Articles | Volume 18, issue 7
https://doi.org/10.5194/tc-18-3177-2024
© Author(s) 2024. 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-18-3177-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Tower-based C-band radar measurements of an alpine snowpack
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Hans-Peter Marshall
Department of Geosciences, Boise State University, Boise, ID, USA
Gabrielle De Lannoy
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Devon Dunmire
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Christian Mätzler
GAMMA Remote Sensing, Gümligen, Switzerland
Hans Lievens
Department of Environment, Ghent University, Ghent, Belgium
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
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Cited
14 citations as recorded by crossref.
- Influence of snowpack properties and local incidence angle on SAR signal depolarization: a mathematical model for high-resolution snow depth estimation A. Mariani et al. https://doi.org/10.5194/tc-20-963-2026
- Design and Implementation of an SFCW Radar Platform for Environmental Monitoring J. Mulders et al. https://doi.org/10.3390/ndt4010006
- Comparison of snowmelt timing estimates from Sentinel-1 SAR and surface observations in British Columbia, Canada S. Darychuk et al. https://doi.org/10.1016/j.rse.2025.114863
- C-Band Radar-Based Improved Snow Depth Estimation (C-RISE) in the Indian Western Himalayas and Colorado Rocky Mountains R. Chandra Prabha et al. https://doi.org/10.1109/JSTARS.2025.3563462
- Snow Depth Estimation Based on STCTNet for Sentinel-1 and Multisource Data Q. Cheng et al. https://doi.org/10.1109/TGRS.2025.3629072
- A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery D. Dunmire et al. https://doi.org/10.1016/j.rse.2024.114369
- C-Band Radar Measurements in a Snow-Covered Boreal Forest Environment I. Brangers et al. https://doi.org/10.1109/LGRS.2024.3521544
- Investigating the Impact of Optical Snow Cover Data on L-Band InSAR Snow Water Equivalent Retrievals J. Tarricone et al. https://doi.org/10.34133/remotesensing.0682
- On demand machine learning-driven surface freeze-thaw retrieval across Canadian agricultural regions using Sentinel-1 SAR data S. Taghipourjavi et al. https://doi.org/10.3389/frsen.2025.1728399
- Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and process-based model simulations L. Boeykens et al. https://doi.org/10.5194/tc-20-3187-2026
- Evaluating L-band InSAR snow water equivalent retrievals with repeat ground-penetrating radar and terrestrial lidar surveys in northern Colorado R. Bonnell et al. https://doi.org/10.5194/tc-18-3765-2024
- Multitemporal analysis of Sentinel-1 backscatter during snowmelt using high-resolution field measurements and radiative transfer modelling F. Carletti et al. https://doi.org/10.5194/tc-19-5579-2025
- Retrieval of snow depth using synthetic aperture radar: past, current, and future Z. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135103
- Sensitivity of Sentinel-1 C-band SAR backscatter, polarimetry and interferometry to snow accumulation in the Alps J. Jans et al. https://doi.org/10.1016/j.rse.2024.114477
14 citations as recorded by crossref.
- Influence of snowpack properties and local incidence angle on SAR signal depolarization: a mathematical model for high-resolution snow depth estimation A. Mariani et al. https://doi.org/10.5194/tc-20-963-2026
- Design and Implementation of an SFCW Radar Platform for Environmental Monitoring J. Mulders et al. https://doi.org/10.3390/ndt4010006
- Comparison of snowmelt timing estimates from Sentinel-1 SAR and surface observations in British Columbia, Canada S. Darychuk et al. https://doi.org/10.1016/j.rse.2025.114863
- C-Band Radar-Based Improved Snow Depth Estimation (C-RISE) in the Indian Western Himalayas and Colorado Rocky Mountains R. Chandra Prabha et al. https://doi.org/10.1109/JSTARS.2025.3563462
- Snow Depth Estimation Based on STCTNet for Sentinel-1 and Multisource Data Q. Cheng et al. https://doi.org/10.1109/TGRS.2025.3629072
- A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery D. Dunmire et al. https://doi.org/10.1016/j.rse.2024.114369
- C-Band Radar Measurements in a Snow-Covered Boreal Forest Environment I. Brangers et al. https://doi.org/10.1109/LGRS.2024.3521544
- Investigating the Impact of Optical Snow Cover Data on L-Band InSAR Snow Water Equivalent Retrievals J. Tarricone et al. https://doi.org/10.34133/remotesensing.0682
- On demand machine learning-driven surface freeze-thaw retrieval across Canadian agricultural regions using Sentinel-1 SAR data S. Taghipourjavi et al. https://doi.org/10.3389/frsen.2025.1728399
- Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and process-based model simulations L. Boeykens et al. https://doi.org/10.5194/tc-20-3187-2026
- Evaluating L-band InSAR snow water equivalent retrievals with repeat ground-penetrating radar and terrestrial lidar surveys in northern Colorado R. Bonnell et al. https://doi.org/10.5194/tc-18-3765-2024
- Multitemporal analysis of Sentinel-1 backscatter during snowmelt using high-resolution field measurements and radiative transfer modelling F. Carletti et al. https://doi.org/10.5194/tc-19-5579-2025
- Retrieval of snow depth using synthetic aperture radar: past, current, and future Z. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135103
- Sensitivity of Sentinel-1 C-band SAR backscatter, polarimetry and interferometry to snow accumulation in the Alps J. Jans et al. https://doi.org/10.1016/j.rse.2024.114477
Saved (final revised paper)
Latest update: 21 Jul 2026
Short summary
To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
To better understand the interactions between C-band radar waves and snow, a tower-based...