Preprints
https://doi.org/10.5194/egusphere-2024-1018
https://doi.org/10.5194/egusphere-2024-1018
24 Apr 2024
 | 24 Apr 2024
Status: this preprint is open for discussion.

Evaluating Snow Depth Retrievals from Sentinel-1 Volume Scattering over NASA SnowEx Sites

Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall

Abstract. Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, sub-weekly temporal resolution, and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers, and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–21 at six study sites across the western United States as part of the NASA SnowEx Mission. Across all sites, we find poor agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements, with a mean RMSE of 0.92 m and a mean Pearson correlation coefficient R of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross-polarization backscatter ratio relative to lidar snow depths. We find a significant correlation between this cross ratio and snow depth over ~1.5 m but no relationship to a slight negative correlation for snow depths less than ~1.5 m. We attribute poor algorithm performance to a) the variable amount of apparent snow depth signal in the S1 cross ratio and b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source frame work for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more research into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume scattering-based retrievals.

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Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall

Status: open (until 08 Jun 2024)

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  • RC1: 'Comment on egusphere-2024-1018', Anonymous Referee #1, 10 May 2024 reply
  • RC2: 'Comment on egusphere-2024-1018', Anonymous Referee #2, 21 May 2024 reply
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall

Data sets

Sentinel-1 Derived Snow Depths and SnowEx Lidar Netcdfs Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dumire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall https://zenodo.org/records/10913396

Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall

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Short summary
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 9 automated snow monitoring stations.