This paper introduces a simple yet powerful channel augmentation for visible-infrared re-identification. Most existing augmentation operations designed for single-modality visible images do not fully consider the imagery properties in visible to infrared matching. Our basic idea is to homogeneously generate color-irrelevant images by randomly exchanging the color channels. It can be seamlessly integrated into existing augmentation operations, consistently improving the robustness against color variations. For cross-modality metric learning, we design an enhanced channel-mixed learning strategy to simultaneously handle the intra- and cross-modality variations with squared difference for stronger discriminability. Besides, a weak-and-strong augmentation joint learning strategy is further developed to explicitly optimize the outputs of augmented images, which mutually integrates the channel augmented images (strong) and the general augmentation operations (weak) with consistency regularization. Furthermore, by conducting the label association between the channel augmented images and infrared modalities with modality-specific clustering, a simple yet effective unsupervised learning baseline is designed, which significantly outperforms existing unsupervised single-modality solutions. Extensive experiments with insightful analysis on two visible-infrared recognition tasks show that the proposed strategies consistently improve the accuracy. Without auxiliary information, the Rank-1/mAP achieves 71.48%/68.15% on the large-scale SYSU-MM01 dataset.