Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-227-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-227-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2026
Research article |
| 14 Jan 2026
| 14 Jan 2026
Scale patterns of the Sentinel-1 SAR-based snow depth product compared with station measurements and airborne LiDAR observations
Jiajie Ying
State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Lingmei Jiang
State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jinmei Pan
China National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
Chuan Xiong
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
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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.
Borah, F. K., Jans, J.-F., Huang, Z., Tsang, L., Lievens, H., and Kim, E.: Time Series Analysis of C-Band Sentinel-1 SAR Over Mountainous Snow with Physical Models of Volume and Surface Scattering, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1825, 2024.
Brock, B. C.: On the detection of crevasses in glacial ice with synthetic-aperture radar, United States, Sandia National Laboratories, Sandia Report SAND2010-0363, https://doi.org/10.2172/989382, 2010.
Broxton, P., Ehsani, M. R., and Behrangi, A.: Improving Mountain Snowpack Estimation Using Machine Learning With Sentinel-1, the Airborne Snow Observatory, and University of Arizona Snowpack Data, Earth and Space Science, 11, e2023EA002964, https://doi.org/10.1029/2023EA002964, 2024.
Bulygina, O. N., Groisman, P. Y., Razuvaev, V. N., and Korshunova, N. N.: Changes in snow cover characteristics over Northern Eurasia since 1966, Environmental Research Letters, 6, 045204, https://doi.org/10.1088/1748-9326/6/4/045204, 2011.
Chang, A. T. C., Foster, J. L., and Hall, D. K.: Nimbus-7 SMMR Derived Global Snow Cover Parameters, Annals of Glaciology, 9, 39–44, https://doi.org/10.3189/S0260305500200736, 1987.
Chang, W., Tan, S., Lemmetyinen, J., Tsang, L., Xu, X., and Yueh, S. H.: Dense Media Radiative Transfer Applied to SnowScat and SnowSAR, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 3811–3825, https://doi.org/10.1109/JSTARS.2014.2343519, 2014.
Cui, Y., Xiong, C., Lemmetyinen, J., Shi, J., Jiang, L., Peng, B., Li, H., Zhao, T., Ji, D., and Hu, T.: Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss, Remote Sensing, 8, 505, https://doi.org/10.3390/rs8060505, 2016.
Daloz, A. S., Mateling, M., L'Ecuyer, T., Kulie, M., Wood, N. B., Durand, M., Wrzesien, M., Stjern, C. W., and Dimri, A. P.: How much snow falls in the world's mountains? A first look at mountain snowfall estimates in A-train observations and reanalyses, The Cryosphere, 14, 3195–3207, https://doi.org/10.5194/tc-14-3195-2020, 2020.
Derksen, C., Toose, P., Rees, A., Wang, L., English, M., Walker, A., and Sturm, M.: Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data, Remote Sensing of Environment, 114, 1699–1709, https://doi.org/10.1016/j.rse.2010.02.019, 2010.
Dozier, J., Bair, E. H., and Davis, R. E.: Estimating the spatial distribution of snow water equivalent in the world's mountains, WIREs Water, 3, 461–474, https://doi.org/10.1002/wat2.1140, 2016.
Du, J., Shi, J., and Rott, H.: Comparison between a multi-scattering and multi-layer snow scattering model and its parameterized snow backscattering model, Remote Sensing of Environment, 114, 1089–1098, https://doi.org/10.1016/j.rse.2009.12.020, 2010.
Enderlin, E. M., Elkin, C. M., Gendreau, M., Marshall, H. P., O'Neel, S., McNeil, C., Florentine, C., and Sass, L.: Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions, Remote Sensing of Environment, 283, 113307, https://doi.org/10.1016/j.rse.2022.113307, 2022.
Fassnacht, S. R. and Deems, J. S.: Measurement sampling and scaling for deep montane snow depth data, Hydrological Processes, 20, 829–838, https://doi.org/10.1002/hyp.6119, 2006.
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung, D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The International Classification for Seasonal Snow on the Ground, IHP-VII Technical Documents in Hydrology No. 83, IACS Contribution No. 1, UNESCO-IHP, Paris, https://unesdoc.unesco.org/ark:/48223/pf0000186462 (last access: 18 November 2025), 2009.
Ge, Y., Jin, Y., Stein, A., Chen, Y., Wang, J., Wang, J., Cheng, Q., Bai, H., Liu, M., and Atkinson, P. M.: Principles and methods of scaling geospatial Earth science data, Earth-Science Reviews, 197, 102897, https://doi.org/10.1016/j.earscirev.2019.102897, 2019.
Girotto, M., Formetta, G., Azimi, S., Bachand, C., Cowherd, M., De Lannoy, G., Lievens, H., Modanesi, S., Raleigh, M. S., Rigon, R., and Massari, C.: Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals, Science of The Total Environment, 906, 167312, https://doi.org/10.1016/j.scitotenv.2023.167312, 2024.
