A new sub-pixel mapping method is presented in this paper, which makes use of multiple shifted remote sensing images to enhance the back-propagation neural network(BPNN)-based sub-pixel mapping method. Different from the original BPNN method that uses a single observed coarse spatial resolution image, the new method integrates multiple coarse spatial resolution images that are shifted from each other to determine the probability of a sub-pixel belonging to each class. The probabilities and land cover fractions are then used to allocate classes for sub-pixels. The proposed method can decrease the uncertainty and errors in BPNN-based sub-pixel mapping. Experimental results show that with both visual and quantitative evaluation, the proposed method can obtain more accurate sub-pixel mapping results.