Lunar Reconnaissance Orbiter (LRO) was launched in 2009 to study and map the Moon and is now completing its fifth extended science mission. The LRO (see Figure 1) hosts a payload of seven different scientific instruments. The Cosmic Ray Telescope for the Effects of Radiation instrument has characterized the lunar radiation environment and allowed scientists to determine potential impacts to astronauts and other life. The Diviner Lunar Radiometer Experiment (DLRE) has identified cold traps where ice could reside and mapped global thermophysical and mineralogical properties by measuring surface and subsurface temperatures. The Lyman Alpha Mapping Project has found evidence of exposed ice in south polar cold traps as well as global diurnal variations in hydration. The Lunar Exploration Neutron Detector has been used to create high-resolution maps of lunar hydrogen distribution and gather information about the neutron component of the lunar radiation environment. The Lunar Reconnaissance Orbiter Camera (LROC) is a system of three cameras [one wide-angle camera and two narrow-angle cameras (NACs)] mounted on the LRO that capture high-resolution black-and-white images and moderate resolution multispectral (seven-color band) images of the lunar surface. These images can be used, for example, to learn new details about the history of lunar volcanism or the present-day flux of impactors. The Miniature Radio Frequency (Mini-RF) instrument is an advanced synthetic aperture radar (SAR) that can probe surface and subsurface coherent rock contents to identify the polarization signature of ice in cold traps. The Lunar Orbiter Laser Altimeter (LOLA) has been used to generate a high-resolution, 3D map of the Moon that serves as the most accurate geodetic framework available for co-locating LRO (and other lunar) data. The data produced by the LRO continue to revolutionize our scientific understanding of the Moon, and are essential to planning NASA's future human and robotic lunar missions.

Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructure, leading to significant damage. Therefore, there is a pressing need to investigate the subsurface tree root system architecture (RSA). Ground-penetrating radar (GPR) has emerged as a powerful tool for this purpose, offering high-resolution and nondestructive testing (NDT) capabilities. One of the primary challenges in enhancing GPR's ability to detect roots lies in accurately reconstructing the 3-D structure of complex RSAs. This challenge is exacerbated by subsurface heterogeneity and intricate interlacement of root branches, which can result in erroneous stacking of 2-D root points during 3-D reconstruction. This study introduces a novel approach using our developed wheel-based dual-polarized GPR system capable of capturing four polarimetric scattering parameters at each scan point through automated zigzag movements. A dedicated radar signal processing framework analyzes these dual-polarized signals to extract essential root parameters. These parameters are then used in an optimized slice relation clustering (OSRC) algorithm, specifically designed for improving the reconstruction of complex RSA. The efficacy of integrating root parameters derived from dual-polarized GPR signals into the OSRC algorithm is initially evaluated through simulations to assess its capability in RSA reconstruction. Subsequently, the GPR system and processing methodology are validated under real-world conditions using natural Angsana tree root systems. The findings demonstrate a promising methodology for enhancing the accurate reconstruction of intricate 3-D tree RSA structures.