Nighttime conditions pose a significant challenge to color constancy due to the diversity of lighting conditions and the presence of substantial low-light noise. Existing color constancy methods struggle with nighttime scenes, frequently leading to imprecise light color estimations. To tackle nighttime color constancy, we propose a novel unsupervised domain adaptation approach that utilizes labeled daytime data to facilitate learning on unlabeled nighttime images. To specifically address the unique lighting conditions of nighttime and ensure the robustness of pseudo labels, we propose adaptive channel masking and reflective uncertainty. The adaptive channel masking is designed to guide the model to progressively learn features that are less influenced by variations in light colors and noise. Moreover, with our reflective uncertainty providing pixel-wise uncertainty estimation, our model can avoid learning from incorrect labels. Our model demonstrates a significant improvement in accuracy, achieving 20% lower Mean Angular Error (MAE) compared to the state-of-the-art method on our nighttime dataset.