Glacier mass balance evolution depends on snow accumulation and snow, firn and ice melt during the cold and warm seasons respectively and is thus considered a reliable indicator of climate fluctuations (Braithwaite & Hughes, 2020). The Little Ice Age (LIA, between the 14th and 19th centuries) represents the last global advance phase for the majority of mountain glaciers around the world (Solomina et al., 2016). Since then, the decline of glaciers has been almost continuous, only interrupted by short stabilization periods (Zemp et al., 2015). Several studies identify the 1980s as a "tipping point in global glacier evolution," followed by accelerated glacier shrinkage (Beniston et al., 2018;Huss & Hock, 2018).Very small glaciers (<0.5 km 2 ) predominate in number in the northern hemisphere mountain ranges at temperate latitudes, since more than 80% of glaciers in these mountains are beneath this area threshold . Shrinkage of very small glaciers occurred more rapidly by the late 20 th and early 21 st centuries than in earlier decades (Bahr & Radić, 2012;Parkes & Marzeion, 2018). This fast shrinkage is explained by their generally low accumulation area ratio, which is mainly driven by the observed global

In mountain areas, the phenology and productivity of grassland are closely related to snow dynamics. However, the influence that snow melt timing has on grassland growing still needs further attention for a full understanding, particularly at high spatial resolution. Aiming to reduce this knowledge gap, this work exploits 1 m resolution snow depth and Normalized Difference Vegetation Index observations acquired with an Unmanned Aerial Vehicle at a sub-alpine site in the Pyrenees. During two snow seasons (2019–2020 and 2020–2021), 14 NDVI and 17 snow depth distributions were acquired over 48 ha. Despite the snow dynamics being different in the two seasons, the response of grasslands greening to snow melt-out exhibited a very similar pattern in both. The NDVI temporal evolution in areas with distinct melt-out dates reveals that sectors where the melt-out date occurs in late April or early May (optimum melt-out) reach the maximum vegetation productivity. Zones with an earlier or a later melt-out rarely reach peak NDVI values. The results obtained in this study area, suggest that knowledge about snow depth distribution is not needed to understand NDVI grassland dynamics. The analysis did not reveal a clear link between the spatial variability in snow duration and the diversity and richness of grassland communities within the study area.

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