Preprints
https://doi.org/10.5194/tc-2021-74
https://doi.org/10.5194/tc-2021-74

  12 Mar 2021

12 Mar 2021

Review status: a revised version of this preprint is currently under review for the journal TC.

Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps

Hans Lievens1, Isis Brangers1, Hans-Peter Marshall2, Tobias Jonas3, Marc Olefs4, and Gabriëlle De Lannoy1 Hans Lievens et al.
  • 1Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
  • 2Department of Geosciences, Boise State University, Boise, ID, USA
  • 3WSL - Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 4ZAMG - Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria

Abstract. Seasonal snow in mountain regions is an essential water resource. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) from regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We demonstrate the ability of the Sentinel-1 mission to monitor weekly snow depth at sub-kilometer (100 m, 300 m and 1 km) resolutions over the European Alps, for 2017–2019. Sentinel-1 backscatter observations, especially for the cross-polarization channel, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in a change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. For dry snow conditions, the 1 km Sentinel-1 retrievals have a spatio-temporal correlation (R) of 0.87 and mean absolute error (MAE) of 0.17 m compared to in situ measurements across 743 sites in the European Alps. A slight reduction in performance is observed for the retrievals at 300 m (R = 0.85 and MAE = 0.18 m) and 100 m (R = 0.79 and MAE = 0.21 m). The results demonstrate the ability of Sentinel-1 to provide regional snow estimates at an unprecedented resolution in mountainous regions, where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resources management, flood forecasting and numerical weather prediction.

Hans Lievens et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-74', Anonymous Referee #1, 09 Apr 2021
    • AC1: 'Reply on RC1', Hans Lievens, 15 Apr 2021
      • CC1: 'Reply on AC1 regarding', Joshua King, 19 Apr 2021
    • AC3: 'Reply on RC1', Hans Lievens, 02 Jun 2021
  • RC2: 'Comment on tc-2021-74', Anonymous Referee #2, 20 Apr 2021
    • AC4: 'Reply on RC2', Hans Lievens, 02 Jun 2021
  • CC2: 'Comment on tc-2021-74', Helmut Rott, 30 Apr 2021
    • AC2: 'Reply on CC2', Hans Lievens, 03 May 2021

Hans Lievens et al.

Hans Lievens et al.

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Short summary
Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resources management, flood forecasting and numerical weather prediction.