Airborne synthetic aperture radar (SAR) sensors have been commonly used during the last decades to monitor different phenomena in medium-scale areas of observation, such as object detection and characterization or topographic mapping. The use of unmanned aerial vehicles (UAVs) is a cost-effective solution that offers higher operational flexibility than airborne systems to monitor these types of scenarios. The Universitat Politècnica de Catalunya has developed the first fully polarimetric SAR system at X-band integrated into a small UAV multicopter platform (UAV MP). The sensor, called AiR-based remote sensing, has been integrated into the platform overcoming restrictions of weight, space, robustness, and power consumption. To demonstrate the validity of the developed system, some measurement campaigns have been conducted in the outskirts of Barcelona, Spain.

Ground-based synthetic aperture radar (GB-SAR) sensors represent a cost-effective solution for change detection and ground displacement assessment of small-scale areas in real-time early warning applications. GB-SAR systems based on stepped linear frequency modulated continuous wave signals have led to several improvements such as a significant reduction of the acquisition time. Nevertheless, the acquisition time is still long enough to force a degradation of the quality of the reconstructed images because of possible short-term variable reflectivity of the scenario. This reduction of the quality may degrade the differential interferometric detection process. In scenarios where interesting targets are surrounded by vegetation, this is normally related to atmospheric conditions, in particular, the wind. The present paper characterizes the effect of the short-term variable reflectivity in the GB-SAR image reconstruction and evaluates its equivalent blurring effect, the decorrelation introduced in the SAR images, and the degradation of the extracted parameters. In order to validate the results, the study assesses different GB-SAR images obtained with the RISKSAR-X sensor, which has been developed by the Universitat Politècnica de Catalunya.

Accurate flight trajectory knowledge is crucial for airborne and drone-borne repeat-pass Synthetic Aperture Radar (SAR) interferometry. Given that Global Navigation Satellite System (GNSS) inaccuracies are comparable to typical radar wavelengths, especially at high-frequency bands (X-band and higher), estimating the Residual Motion Error (RME) is essential to ensure precise coregistration of Single-Look Complex (SLC) stacks and to generate high-quality repeat-pass interferograms. In this framework, traditional RME estimation techniques, such as the multisquint algorithm, often suffer performance degradation under large errors and high-decorrelation conditions, as they rely heavily on the quality of uncoregistered interferograms. To address this limitation, this paper introduces an enhanced version of the multisquint technique that solves the estimation of RME using a minimization strategy in the complex domain and uses the retrieved RMEs of the entire interferometric dataset to refine the estimates. This approach improves RME estimation robustness against large errors and decorrelation artifacts, particularly in high-frequency airborne and drone-borne SAR datasets. The proposed method is validated using a real dataset acquired with a Ku-band drone-borne SAR system, demonstrating significant improvements in coregistration accuracy. Quantitative evaluations based on interferometric coherence and phase fringe frequency metrics confirm its effectiveness, establishing the proposed refinement as a robust solution for high-precision interferometric applications.

Multitemporal differential interferometric synthetic aperture radar (MT-DInSAR) techniques have become essential tools for monitoring ground displacement with millimeter-scale precision. While widely applied to spaceborne SAR systems for global, long-term monitoring, and to ground-based SAR (GBSAR) for continuous tracking in localized areas, these platforms remain constrained by fixed acquisition geometries and line-of-sight (LOS) sensitivity. In addition, spaceborne systems face inherent limitations in capturing high-magnitude displacements due to their relatively long revisit times, often leading to temporal decorrelation in such scenarios. In this context, airborne SAR systems offer a compromise by enabling flexible acquisition geometries and on-demand revisit times, with drone platforms emerging as a cost-effective alternative to the high operational and logistical demands of conventional airborne systems. This article presents the SAR-Drone system, a Ku-band drone-based SAR, and a coregistration and interferometric processor together with two multitemporal DInSAR (MT-DInSAR) methodologies adapted to the SAR-Drone data. The first is displacement-based and designed for scenarios with moderate motion, where interferometric phase remains coherent between consecutive acquisitions; the second is velocity-based, targeting scenarios with high-magnitude displacements where decorrelation occurs even when only a few hours separate acquisitions. The displacement-based methodology is validated in a controlled experiment using corner reflectors, while the velocity-based methodology is demonstrated in a real open-pit mine, where metric-scale slope movements occur within intervals of approximately one day. The results presented in the article demonstrate the potential of drone-based SAR to complement existing spaceborne, ground-based, and airborne systems by enabling high-resolution 3-D displacement tracking in complex and rapidly evolving environments.