Current satellite-based active microwave observations lack the temporal resolution needed to accurately capture rapid Earth system dynamics such as soil–plant–atmosphere interactions, rainfall interception, snowfall and rain-on-snow events. Ground-based radar systems can resolve these processes but typically rely on high-end VNAs, limiting their affordability and deployment scale. This work presents a low-cost SFCW radar system built around a compact, SDR-based VNA with an enhanced RF front end supported by remote-access firmware and a cloud-based back end with automatic backup. Calibration experiments and preliminary measurements demonstrate that the system achieves stable performance and is capable of capturing high-temporal-resolution microwave signatures relevant for climate monitoring.

Full‐waveform inverse modelling of proximal ground‐penetrating radar was used to measure soil surface water content in irrigated areas of southern Tunisia. The ground‐penetrating radar system consisted of a hand held vector network analyser combined with an off‐ground monostatic horn antenna, thereby setting up an ultra wideband stepped‐frequency continuous‐wave radar. Inversion of the radar Green’s function was performed in the time‐domain, on a time window focused on the surface reflection only. Results were compared with volumetric and time‐domain reflectometry measurements, as well as with an improved version of the standard reflection coefficient method. Except for water contents close to saturation, good agreements were obtained between ground‐penetrating radar, time‐domain reflectometry and volumetric samples. Significant differences were observed when the soil electric conductivity was high and could not be neglected anymore in the inversion process. Accounting for electric conductivity provided better results. Remaining errors were attributed to the different scales of characterization dealt with in relation to the vertical variability of water content in the top few centimetres of the soil. The proposed method appears to be more practical and accurate than the standard reflection coefficient method and shows great promise for real‐time mapping of surface soil moisture at the field scale.

Estimating surface water content from GPR data requires first an accurate estimate of the electric permittivity that can be attributed to a small depth range below the surface. Only when this is achieved does it make to sense to try and relate the permittivity to water content, which will then be surface water content. For small and large scale investigations, suitable methods are available. For the intermediate field or watershed scale, GPR is the most researched method. We briefly go through the most promising method available now using GPR surface reflection data calibrated by metallic plate reflections. We summarize the assumption underlying the common surface reflection model and extend the model by relaxing some of the assumptions, without the need of performing full waveform inversion. We find that the plane wave reflection coefficient approximation holds quite well over a wide frequency range, while keeping the antenna at sufficient height above the surface that is already necessary due to other assumptions. We find that extending the model to allow for frequency dependence also improves the permittivity estimate quite strongly in the high-frequency regime.