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Abstract In the Alps, snow cover dynamics can be monitored using Earth observation (EO). However, low revisit frequency and cloud cover pose a challenge to long‐term time series analysis using high spatial resolution EO images. In this study, we applied the random forest regression to model regional snowline elevations (RSEs). In this manner, daily snowline dynamics and their long‐term trends can be derived, despite the aforementioned challenges. Of the six investigated Alpine catchments between 1984 and 2018, a significant increasing trend of RSEs is shown in four catchments in the early ablation seasons (between 5.38 ± 2.64 and 11.29 ± 4.79 m·a −1 ) and five catchments in the middle ablation seasons (between 4.17 ± 2.62 and 8.76 ± 4.42 m·a −1 ). On average, the random forest regression models can explain 75% of the RSE variations. Furthermore, air temperature was found influential in snow persistence especially during middle and late ablation seasons.
Hu et al. (Sun,) studied this question.