  A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to
map snow/ice cover. The NBSI-MS is based on the spectral characteristics of
different Land Cover Types (LCTs) such as snow, water, vegetation, bare land,
impervious, and shadow surfaces. This index can increase the separability
between NBSI-MS values corresponding to snow from other LCTs and accurately
delineate the snow/ice cover in non-binary maps. To test the robustness of the
NBSI-MS, Greenland and France-Italy regions were examined where snow interacts
with highly diversified geographical ecosystem. Data recorded by Landsat 5 TM,
Landsat 8 OLI, and Sentinel-2A MSI satellites have been used. The NBSI-MS
performance was also compared against the well-known NDSI, NDSII-1, S3, and SWI
methods and evaluated based on Ground Reference Test Pixels (GRTPs) over
non-binarized results. The results show that the NBSI-MS achieves overall
accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same
range as OA. The precision assessment confirms the performance superiority of
the proposed NBSI-MS method for removing water and shadow surfaces over the
compared relevant indices.
