Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-227-2026
https://doi.org/10.5194/tc-20-227-2026
Research article
 | 
14 Jan 2026
Research article |  | 14 Jan 2026

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

Jiajie Ying, Jianwei Yang, Lingmei Jiang, Jinmei Pan, and Chuan Xiong

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Cited articles

Alfieri, L., Avanzi, F., Delogu, F., Gabellani, S., Bruno, G., Campo, L., Libertino, A., Massari, C., Tarpanelli, A., Rains, D., Miralles, D. G., Quast, R., Vreugdenhil, M., Wu, H., and Brocca, L.: High-resolution satellite products improve hydrological modeling in northern Italy, Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, 2022. 
Alonso-González, E., López-Moreno, J. I., Gascoin, S., García-Valdecasas Ojeda, M., Sanmiguel-Vallelado, A., Navarro-Serrano, F., Revuelto, J., Ceballos, A., Esteban-Parra, M. J., and Essery, R.: Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014, Earth Syst. Sci. Data, 10, 303–315, https://doi.org/10.5194/essd-10-303-2018, 2018. 
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005. 
Baumgartner, F., Jezek, K., Forster, R. R., Gogineni, S. P., and Zabel, I. H. H.: Spectral and angular ground-based radar backscatter measurements of Greenland snow facies, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No. 99CH36293), 1053–1055, https://doi.org/10.1109/IGARSS.1999.774530, 1999. 
Bernier, M., Fortin, J.-P., Gauthier, Y., Gauthier, R., Roy, R., and Vincent, P.: Determination of snow water equivalent using RADARSAT SAR data in eastern Canada, Hydrological Processes, 13, 3041–3051, https://doi.org/10.1002/(SICI)1099-1085(19991230)13:18<3041::AID-HYP14>3.0.CO;2-E, 1999. 
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Short summary
The Sentinel-1 C-band product (C-snow) has been widely used as reference data across various scales, but its reliability remains unknown. This study systematically evaluates its performance at 1, 10, and 25 km scales using ground-based measurements and airborne Light Detection and Ranging (LiDAR) data. Its performance varies with forest cover, topography, permanent ice, and wet snow. Errors increase with scale relative to stations but decrease compared with LiDAR observations.
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