the dc component in the spatial frequency direction is applied. Both methods are applied to an experimental data set which is acquired by a bistatic radar measurement to detect a buried landmine model with a depth of 10 cm. In addition, it is confirmed that they can suppress the undesired fluctuation of the images nearly one-tenth and help the reliable detection of a buried object.

We designed a ground-based radar system with a C-band 2-D cross multiple input multiple output (MIMO) array for 3-D imaging and displacement estimation purposes. For this system, we developed a far-field pseudo-polar image format algorithm using pseudo-polar spherical coordinate. The use of a tensor compressive sensing technique allows to focus under-sampled raw data and to optimize the data acquisition time and memory usage. A novel algorithm, named as tensor-based iterative adaptive approach, is proposed for the effective and efficient reconstruction of sparse targets with a reduced level of sidelobes. Experimental results validate the designed radar system and the proposed algorithms.