An integrated approach to reconstructing snow cover under clouds and cloud shadows on Sentinel-2 Time-Series images in a mountainous area.

• Optical images are easily affected by clouds and cloud shadows. • Fmask is introduced into the GEE platform and modified for Sentinel-2 images. • The improved SNOWL algorithm considers unstable snow cover areas. • Snow cover extraction accuracy in flat surface and shady slope is the highest. • Snow cover in mountainous is reconstructed by combing the Fmask, Sen2Cor and the improved SNOWL algorithms. Snow is an important type of surface cover and an important freshwater resource in the arid regions of northwest China, and optical satellites are an important means for monitoring variation in snow cover. However, the radiation values measured by satellite sensors are affected by cloud cover and cloud shadows; thus, the reconstruction of snow cover under clouds and cloud shadows becomes a bottleneck problem especially in mountains areas. Based on the digital elevation model (DEM), in this paper, an integrated method of snow cover reconstruction on Sentinel-2 images is created by combining the improved SNOWL (snow line) algorithm considering unstable snow cover areas, Fmask cloud detection algorithms, and the Sen2Cor clear sky snow cover detection tool with a geospatial processing platform, GEE (Google Earth Engine). Considering the Babao River Basin as the study area, 474 Sentinel-2 scenes (10 m) were selected to obtain the snow cover spatiotemporal variations of 158 days from November 2016 to March 2021. Compared with the high-resolution GF-2 satellite images, the integrated method can extract snow under clouds and cloud shadows well, and the improved SNOWL algorithm improves the overall accuracy from 66.05 % to 84.26 %. The experimental results show that approximately half of the Babao River Basin is unstable snow cover, and areas above 3977 m are covered with snow for at least half a year. [ABSTRACT FROM AUTHOR]