The elevational patterns of plant β diversity and their underlying drivers are known to be scale-dependent, but pinpointing the spatial scales at which different ecological processes occur remains challenging using traditional field inventory methods. Remote sensing has emerged as a promising alternative, providing continuous spatial data for monitoring plant diversity. In this study, we used field inventory data and corresponding Sentinel-2 images from a subtropical mountain to simulate pooled assemblages and assess the potential of using multispectral satellite images in predicting tree β diversity. We further examined the scale-dependent elevational gradient of the spectral β diversity and identified primary topographic variables across different spatial extents (0.16–64 ha). The spectral β diversity showed a consistently positive relationship with the inventory β diversity calculated using various indices (average pairwise Sørensen, Jaccard, and Bray–Curtis dissimilarities, as well as multi-community differentiation measures based on Hill numbers), with the strongest correlation observed for abundance-weighted indices and images from early spring and late autumn (R²_max = 0.63). However, a null model-derived β deviation showed only a weak correlation between remote sensing and field-based measures. A declining trend in the spectral β diversity with an increasing elevation was observed and became more pronounced at larger extents. The topographic heterogeneity, represented by the slope and northness, explained the elevational gradients at spatial extents >4 ha, attesting the significant role of environmental filtering in shaping plant diversity patterns, even at fine scales. While the northness was more influential at smaller spatial extents (4 ha). This study showcases the potential of using readily available remote sensing data to address difficult questions in plant diversity research.