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July 30, 2025Open Access

Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and physically-based model simulations

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Authors

LBLucas BoeykensGhent University HospitalDDDevon DunmireUniversity at Buffalo, State University of New YorkJJJonas-Frederik JansGhent University

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Implication

This study demonstrates improved snow depth estimation in the Alps using machine learning and meteorological data, highlighting the role of Sentinel-1 observations.

Key Points

  • Accurate snow depth estimation is crucial for water resource management, especially in the European Alps.
  • Using Sentinel-1 PolSAR observations improves snow depth accuracy compared to traditional backscatter methods.
  • Incorporating meteorological forcing data in XGBoost models further enhances snow depth estimations.
  • Spatial training data is vital for capturing topographic effects on snow depth and achieving accurate predictions.

Cite This Study

Boeykens et al. (2025) studied this question.

synapsesocial.com/papers/689a094be6551bb0af8cf133https://doi.org/10.5194/egusphere-2025-3327
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and process-based model simulations2026
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  4. 4Leveraging snow probe data, lidar, and machine learning for snow depth estimation in complex-terrain environments2025 · 2 citations
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