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              <p>Abstract. Information on the spatial–temporal variability of seasonal snow cover duration over long time periods is critical for studying the responses of mountain ecosystems to climate change. However, this information is often lacking due to the sparse distribution of in situ observations or the lack of adequate remote sensing products. Here, we combined snow cover data from 10 different optical platforms, i.e. SPOT (Satellites Pour l'Observation de la Terre) 1–5, Landsat 5–8, and Sentinel-2A and Sentinel-2B, to build a time series of the annual snow melt-out day (SMOD, i.e. the first day of no snow cover) at 20 m resolution across the French Alps and Pyrenees (43 × 103 km2). We evaluated the pixel-wise accuracy of the computed SMOD using in situ snow measurements at 276 stations. We found that the residuals are unbiased (median error of 1 d) despite a dispersion (RMSE of 28 d), which suggests that this dataset can be used to study SMOD trends after spatial aggregation. We found average reductions of 21.4 d (5.78 d per decade) over the French Alps and 16 d (4.33 d per decade) over the Pyrenees over the period 1986–2023. The SMOD reduction is robust and significant in most parts of the French Alps and can reach 1 month above 3000 m. The trends are less consistent and more spatially variable in the Pyrenees. This dataset is available for future studies of mountain ecosystem changes and is updated every year using Sentinel-2 data.</p>
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