Herbert, J. N., Raleigh, M. S., and Small, E. E.: Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data, The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, 2024.
Hoppinen, Z., Palomaki, R. T., Brencher, G., Dunmire, D., Gagliano, E., Marziliano, A., Tarricone, J., and Marshall, H.-P.: Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites, The Cryosphere, 18, 5407–5430, https://doi.org/10.5194/tc-18-5407-2024, 2024.
Hou, Y., Huang, X., and Zhao, L.: Point-to-Surface Upscaling Algorithms for Snow Depth Ground Observations, Remote Sensing, 14, 4840, https://doi.org/10.3390/rs14194840, 2022.
Kelly, R.: The AMSR-E snow depth algorithm: Description and initial results, Journal of the Remote Sensing Society of Japan, 29, 307–317, https://doi.org/10.11440/rssj.29.307, 2009.
King, J., Derksen, C., Toose, P., Langlois, A., Larsen, C., Lemmetyinen, J., Marsh, P., Montpetit, B., Roy, A., Rutter, N., and Sturm, M.: The influence of snow microstructure on dual-frequency radar measurements in a tundra environment, Remote Sensing of Environment, 215, 242–254, https://doi.org/10.1016/j.rse.2018.05.028, 2018.
Klápště, P., Fogl, M., Barták, V., Gdulová, K., Urban, R., and Moudrý, V.: Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds, International Journal of Digital Earth, 13, 1672–1694, https://doi.org/10.1080/17538947.2020.1791267, 2020.
Leinss, S., Löwe, H., Proksch, M., Lemmetyinen, J., Wiesmann, A., and Hajnsek, I.: Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series, The Cryosphere, 10, 1771–1797, https://doi.org/10.5194/tc-10-1771-2016, 2016.
Lemmetyinen, J., Schwank, M., Rautiainen, K., Kontu, A., Parkkinen, T., Mätzler, C., Wiesmann, A., Wegmüller, U., Derksen, C., Toose, P., Roy, A., and Pulliainen, J.: Snow density and ground permittivity retrieved from L-band radiometry: Application to experimental data, Remote Sensing of Environment, 180, 377–391, https://doi.org/10.1016/j.rse.2016.02.002, 2016.
Lievens, H., Demuzere, M., Marshall, H.-P., Reichle, R. H., Brucker, L., Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W., Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J., and De Lannoy, G. J. M.: Snow depth variability in the Northern Hemisphere mountains observed from space, Nature Communications, 10, 4629, https://doi.org/10.1038/s41467-019-12566-y, 2019.
Lievens, H., Brangers, I., Marshall, H.-P., Jonas, T., Olefs, M., and De Lannoy, G.: Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps, The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, 2022.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Mortimer, C., Derksen, C., Mudryk, L., Moisander, M., Hiltunen, M., Smolander, T., Ikonen, J., Cohen, J., Salminen, M., Norberg, J., Veijola, K., and Venäläinen, P.: GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset, Scientific Data, 8, 163, https://doi.org/10.1038/s41597-021-00939-2, 2021.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An Overview of the Global Historical Climatology Network-Daily Database, Journal of Atmospheric and Oceanic Technology, 29, 897–910, https://doi.org/10.1175/JTECH-D-11-00103.1, 2012.
Neuenschwander, A., Guenther, E., White, J. C., Duncanson, L., and Montesano, P.: Validation of ICESat-2 terrain and canopy heights in boreal forests, Remote Sensing of Environment, 251, 112110, https://doi.org/10.1016/j.rse.2020.112110, 2020.
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann, C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C., and Winstral, A.: The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo, Remote Sensing of Environment, 184, 139–152, https://doi.org/10.1016/j.rse.2016.06.018, 2016.
Picard, G., Sandells, M., and Löwe, H.: SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0), Geosci. Model Dev., 11, 2763–2788, https://doi.org/10.5194/gmd-11-2763-2018, 2018.
Picard, G., Löwe, H., Domine, F., Arnaud, L., Larue, F., Favier, V., Le Meur, E., Lefebvre, E., Savarino, J., and Royer, A.: The Microwave Snow Grain Size: A New Concept to Predict Satellite Observations Over Snow-Covered Regions, AGU Advances, 3, e2021AV000630, https://doi.org/10.1029/2021AV000630, 2022.
Qin, Y., Abatzoglou, J. T., Siebert, S., Huning, L. S., AghaKouchak, A., Mankin, J. S., Hong, C., Tong, D., Davis, S. J., and Mueller, N. D.: Agricultural risks from changing snowmelt, Nature Climate Change, 10, 459–465, https://doi.org/10.1038/s41558-020-0746-8, 2020.
