Interferometric Synthetic Aperture Radar (InSAR) for Monitoring Seasonal Snow

Abstract

Interferometric Synthetic Aperture Radar (InSAR) offers a potential pathway for measuring seasonal snow water equivalent (SWE) and snow depth at spatial and temporal scales needed for basin‐scale hydrology. First proposed for seasonal snow more than 25 years ago, InSAR uses repeat‐pass radar phase differences to estimate changes in SWE or depth caused by microwave propagation through snow. This review synthesizes InSAR‐based snow monitoring from early theoretical and satellite demonstrations through recent tower, airborne, and spaceborne studies. We summarize the retrieval physics, evaluate published validation results, compare performance across wavelengths and snow environments, and identify barriers to operational implementation. The literature shows that InSAR can provide high‐resolution, spatially distributed information on snow accumulation and ablation, especially under dry‐snow conditions when temporal coherence is maintained and snow phase can be separated from atmospheric, vegetation, soil‐moisture, and other non‐snow contributions. Longer wavelengths, particularly L‐band, help maintain coherence, reduce phase‐wrapping ambiguity, and support retrievals in some vegetated environments, making the NASA–ISRO Synthetic Aperture Radar (NISAR) mission a timely opportunity for basin‐scale testing. Remaining challenges include correcting ionospheric and tropospheric delays; defining stable reference phases; identifying, correcting, or flagging coherence loss and phase‐unwrapping errors; accounting for forest‐canopy interactions; collecting spatially distributed validation data; and scaling site demonstrations to regional or global products. Operational use will require algorithmic maturation, robust validation, and integration with other remote sensing and modeling approaches. If these challenges are addressed, InSAR‐capable satellites could become an important component of an integrated global snow observation suite.