Snow depth plays a central role in seasonal snowpack characterization and the terrestrial water cycle, yet remains challenging to estimate at high spatial resolution. Recent studies have shown that repeat-pass interferometric synthetic aperture radar (InSAR) measurements combined with physics-based models can enable effective snow water equivalent (SWE) retrieval. However, the performance of these methods depends strongly on measurement accuracy and modeling assumptions. Building on the success of InSAR-based approaches, we develop a robust learning-based model that directly learns the relationship between measured InSAR observables and snow depth. The model is trained on a single SnowEx Idaho site and evaluated across independent years and geographically distinct regions. Results demonstrate strong temporal and spatial transferability. In temporal transfer experiments, the proposed approach achieves a Pearson correlation of 0.81 with lidar snow depth, compared to a correlation of approximately 0.47 reported for physics-based Sentinel-1 SWE retrievals over the same site.

翻译：积雪深度在季节性积雪表征和陆地水循环中具有核心作用，但其高空间分辨率估算仍面临挑战。近期研究表明，将重复轨道干涉合成孔径雷达（InSAR）测量数据与物理模型相结合，可实现有效的雪水当量（SWE）反演。然而，这类方法的性能高度依赖于测量精度与建模假设。基于InSAR方法的成功经验，我们开发了一种稳健的深度学习模型，能够直接学习InSAR观测变量与积雪深度之间的映射关系。该模型在单个SnowEx爱达荷州站点完成训练，并在不同年份及地理位置独立的区域进行评估。结果表明该模型具备强大的时空迁移能力。在时间迁移实验中，本方法反演的积雪深度与激光雷达测量值的皮尔逊相关系数达到0.81，而同一站点基于物理模型的哨兵一号SWE反演结果相关系数约为0.47。