Remote sensing indices like NDVI are widely used to analyze vegetation, snow (NDSI), water dynamics (NDWI), … However, their non-linearity and bounded nature create challenges when applying common analytical methods such as averaging, resolution scaling, and trend estimation. These issues are particularly evident in mountainous and low-vegetation environments, where complex terrain, vegetation gradients, and seasonal variability further complicate interpretation.

This study examines how spatial and temporal averaging can misrepresent environmental patterns, how resolution choices influence index behavior, and why linear regression can produce misleading trends. We review existing methodologies, discuss their limitations, and explore how researchers have attempted to address these issues. By assessing these challenges, we aim to provide a clearer understanding of the implications of linear assumptions in remote sensing analyses.