High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images

Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such is vital for water supply management regulation by national stakeholders. Recent advances in remote sensing have made it possible reliably estimate quantify the spatial temporal variability of at different scales. However, because technological constraints, there a compromise between temporal, spectral, resolutions available satellites. In addition, atmospheric conditions cloud contamination may increase number missing satellite observations. Therefore, data from single insufficient accurately capture dynamics, especially semi-arid areas where snowfall extremely variable both time space. Considering these limitations, combined use next generation multispectral sensor Landsat-8 (L8) Sentinel-2 (S2), with resolution ranging 10 30 m, unprecedented opportunities enhance mapping. Hence, purpose this study examine effectiveness optical sensors through image fusion techniques capturing producing detailed dense normalized difference index (NDSI) series within context. Three models include enhanced adaptive reflectance model (ESTARFM), flexible spatio-temporal (FSDAF), pre-classification (pre-classification FSDAF) were tested compared merge L8 S2 data. The results showed that FSDAF generates most accurate precise fused NDSI images retains detail other models, root mean square error (RMSE = 0.12) correlation coefficient (R 0.96). Our reveal that, high-resolution merged can compensate lack ground-based