Evaluating Sentinel-1 volume scattering based snow depth retrievals over NASA SnowEx sites
- 1Cryosphere Geophysics and Remote Sensing, Boise State University, Boise, ID, USA
- 2U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH, USA
- 3Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA
- 4Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
- 5Postdoctoral Program, NASA Goddard Space Flight Center, Greenbelt, MD, USA
- 6Civil and Environmental Engineering Department, University of Washington, Seattle, WA, USA
- 7Department of Earth and Environmental Sciences, KU Leuven, Herverlee, BE
- 8Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
- 9Center for Water and the Environment, University of New Mexico, Albuquerque, NM, USA
- 10Department of Geosciences, Colorado State University, Fort Collins, CO, USA
Synthetic aperture radar will be at the forefront of future advancements in global
remote sensing of snow depth and snow water equivalent. Recently, snow depth
retrievals using an empirical volume scattering approach with C-band Sentinel-1 (S1)
data have been demonstrated over the European Alps and Northern Hemisphere, with
the most accurate results obtained in regions with dry, deep (>1.5 m) snowpacks and
little vegetation influence. However, these S1-based snow depth retrievals have
previously been compared only to point-based measurements or modeled snow depth
products. In this study we develop an open-source version of the S1 snow depth
retrieval technique and compare the results to spatially-distributed lidar snow depth
measurements. The highly accurate and fine resolution lidar datasets were collected
during the NASA Snow SnowEx 2020 and 2021 field campaigns at six study sites
across the western United States. These regions represent different snow environments
and characteristics than the datasets used for comparison in previous investigations.
We compare the S1 and lidar snow depths at a range of spatial resolutions and interpret
the results within the context of snowpack, vegetation, and terrain characteristics. At 90
m resolution, comparisons between lidar and S1 snow depth retrievals show low to
moderate correlations (R = 0.38) and high RMSE (0.98 m) averaged across the study
sites, with improved performance at 500 m resolution (R = 0.59, RMSE = 0.69 m). The
distribution of S1 and lidar snow depths are more similar in regions of deeper snow,
lower forest coverage, higher incidence angles, dry snow, and at coarser spatial
resolutions. Our results highlight limitations of the current S1 snow depth algorithm and
present opportunities to improve the technique for future snow depth retrievals across
varied snow environments.
How to cite: Hoppinen, Z., Palomaki, R., Tarricone, J., Brencher, G., Dunmire, D., Gagliano, E., Marziliano, A., Adebisi, N., Bonnell, R., and Marshall, H.-P.: Evaluating Sentinel-1 volume scattering based snow depth retrievals over NASA SnowEx sites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21766, https://doi.org/10.5194/egusphere-egu24-21766, 2024.
