Assessing the Applicability of Sentinel-1 D-InSAR for Snow Depth Retrieval in the Maxian Mountains

Differential interferometric synthetic aperture radar (D-InSAR) provides a potential approach for retrieving mountain snow depth at high spatial resolution using Sentinel-1 interferometric phase information. However, its performance in complex mountainous terrain remains insufficiently evaluated. This study assessed the applicability and uncertainty of Sentinel-1 D-InSAR for multi-temporal snow depth retrieval in the Maxian Mountains. Sentinel-1 interferometric pairs from 2020 to 2022 were used for snow depth retrieval, and UAV-LiDAR snow depth estimates served as independent reference data. The spatial distribution of interferometric coherence was examined, and coherence statistics were compared among elevation intervals and land-cover types. Snow depth was derived from interferometric phase using snow density and local incidence angle as model inputs, and sensitivity to both parameters was evaluated. The retrievals characterized the main spatial distribution of snow depth, but validation accuracy varied markedly among periods. Compared with November 2020, the root mean square error (RMSE) and mean absolute error (MAE) in April 2021 increased by 3.62 and 2.55 cm, respectively, indicating larger errors under spring snow conditions. Interferometric coherence was a key factor controlling retrieval reliability, and areas with low coherence were more prone to phase-unwrapping errors, missing retrievals, and local anomalies. Sensitivity analysis showed that snow density had a greater influence than local incidence angle.