Classifiers trained on airborne hyperspectral imagery are proficient in identifying tree species in hyperdiverse tropical rainforests. However, spectral fluctuations, influenced by intrinsic and environmental factors, such as the heterogeneity of individual crown properties and atmospheric conditions, pose challenges for large-scale mapping. This study proposes an approach to assess the instability of airborne imaging spectroscopy reflectance in response to environmental variability. Through repeated overflights of two tropical forest sites in French Guiana, we explore factors that affect the spectral similarity between dates and acquisitions. By decomposing acquisitions into subsets and analyzing different sources of variability, we analyze the stability of reflectance and various vegetation indices with respect to specific sources of variability. Factors such as the variability of the viewing and sun angles or the variability of the atmospheric state shed light on the impact of sources of spectral instability, informing processing strategies. Our experiments conclude that the environmental factors that affect the canopy reflectance the most vary according to the considered spectral domain. In the short wave infrared (SWIR) domain, solar angle variation is the main source of variability, followed by atmospheric and viewing angles. In the visible and near infrared (VNIR) domain, atmospheric variability dominates, followed by solar angle and viewing angle variabilities. Despite efforts to address these variabilities, significant spectral instability persists, highlighting the need for more robust representations and improved correction methods for reliable species-specific signatures.