Work

Scale patterns of the Sentinel-1 SAR-based snow depth product compared with station measurements and airborne LiDAR observations

Year: 2026

Type: article

Abstract: . Water storage in snowpacks in mountain areas is critical for hydropower production, hydrological forecasting, and freshwater availability. Spaceborne synthetic aperture radar (SAR) is a powerful tool for quantitatively measuring snow mass because of its high spatial resolution and the sensitivity of signals to snow depth (SD). In particular, the first SAR SD product (C-snow) based on Sentinel-1 satellites displays high sensitivity to depolarization signals for dynamic SD monitoring in mountainous areas. Moreover, upscaled C-snow retrievals (e.g., 10 and 25 km) have been used to provide reference data to train machine learning models, improve passive microwave-based retrieval, and calibrate many hydrological models. However, a systematic assessment of C-snow products at various scales has not been conducted, until now. In this study, the performance of C-snow products at three scales (1, 10 and 25 km) is comparatively assessed via station-based measurements and airborne LiDAR observations, and the scale patterns associated with the heterogeneity of the geographic environment and the representativeness of so-called truth data are analyzed. The results indicate that the scale patterns of the C-snow products across various resolutions differ from those of station- and airborne-based reference data. As the spatial scale increases from 1 to 25 km, the error of C-snow retrieval in reference to station measurements tends to increase (e.g., ubRMSE from 68.18 to 77.47 cm, bias from -9.81 to 10.68 cm), whereas it tends to decrease compared with airborne snow observatory (ASO) data, with ubRMSE values ranging from 104.3 to 83.29 cm, and the bias values from -91.31 to -52.73 cm. We also found that land cover types, e.g., tree cover and permanent ice, affect the C-snow product at various scales. Especially an overestimation tends to occur in coarse pixels covered with even a small amount of permanent ice. It is concluded that C-snow retrieval at three scales is characterized by high uncertainty. Researchers should focus on developing a robust SD retrieval algorithm by combining SAR backscattering signals and polarimetric and interferometric information.

Source: The cryosphere

DOI: 10.5194/tc-20-227-2026

Authors: Jiajie Ying, Lingmei Jiang, Jinmei Pan, Chuan Xiong, Jianwei Yang