In this study, we proposed a method for intercalibration between DMSP/OLS radiance calibrated images and NPP/VIIRS images based on stable pixels, while the challenge is that these two datasets do not have overlapped periods which makes it difficult to find the stable pixels. In this study, the outliers, which are the changed pixels, were iteratively removed considering the radiometric and spatial differences between the two kinds of images, and then the stable pixels were obtained. Using the stable pixels, the DMSP/OLS radiance calibrated images and NPP/VIIRS images were intercalibrated based on a regression model. Taking mainland China as an experimental region, we obtained DMSP-like VIIRS images from 2013 to 2019. The growth of night-time light (NTL) from 2010 to later years (e.g., 2013–2019) in provincial regions of mainland China is well correlated to the growth of gross domestic product (GDP), and the averaged ${R^2}$ between the two variables is 0.7126. The growth of NTL from 2010 to later years (e.g., 2013–2019) in 236 municipal regions is also well correlated to the growth of GDP, and the averaged ${R^2}\ $ is 0.5974. Compared with a multitemporal logistic model which also aims to intercalibrate the two kinds of images, our method showed better a better performance for the intercalibration as our method can generate time series NTL images with stronger correlation to economic growth. Finally, we also found that the NTL, derived from the intercalibrated images, is growing faster in the area where urbanization is faster. This study shows that the proposed method is effective for the intercalibration between the two sets of images which do not have an overlapped time series.