As vegetation time evolution is one of the most relevant information to discriminate the different land cover types, land cover classification requires both temporal and spatial information. Due to the physical properties of remote sensors, this temporal information can only be derived from coarse resolution sensors such as MERIS (300 × 300 m² pixel size) or SPOT/VGT (1 km² pixel size). In this paper, we propose to use jointly high and coarse spatial resolution to perform an efficient high resolution land cover classification. The method is based on Bayesian theory and on the linear mixture model permitting, through a simulated annealing algorithm, to perform a high resolution classification from a coarse resolution time series.