Continuous-based predictors of habitat characteristics derived from satellite imagery are increasingly used in species distribution models (SDM). This is especially the case of Normalized Difference Vegetation Index (NDVI) which provides estimates of vegetation productivity and heterogeneity. However, when NDVI predictors are incorporated into SDM, synchrony between biological observations and image acquisition must be questionned. Due to seasonal variations of NDVI during the year, landscape patterns of habitats are revealed differently from one date to another leading to variations in models’ performance. In this paper, we investigated the influence of acquisition time period of NDVI to explain and predict bird community patterns over France. We examined if the NDVI acquisition period that best fit the bird data depends on the dominant land cover context. We also compared models based on single time period of NDVI with one model built from the Dynamic Habitat Index (DHI) components which summarize variations in vegetation phenology throughout the year from the fraction of radiation absorbed by the canopy (fPAR). Bird species richness was calculated as response variable for 759 plots of 4 km2 from the French Breeding Bird Survey. Bird specialists and generalists to habitat were considered. NDVI and DHI predictors were both derived from MODIS products. For NDVI, five time periods in 2010 were compared, from late winter to begin of autumn. A climate predictor was also used and Generalized Additive Models were fitted to explain and predict bird species richness. Results showed that NDVI-based proxies of dominant habitat identity and spatial heterogeneity explain more bird community patterns than DHI-based proxies of annual productivity and seasonnality. We also found that models’ performance was both time and context-dependent, varying according to the bird groups. In general, best time period of NDVI did not match with the acquisition period of bird data because in case of synchrony, differences in habitats are less pronounced. These findings suggest that the most powerful approach to estimate bird community patterns is the simplest one. It only requires NDVI predictors from a single appropriate time period, in addition to climate, which makes the approach very operational.