Abstract. Accurate estimation of Snow Water Equivalent (SWE) from satellite platforms remains one of the most complex challenges in cryospheric remote sensing, primarily limited by temporal coherence loss over natural surfaces and the complex dielectric interaction of radar signals with heterogeneous snowpacks. While C-band Interferometric Synthetic Aperture Radar (InSAR) has demonstrated theoretical potential for detecting snow accumulation via refractive phase delays, its operational application is often hindered by decorrelation phenomena and the instability of natural targets in alpine environments. Active Reflectors (AR), electronic devices traditionally employed for radiometric calibration and geodetic stability monitoring, have not yet been fully exploited for direct snowpack characterization. This study presents a novel methodological approach based on the installation of a C-band Active Reflector positioned flush with the ground surface, allowing natural snow accumulation directly over the device. The system, designed for operation with the Sentinel-1 constellation, integrates separate receiving and transmitting patch antennas with 42 dB radio frequency amplification, ensuring a stable phase response and dominant backscatter signal. The experimental campaign, conducted in Courmayeur (Italy) during the winter of 2024–2025, validated the system capability to provide point-scale SWE measurements through interferometric phase analysis. The system successfully tracked the evolution of the snowpack, measuring a peak SWE of approximately 250 mm, in close agreement with the in situ measurements. Statistical analysis reveals a high correlation between estimated and ground truth values (R2 > 0.93) for both Sentinel-1 descending relative orbits 066 and 139, with a Root Mean Square Error (RMSE) generally below 26 mm. These results indicate that this technology, by overcoming the logistical limitations of passive reflectors, can provide essential calibration nodes for future SAR missions and for data assimilation in hydrological models.
Perret et al. (2026) studied this question.