Abstract Seasonal foliage display (leaf phenology) is a key determinant of ecosystem function. Variation in land surface phenology, observed via space‐borne remote sensing, can be explained at broad spatial scales by climate, but we lack understanding of how vegetation structure and floristic diversity mediates these relationships. This lack of understanding hampers our ability to predict changes in phenology and therefore ecosystem function, in light of rapid ongoing shifts in biodiversity and ecosystem structure due to land use and climate change. We combined a network of 619 vegetation monitoring sites across seasonally dry tropical deciduous woodlands in Zambia with land surface phenology metrics to investigate the role of tree species diversity, composition and vegetation structure on patterns of land surface phenology, including the phenomenon of pre‐rain green‐up. Tree species diversity was associated with earlier pre‐rain green‐up, a longer growing season, and greater cumulative foliage production. Independent of diversity, proportional abundance of Detarioideae species (subfamily of Fabaceae) was associated with a longer growing season by facilitating earlier pre‐rain green‐up. Woodland stands with larger trees green up earlier, suggesting access to deep groundwater reserves. Senescence metrics showed variation among sites but were not well‐explained by precipitation, temperature, structure or diversity. Synthesis: Tree diversity, composition and structure are co‐determinants of seasonal patterns of foliage display in seasonally dry tropical deciduous woodlands, as measured via land surface phenology, at regional scale. Our study identifies both a niche complementarity effect whereby diverse woodlands exhibit longer growing seasons, as well as a mass‐ratio effect whereby detarioid species drive earlier pre‐rain green‐up, providing insights into the mechanisms underlying the biodiversity ecosystem function relationship in this biome. We stress the importance of considering biotic controls on ecosystem functioning in the next generation of earth‐system models predicting the response of communities to global change.