In this paper, we present a new method for subpixelic land-cover change detection using coarse resolution time series, as they offer a high time-repetitiveness of acquisition. Changes are detected by analyzing the coherence between a coarse resolution time series and a high resolution classification as a description of the land-cover state at the date of reference. To that aim, an a contrario model is derived, leading to the definition of a probabilistic coherence criterion free of parameter and free of any a priori information. This measure is the core of a stochastic algorithm that selects automatically the image sub-domain representing the most likely changes. Some particular problems related to the use of time series are discussed, such as the potential high variability of a time series or the problem of missing data. Some experiments are then presented on pseudo-actual data, showing a good performance for change detection and a high robustness to the considered resolution ratio (between the high resolution classification and the coarse resolution time series).