Rott, H., Yueh, S. H., Cline, D. W., Duguay, C., Essery, R., Haas, C., Hélière, F., Kern, M., Macelloni, G., Malnes, E., Nagler, T., Pulliainen, J., Rebhan, H., and Thompson, A.: Cold Regions Hydrology High-Resolution Observatory for Snow and Cold Land Processes, Proceedings of the IEEE, 98, 752–765, https://doi.org/10.1109/JPROC.2009.2038947, 2010.
Sander, G. J. and Bickel, D. L.: Antarctica X-band MiniSAR crevasse detection radar: final report, United States, Sandia National Laboratories, Sandia Report SAND2007-3526, https://doi.org/10.2172/920457, 2007.
Scott, J. B. T., Mair, D., Nienow, P., Parry, V., and Morris, E.: A ground-based radar backscatter investigation in the percolation zone of the Greenland ice sheet, Remote Sensing of Environment, 104, 361–373, https://doi.org/10.1016/j.rse.2006.05.009, 2006.
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data, Water Resources Research, 35, 2145–2160, https://doi.org/10.1029/1999WR900090, 1999.
Shi, J. and Dozier, J.: Estimation of snow water equivalence using SIR-C/X-SAR. II. Inferring snow depth and particle size, IEEE Transactions on Geoscience and Remote Sensing, 38, 2475–2488, https://doi.org/10.1109/36.885196, 2000.
Sourp, L., Gascoin, S., Jarlan, L., Pedinotti, V., Bormann, K. J., and Baba, M. W.: Evaluation of high-resolution snowpack simulations from global datasets and comparison with Sentinel-1 snow depth retrievals in the Sierra Nevada, USA, Hydrol. Earth Syst. Sci., 29, 597–611, https://doi.org/10.5194/hess-29-597-2025, 2025.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.: Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes, Journal of Hydrometeorology, 11, 1380–1394, https://doi.org/10.1175/2010JHM1202.1, 2010.
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.-P., Koskinen, J., and Bojkov, B.: Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements, Remote Sensing of Environment, 115, 3517–3529, https://doi.org/10.1016/j.rse.2011.08.014, 2011.
Tanniru, S. and Ramsankaran, R.: Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions, Remote. Sens., 15, 1052, https://doi.org/10.3390/rs15041052, 2023.
Tsang, L., Durand, M., Derksen, C., Barros, A. P., Kang, D.-H., Lievens, H., Marshall, H.-P., Zhu, J., Johnson, J., King, J., Lemmetyinen, J., Sandells, M., Rutter, N., Siqueira, P., Nolin, A., Osmanoglu, B., Vuyovich, C., Kim, E., Taylor, D., Merkouriadi, I., Brucker, L., Navari, M., Dumont, M., Kelly, R., Kim, R. S., Liao, T.-H., Borah, F., and Xu, X.: Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing, The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, 2022.
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave remote sensing: Active and passive, Volume 2 – Radar remote sensing and surface scattering and emission theory, ISBN 0-89006-191-2, 1982.
Venäläinen, P., Luojus, K., Lemmetyinen, J., Pulliainen, J., Moisander, M., and Takala, M.: Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy, The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, 2021.
Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021a.
Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020) (Version v2), Zenodo [data set], https://doi.org/10.5281/zenodo.5217044, 2021b.
Wan, W., Zhang, J., Dai, L., Liang, H., Yang, T., Liu, B., Guo, Z., Hu, H., and Zhao, L.: A new snow depth data set over northern China derived using GNSS interferometric reflectometry from a continuously operating network (GSnow-CHINA v1.0, 2013–2022), Earth Syst. Sci. Data, 14, 3549–3571, https://doi.org/10.5194/essd-14-3549-2022, 2022.
Xiong, C., Yang, J., Pan, J., Lei, Y., and Shi, J.: Mountain Snow Depth Retrieval From Optical and Passive Microwave Remote Sensing Using Machine Learning, IEEE Geoscience and Remote Sensing Letters, 19, 1–5, https://doi.org/10.1109/LGRS.2022.3226204, 2022.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophysical Research Letters, 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017.
Yang, J., Jiang, L., Zheng, Z., Pan, J., and Shayiran, A.: A New Operational Northern Hemisphere Snow Water Equivalent Retrieval Algorithm for FY-3F/MWRI-II Based on Pixel-Based Regression Coefficients, IEEE Transactions on Geoscience and Remote Sensing, 62, 1–15, https://doi.org/10.1109/TGRS.2024.3479452, 2024.
Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N. E., Ramoino, F., and Arino, O.: ESA WorldCover 10 m 2020 v100, Zenodo [data set], https://doi.org/10.5281/zenodo.5571936, 2021.
Zhu, J., Tan, S., Tsang, L., Kang, D.-H., and Kim, E.: Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations, Water Resources Research, 57, e2020WR027563, https://doi.org/10.1029/2020WR027563, 2021.
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.
The Sentinel-1 C-band product (C-snow) has been widely used as reference data across various...
