Land cover classification plays a crucial role in land resource monitoring and planning. Recently, deep learning-based methods are becoming the dominating method for precise land cover mapping. However, the large-scale application of them is deeply hindered by the domain shift between different images, which is easily caused by illumination, climate, regional divergence, and so on. With the aim to cope with the problem of domain shift, many domain adaptation (DA) methods have been provided and great achievements have been made, especially the newborn adversarial DA, which usually contains a generator and a discriminator. Among these methods, the pixel-level methods are of high memory consumption, whereas feature-level methods are found hard to decode the structured information for semantic segmentation tasks due to the lack of low-dimensional information. Therefore, we propose an adversarial domain adaptation framework with Kullback–Leibler constraint (KL-ADDA) for remote sensing land cover classification. A state-of-the-art (SOTA) semantic segmentation network is utilized as the generator, which directly outputs the segmentation results to the discriminator to retain more low-level information. Besides, a Kullback–Leibler (KL)-divergence is calculated to improve the discriminative ability of the discriminator and thus enhance the generator’s performance. Experiments on the international society for photogrammetry and remote sensing (ISPRS) data set and two simulated target data sets have shown the effectiveness of KL-ADDA for DA.