High-resolution soil moisture (SM) observations are critical for agricultural monitoring, forestry management, and hazard prediction, yet current satellite passive microwave missions are unable to directly provide retrievals at tens-of-meter spatial scales. Unmanned aerial vehicle (UAV)–mounted microwave radiometry presents a promising alternative, but most evaluations to date have focused on agricultural settings, with limited exploration across other land covers and few efforts to quantify retrieval uncertainty. This study addresses both gaps by evaluating SM retrievals from a drone-based Portable L-band Radiometer (PoLRa) across shrubland, bare soil, and forest strips in Central Illinois, U.S., using a 10-day field campaign in 2024. Controlled UAV flights at altitudes of 10 m, 20 m, and 30 m were performed to generate brightness temperatures (TB) at spatial resolutions of 7 m, 14 m, and 21 m. SM retrievals were carried out using multiple tau-omega-based algorithms, including the single channel algorithm (SCA), dual channel algorithm (DCA), and multi-temporal dual-channel algorithm (MT-DCA). A Bayesian inference framework was then applied to provide probabilistic uncertainty characterization for both SM and vegetation optical depth (VOD). Results show that the gridded TBdistributions consistently capture dry-wet gradients associated with vegetation density variations, and spatial correlations between polarized observations are largely maintained across scales. Validation againstin situmeasurements indicates that PoLRa-derived SM retrievals from the SCA-V and MT-DCA algorithms achieve unbiased root-mean-square errors (ubRMSE) generally below 0.04 m3/m3across different land covers. Bayesian posterior analyses confirm that reference SM values largely fall within the derived uncertainty intervals, with mean uncertainty ranges around ± 0.02 m3/m3and ± 0.11 m3/m3for SCA and DCA-related retrievals. These findings underscore the potential of UAV-mounted PoLRa for high-resolution SM retrieval across varied landscapes and emphasize the need for standardized calibration and uncertainty quantification frameworks to support broader scientific and operational adoption.

Moisture estimation of sub-surface soil and the overlaying biomass layer is pivotal in precision agriculture and wildfire risk assessment. However, the characterization of layered material is nontrivial due to the radar penetration-resolution tradeoff. Here, a waveform inversion-based method was proposed to predict the dielectric permittivity (as a moisture proxy) of the bottom soil layer and the top biomass layer from radar signals. Specifically, the use of a combination of a higher and a lower frequency radar compared to a single frequency in predicting the permittivity of both the soil and the overlaying layer was investigated in this study. The results show that each layer was best characterized via one of the frequencies. However, for the simultaneous prediction of both layers’ permittivity, the most consistent results were achieved by inversion of data from a combination of both frequencies, showing better correlation with in situ permittivity and reduced prediction errors.

Accurate forest biomass estimation is crucial for effective wildfire risk assessment and management as well as in agricultural applications. However, existing remote sensing methods cannot estimate under-canopy biomass. This paper investigates the feasibility of a novel approach that leverages ultra-wideband radars mounted on uncrewed aerial systems in conjunction with reference ground reflectors to estimate the under-canopy biomass profile. Through extensive electromagnetic wave propagation simulations encompassing diverse material configurations, we investigate the sensing capabilities of our proposed technique. We leverage deep learning to effectively extract biomass information from radar signals, enabling the prediction of material properties and geometries. The developed deep learning system was successfully trained with an R2metric of 98% in testing and produced dielectric permittivity distributions that visually match the target configurations. The results underscore the potential of our approach in advancing wildfire management strategies, bolstering carbon sequestration efforts, preserving forests, and aiding agricultural applications by facilitating accurate biomass characterization and estimations of crop yield and soil moisture.