Forests cover about 30 percent of the earth surface, they are the most biodiverse terrestrial ecosystems and they are at the base of many ecological processes and services. The loss of forest biodiversity makes in risk the benefits that the humans derived from theme. The assessment of biodiversity is therefore an important and essential goal to achieve, that however can result difficult, time consuming and expensive if estimated through field data. Through the remote sensing it is possible to estimate in a more objectively way the species diversity, using limited resources, covering broad surfaces with high quality and standardized data. One of the method to estimate biodiversity from remote sensing data is through the Spectral Variation Hypothesis (SVH) , which states that the higher the spectral variation of an image, the higher the environmental heterogeneity and the species diversity of that area. The SVH has been tested using different indexes and measures; recently in literature, the Rao’s Q index, applied to remote sensing data has been theoretically tested as a new and innovative spectral variation measure. In this paper for the first time, the SVH through the Rao’s Q index has been tested with an NDVI time series derived from the Sentinel 2 (with a spatial resolution of 10m) and Landsat 8 satellites (spatial resolution of 30m) and correlated with data of species diversity (through Shannon’s H) collected in forest. The results showed that the Rao’s Q is a grateful spectral variation index. For both the sensors, the correlation with the field data had the same tendency as the NDVI trend, reaching the highest value of correlation (through the coefficient of determination R2) in June, when the NDVI was at its peak. In this case the correlation reached a value of R2=0.61 for the Sentinel 2 and of R2=0.45 for the Landsat 8, showing that the SVH is scale and sensor dependent. The SVH tested with optical images through the Rao’s Q index showed grateful and promising results in alpine forests and could lead to as much good results with other remote sensing data or in other ecosystems.