Abstract Climate‐driven phenological mismatches have the potential to disrupt plant–pollinator interactions, emphasizing the need to uncover drivers behind spatial and temporal dynamics of floral resource availability. This is especially important in habitats such as mountain meadows, where climate change is not only likely to have outsized impacts, but topographic complexity creates a mosaic of microclimate and habitat heterogeneity. We investigated the impacts of elevation, canopy cover, and their interaction on the temporal availability of floral resources by deploying 35 trail cameras in open and forested habitats below and near the tree line in the Swiss Alps. We hypothesized that tree cover would lower species richness and floral abundance, especially at high elevations where low light might interact with harsh climates. However, we also hypothesized that a mosaic of open and forested habitats at any elevation may offer temporal benefits to pollinators by extending the flowering season and potentially providing complementary flower resources during critical life history phases. We applied machine learning approaches to images to extract first and last flowering dates, overall flowering duration, and flowering species richness, and then tested how these flowering metrics varied by site (low vs. high) and canopy categories (open vs. closed) and their interactions. We also explored temporal changes in species richness and the individual flowering phenology of the most abundant species. We found that canopy cover extended the entire flowering period while higher elevations shortened it, with both factors delaying the start of the flowering season. Flowering species richness was highest at the tree line, and floral abundance increased at and above the tree line relative to lower elevations. These results highlight the complex interactions between habitat structure and elevation in influencing flowering phenology and flower resource diversity. Understory wildflowers emerge as a potentially complementary resource for pollinators in mountain ecosystems, potentially benefiting them during the early season. This work also highlights the benefit of combining machine learning technologies with automated image capture (in our case, wildlife cameras) that allowed us to quantify phenology at an extremely fine temporal scale.