Area-averaged vegetation index (VI) depends on spatial resolution and the computational approach used to calculate the VI from the data. Certain data treatments can introduce scaling effects and a systematic bias into datasets gathered from different sensors. This study investigated the mechanisms underlying the scaling effects of a two-band spectral VI defined in terms of the ratio of two linear sums of the red and near-infrared reflectances (a general form of the two-band VI). The general form of the VI model was linearly transformed to yield a common functional VI form that elucidated the nature of the monotonic behavior. An analytic investigation was conducted in which a two-band linear mixture model was assumed. The trends (increasing or decreasing) in the area-averaged VIs could be explained in terms of a single scalar index, , which may be expressed in terms of the spectra of the vegetation and nonvegetation endmembers as well as the coefficients unique to each VI. The maximum error bounds on the scaling effects were derived as a function of the endmember spectra and the choice of VI. The validity of the expressions was explored by conducting a set of numerical experiments that focused on the monotonic behavior and trends in several VIs.