Impervious surface mapping is of great importance in urban studies. Impervious surfaces are major components of urban infrastructures and their expansion represents urban development. These surfaces mainly include built-up areas and streets; they are composed of various materials and found in diverse sizes and shapes. Impervious surface detection is challenging due to the confusion of these surfaces with other land cover classes. These confusions are not constant over different seasons, as seasonality affects the target’s responses. This study particularly focused on the seasonal effect on impervious surface detection using Sentinel-1 and Sentinel-2 images to find the optimum season. The study area is the city of Sanandaj, in the west of Iran. All processes have been executed on the Google Earth Engine as it provides a platform to access and process the satellite images. To exclude the effect of the classification algorithm on the obtained results, three commonly used classifiers have been compared; i.e., maximum likelihood, support vector machine, and neural network. The results show that spring is the best season to delineate impervious surfaces from remaining land covers, while the use of winter images does not provide acceptable results. Sentinel-2 results outperform Sentinel-1. Variation in topography and high sensitivity of SAR responses to moisture and volume structure hinder the application of Sentinel-1 images in the heterogeneous urban area. The built-up class has higher producer accuracy as compared to the street class. There was considerable confusion between the street and bare soil classes in both Sentinel-1 and Sentinel-2 images.
