Articles | Volume 18, issue 8
https://doi.org/10.5194/tc-18-3765-2024
© 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-18-3765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Aug 2024
Research article |
| 22 Aug 2024
| 22 Aug 2024
Evaluating L-band InSAR snow water equivalent retrievals with repeat ground-penetrating radar and terrestrial lidar surveys in northern Colorado
Randall Bonnell
CORRESPONDING AUTHOR
Department of Geosciences, Colorado State University, Fort Collins, Colorado, USA
Daniel McGrath
Department of Geosciences, Colorado State University, Fort Collins, Colorado, USA
Jack Tarricone
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
NASA Postdoctoral Program, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Hans-Peter Marshall
Department of Geosciences, Boise State University, Boise, Idaho, USA
Ella Bump
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, USA
Caroline Duncan
Alaska District, U.S. Army Corps of Engineers, Anchorage, Alaska, USA
Stephanie Kampf
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, USA
Yunling Lou
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Alex Olsen-Mikitowicz
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, USA
Megan Sears
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, USA
Keith Williams
GAGE Facility, UNAVCO Inc., Boulder, Colorado, USA
Lucas Zeller
Department of Geosciences, Colorado State University, Fort Collins, Colorado, USA
Yang Zheng
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Viewed
Total article views: 6,277 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Feb 2024)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 4,973 | 1,165 | 139 | 6,277 | 379 | 185 | 323 |
- HTML: 4,973
- PDF: 1,165
- XML: 139
- Total: 6,277
- Supplement: 379
- BibTeX: 185
- EndNote: 323
Total article views: 3,692 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Aug 2024)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,200 | 427 | 65 | 3,692 | 119 | 101 | 153 |
- HTML: 3,200
- PDF: 427
- XML: 65
- Total: 3,692
- Supplement: 119
- BibTeX: 101
- EndNote: 153
Total article views: 2,585 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Feb 2024)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,773 | 738 | 74 | 2,585 | 260 | 84 | 170 |
- HTML: 1,773
- PDF: 738
- XML: 74
- Total: 2,585
- Supplement: 260
- BibTeX: 84
- EndNote: 170
Viewed (geographical distribution)
Total article views: 6,277 (including HTML, PDF, and XML)
Thereof 6,142 with geography defined
and 135 with unknown origin.
Total article views: 3,692 (including HTML, PDF, and XML)
Thereof 3,564 with geography defined
and 128 with unknown origin.
Total article views: 2,585 (including HTML, PDF, and XML)
Thereof 2,578 with geography defined
and 7 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
13 citations as recorded by crossref.
- Deep learning of seasonal peak snow water content of global boreal forest and arctic using spaceborne L-band radiometry D. Kumawat et al. https://doi.org/10.1016/j.rse.2025.114963
- Multi-layered snowpack refractive index estimation based on optical path difference compensation L. Harkati https://doi.org/10.1080/01431161.2025.2586473
- Reflectance of Solar Light From Wet Snowpack: Direct and Inverse Problems A. Kokhanovsky et al. https://doi.org/10.1109/TGRS.2025.3650360
- 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
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- Snow depth measurements from Arctic tundra and boreal forest collected during NASA SnowEx Alaska campaign S. Stuefer et al. https://doi.org/10.1038/s41597-025-05170-x
- 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
- Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data I. Alabi et al. https://doi.org/10.3389/frsen.2024.1481848
- UAV-borne GPR for snowpack characterization: Potential, limitations and operational guidelines B. Dupuy et al. https://doi.org/10.1016/j.coldregions.2025.104641
- A spatiotemporal analysis of errors in InSAR SWE measurements caused by non-snow phase changes R. Palomaki et al. https://doi.org/10.5194/tc-20-2703-2026
- Exploring D-Pol-InSAR Coherence Regions for Snow Water Equivalent Estimation K. Belinska et al. https://doi.org/10.1109/TGRS.2026.3672275
- Calculating the Optimal Point Cloud Density for Airborne LiDAR Landslide Investigation: An Adaptive Approach Z. Liao et al. https://doi.org/10.3390/rs16234563
- Monitoring Snowmelt in Mountainous Areas by Considering SAR Geometric Distortion From Ascending and Descending Orbits Y. Zhang et al. https://doi.org/10.1109/JSTARS.2025.3580604
13 citations as recorded by crossref.
- Deep learning of seasonal peak snow water content of global boreal forest and arctic using spaceborne L-band radiometry D. Kumawat et al. https://doi.org/10.1016/j.rse.2025.114963
- Multi-layered snowpack refractive index estimation based on optical path difference compensation L. Harkati https://doi.org/10.1080/01431161.2025.2586473
- Reflectance of Solar Light From Wet Snowpack: Direct and Inverse Problems A. Kokhanovsky et al. https://doi.org/10.1109/TGRS.2025.3650360
- 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
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- Snow depth measurements from Arctic tundra and boreal forest collected during NASA SnowEx Alaska campaign S. Stuefer et al. https://doi.org/10.1038/s41597-025-05170-x
- 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
- Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data I. Alabi et al. https://doi.org/10.3389/frsen.2024.1481848
- UAV-borne GPR for snowpack characterization: Potential, limitations and operational guidelines B. Dupuy et al. https://doi.org/10.1016/j.coldregions.2025.104641
- A spatiotemporal analysis of errors in InSAR SWE measurements caused by non-snow phase changes R. Palomaki et al. https://doi.org/10.5194/tc-20-2703-2026
- Exploring D-Pol-InSAR Coherence Regions for Snow Water Equivalent Estimation K. Belinska et al. https://doi.org/10.1109/TGRS.2026.3672275
- Calculating the Optimal Point Cloud Density for Airborne LiDAR Landslide Investigation: An Adaptive Approach Z. Liao et al. https://doi.org/10.3390/rs16234563
- Monitoring Snowmelt in Mountainous Areas by Considering SAR Geometric Distortion From Ascending and Descending Orbits Y. Zhang et al. https://doi.org/10.1109/JSTARS.2025.3580604
Saved (final revised paper)
Latest update: 30 May 2026
Short summary
Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Snow provides water for billions of people, but the amount of snow is difficult to detect...
