Generative Adversarial Networks (GAN) have been used for both image generation and image style translation. In this paper, we aim to apply GANs to multispectral satellite image. For the image generation, we take advantage of the progressive GAN training methodology, that is purposely modified to generate multi-band 16 bits satellite images that are similar to a Sentinel-2 level-1C product. The generated images that we obtained imitate closely the spectral signatures of the kind of terrain in the images, as it can be seen by comparing typical spectral view between synthetic and natural images. Furthermore, we consider the recent use of GAN architectures for transferring the style of the images and apply them to perform land-cover transfer of satellite images. Specifically, we used the unpaired style transfer method to modify images that are dominant in vegetation land cover into images that are dominated by bare land cover and vice versa. The land-cover transfer via GANs gives very promising results and the visual quality for the transferred images is also satisfactory, showing that the land-cover transfer is an easier task compared to the GAN generation from scratch. Especially, results are good when the target domain is bare land, in which the visual quality for the transferred images is also very good.