Articles | Volume 20, issue 8
https://doi.org/10.5194/tc-20-4537-2026
© 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-20-4537-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Aug 2026
Research article |
| 20 Aug 2026
| 20 Aug 2026
Using LIDAR and SNOTEL data for evaluating the performance of snow water equivalent retrieval using Sentinel-1 repeat-pass interferometry
Shadi Oveisgharan
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA, USA
Emre Havazli
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA, USA
Robert Zinke
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA, USA
Zachary Hoppinen
Boise State University, Department of Geosciences, 1295 University Drive, Boise, ID, USA
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Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
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The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
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We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
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This preprint is open for discussion and under review for The Cryosphere (TC).
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Radar satellites measure new snowfall by passing over the same ground twice and sensing how the snow delays their signal. To map it, they average each measurement over patches tens of meters wide, assuming snowfall is uniform within them. Aircraft laser surveys show it actually varies sharply below ~25 m, far from uniform. This unevenness degrades the measurement and biases the estimate, worst in steep terrain and big storms, as two radar image pairs over snowy and snow-free periods confirm.
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The recently-launched NISAR satellite has potential to provide new measurements of the water stored in seasonal snow, but various non-snow factors can introduce error into these new measurements. We analyzed the effects of six non-snow errors and found that impacts from the ionosphere on NISAR phase data must be addressed for accurate snow measurements. Other non-snow factors can have partially offsetting effects on snow measurements, depending on environmental properties at a particular site.
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This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar-derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and nine automated snow monitoring stations.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
Short summary
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The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024, https://doi.org/10.5194/tc-18-575-2024, 2024
Short summary
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We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
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Short summary
Accurate measurements of water stored in mountain snow are essential for managing water resources and understanding climate change. We evaluated a satellite-based method using airborne laser surveys and ground observations across several snow-covered regions. The method performed well under dry snow conditions and identified the factors that affect its accuracy, supporting improved monitoring of mountain snow from future satellite missions.
Accurate measurements of water stored in mountain snow are essential for managing water...
