This paper presents the analyses of texture variables from the image enhanced using super-resolution mapping. Two widely known super-resolution mapping techniques, pixel swapping and Hopfield neural network are used. The texture analyses include land cover patches of varying sizes, shapes, and spatial pattern of patches. A time series coarse MODIS 250 images are used to improve the representation of land cover patches and reduce the spatial variability. Results show that using a fusion of time series images and properly setting the weights for the Hopfield neural network produce superior accuracy of representing the texture of land cover mapping.