A 37 years [1984–2021] Landsat/Sentinel-2 derived snow cover time-series for Switzerland

Switzerland, renowned for its mountainous landscapes, holds nearly 10% of Europe's water reserves, with 40% of its running waters originating from snowmelt. Snow plays a crucial role in the country's water management, hydroelectric power, and alpine ecosystems. It supports freshwater supply, agriculture, and tourism, making accurate snow monitoring vital for resource management and environmental preservation. Climate change, however, threatens snow cover, impacting water availability, biodiversity, and ecosystem services. Remote sensing technologies have emerged as key tools for monitoring snow cover, providing critical data for climate models, hazard prediction, and resource planning. In Switzerland, snow cover is monitored using ground-based measurements, remote sensing, and climate models, with datasets from satellites like Landsat and Sentinel-2 offering valuable insights despite challenges such as cloud obstruction. Such data are essential for hydrological modelling, agricultural monitoring, and climate studies, contributing to our understanding of global warming and aiding in natural hazard assessment. Hereafter, we present a 37-year monthly time-series of snow cover derived from Landsat and Sentinel-2 data using the Snow Observations from Space algorithm and processed in the Swiss Data Cube that facilitates the analysis, production and reuse of this Essential Climate Variable, enhancing environmental monitoring efforts at national scale.

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Publication Details

Journal
Scientific Data
Published
2025-04-15
DOI
https://doi.org/10.1038/s41597-025-04961-6
Citations
3
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
3.90

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article

A 37 years [1984–2021] Landsat/Sentinel-2 derived snow cover time-series for Switzerland

Bruno Chatenoux, Charlotte Poussin, Grégory Giuliani, Pascal Peduzzi
3 citations
Scientific Data
Cryospheric studies and observations
3.90
article

A 37 years [1984–2021] Landsat/Sentinel-2 derived snow cover time-series for Switzerland

Bruno Chatenoux, Charlotte Poussin, Grégory Giuliani, Pascal Peduzzi
article en
3 citations

Abstract

Switzerland, renowned for its mountainous landscapes, holds nearly 10% of Europe's water reserves, with 40% of its running waters originating from snowmelt. Snow plays a crucial role in the country's water management, hydroelectric power, and alpine ecosystems. It supports freshwater supply, agriculture, and tourism, making accurate snow monitoring vital for resource management and environmental preservation. Climate change, however, threatens snow cover, impacting water availability, biodiversity, and ecosystem services. Remote sensing technologies have emerged as key tools for monitoring snow cover, providing critical data for climate models, hazard prediction, and resource planning. In Switzerland, snow cover is monitored using ground-based measurements, remote sensing, and climate models, with datasets from satellites like Landsat and Sentinel-2 offering valuable insights despite challenges such as cloud obstruction. Such data are essential for hydrological modelling, agricultural monitoring, and climate studies, contributing to our understanding of global warming and aiding in natural hazard assessment. Hereafter, we present a 37-year monthly time-series of snow cover derived from Landsat and Sentinel-2 data using the Snow Observations from Space algorithm and processed in the Swiss Data Cube that facilitates the analysis, production and reuse of this Essential Climate Variable, enhancing environmental monitoring efforts at national scale.

Scientific DataVol. 12(1)
University of Geneva (CH)
Université de Genève, Universität Zürich, Swiss Federal Institute for Forest, Snow and Landscape Research, Bundesamt für Umwelt
Openalex Percentile: Top 7%
Cryospheric studies and observations
3.90
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A 37 years [1984–2021] Landsat/Sentinel-2 derived snow cover time-series for Switzerland — Bruno Chatenoux, Charlotte Poussin, et al. · Scientific Data (2025) | TGRS Research Map | TGRS