Deep neural networks have recently shown outstanding performances in remote sensing image classification. The success of these models is highly reliant on the availability of large collections of hand-labeled training images which are usually scarce. Data augmentation mitigates this scarcity by enriching labeled training sets using different geometric and photometric transformations, or by relying on deep generative models. In this paper, we investigate the potential of generative models, and particularly normalizing flows (NFs), in remote sensing image augmentation and classification. The main contribution relies on a novel conditional NF model that achieves a bidirectional mapping of images between ambient and latent spaces with the particularity of learning disentangled multi-modal distributions through image classes. The proposed NF also achieves nonlinear augmentations in highly intricate ambient spaces by mapping images to latent spaces where augmentations become linear and more tractable. Extensive experiments conducted on the EuroSAT benchmark show the benefit of our NF-based augmentation when learning vision transformers.