Towards operational mapping and estimation of snow cover phenology parameters in the Atlas Mountains, Morocco, using multi-sensor satellite data and Google Earth Engine.

The lack of knowledge about the temporal variability, or snow cover phenology and its spatial variation poses enormous challenges to water resource managers who mostly rely on a few weather stations with limited spatial coverage which prevents them from having a complete understanding of snow changes as a whole. Meanwhile, the free availability, wide-coverage, frequent updating, and long-term time horizon make data from programs such as Landsat and Sentinel-2 a valuable data source for reliable snow data information at an unprecedented spatial scale.

In this context, this research aims to derive the snow phenology parameters (first day of snowfall, last day of snow melt; and snow duration) over Morocco’s Atlas Mountains by combining over 10,000 images from Landsat-8 and Sentinel-2 satellites for four hydrological years (2016-2021) to create a harmonized product with a time interval of about 3 days using Google Earth Engine platform. The time series produced allowed us to create detailed maps of snow cover and extract a homogeneous normalized difference snow index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence.

  The results showed that derived seasonality snow metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be observed. The experimental results demonstrate that the proposed workflow can accurately derive snow seasonality timing with almost a day and a half delay than the in-situ observed dates and with an overall accuracy equal to 0.96.

  We expect these results to benefit various applications such as hydrological modeling, natural hazards, and regional climate change studies.