Recent advancements in the uncrewed aerial vehicles (UAVs) as remote system platforms have enabled low-cost and easy-to-use options for various applications. An example of such a remote sensing system is a software-defined radar (SDRadar) mounted on a UAV. There is a need to accurately calibrate the radar system with low-cost and field-ready schemes to utilize the full potential of the radar system. Our system is an SDRadar mounted on a small UAV. The waveform of the radar has a 1-GHz bandwidth with a center frequency set to 750 MHz. The calibration employs a 59-cm-diameter disk elevated above the ground as an external passive target. Our results demonstrate a calibration accuracy of approximately 1 dB across most of the transmitted band, in both laboratory and field conditions. However, calibration challenges persist in the lowest parts of the frequency range, which are affected by the high side lobes and grating lobes in the antenna pattern. This calibration process is adaptable to various bandwidths and center frequencies.

Recent advancements in uncrewed aerial vehicles (UAVs) allow them to be cost-effective and easy-to-use platforms for remote sensing. The advancements in software-defined radar systems allow the use of ultra-wideband waveforms and mounting them in a hexacopter UAV. Radar cross section calibration for radar systems is essential to retrieve various geophysical variables and explore the full potential of these radar systems. In our study, we utilized a disk as an external calibration target, achieving a standard deviation ranging from 0.5 dB to 1 dB across most of our operational frequency range. Further characterizations of the system are needed to fully understand certain frequency bands and to assist the full performance potential of the calibration.

Surface-to-depth profiles of soil moisture are among the most important Earth system variables due to their strong influence on the partitioning of the water cycle [1] . Yet, our directly observed knowledge of these profiles, sometimes also referred to as root-zone soil moisture (RZSM) profiles, is quite limited. Soil moisture often exhibits rapid spatial and temporal dynamics and can change significantly over a short time. For example, during the monsoon season in the US Southwest, there may be significant and sudden precipitation over localized cells.

A ground-based distributed sensing network is described in this work that leverages elements of wireless sensor networks (WSN) and uncrewed areal vehicles (UAVs) with software-defined radar payloads. Hardware and software advancements are made towards combining the operations of WSNs and UAVs for dynamic spatiotemporal monitoring of surface to subsurface soil moisture at kilometer scales. The multi-agent and distributed sensing approach demonstrates coordination, collaboration, and parallel operation of discrete assets for optimal soil moisture monitoring. Results from the first field experiment showing this coordinated operation are reported.

This paper aims to retrieve the soil moisture profile above the water table using ground penetrating radar. For the forward model, the Van Genuchten soil saturation model and the generalized refractive mixing dielectric model are used to parameterize the soil saturation profile. The finite-difference time-domain method was used to simulate the electromagnetic signal. The soil moisture profile retrieval was carried out using a look-up table. We demonstrate successful retrieval of soil moisture profile in the presence of noise.