The use of remotely sensed data for ecological applications (e.g. prediction of species richness) has increased in recent years. We present new opportunities to identify and characterize landscapes at a fine scale with remote sensing data more specifically, we focus on the monitoring of the intra and inter-annual dynamics of vegetation covers using information related to their biophysical properties (i.e. Leaf Area Index) using optical images or to structural properties (i.e. vegetation density) using SAR (radar) images. Landscape heterogeneity and the amount of semi natural habitats areas have been shown to be major drivers of biodiversity in agricultural landscape. However, little is known on the effect of 1) the intra-annual dynamic of the agricultural mosaic and 2) the interfaces between covers. We tested the hypothesis that interface types and their intra-annual dynamics (i.e. crop phenology) measured at different spatial scales influence directly the intra-annual changes in species composition. We evaluated the potential of indicators (i.e. crop phenology, hedgerow structures) derived from remotely sensed data at two scale –local and landscape scales- to explain the intra-annual dynamic of species composition (carabid beetles) in agricultural landscape. The investigated area is the 'Pleine Fougères' site of theLong Term Ecological Research zone Armorique , located in the southern part of the Bay of the Mont-Saint-Michel, France. Carabid beetles were sampled in 45 Maize fields using pitfall traps from April to October 2012. The Maize field plots were chosen to maximize the diversity of local and landscape environments (sparse hedgerows, dense hedgerows, crop interfaces,...). This study shows the high potential of remotely sensed data for modeling biodiversity in agricultural landscapes. At local and landscape scales, intra-annual changes in species composition are higher in field plots surrounding by winter crops than in fields plots surrounding by dense hedger