## 1 Introduction  
Impermeable surfaces are areas dominated by man-made surfaces [1], which mainly include urban impermeable surfaces and construction sites. Due to the urban sprawl occurring globally, more and more impervious surfaces are replacing the original permeable surfaces [2]. These impervious surfaces affect the evaporation and infiltration of surface water, alter the absorption and reflection of solar radiation [3], and directly affect the hydrological cycle, surface temperature, and environmental quality of cities [4]. Therefore, accurate quantification of the percentage of impervious surfaces is important for understanding the urbanization process, urban hydrothermal cycle, and environmental detection [5, 6]. The traditional method of extracting impervious surfaces obtains information about impervious surfaces by manually identifying the relevant feature information of remote sensing graphics and distinguishing different features. This method faces many difficulties in its implementation in the underdeveloped areas due to the huge workload that requiresa significant amount of labor effort and can easily cause errors. Furthermore, the extraction process is long, inefficient, and costly [7]. The rapid development of remote sensing theory and applications has shown its great potential to achieve understanding of various socioeconomic indicators related to human activities, providing a new approach to impermeable surface mapping [2]. Unlike traditional manual mapping carried out by governments or international organizations, remote sensing measurements are highly robust against human error. Moreover, they are very effective and can provide more objective results [8]. The selection of remote sensing data with suitable spectral, temporal, and spatial characteristics is the first important step in extracting impermeable surfaces using remote sensing. Previously, Landsat Thematic Mapper (TM) images were mostly applied for impermeable surface extraction [9]. Among these images, fine spatial resolution images contain richer spatial information (e.g., land use and land cover) and image features (buildings and roads), which facilitate the estimation and mapping of impervious surface extent [10]. Common methods for impervious surface extraction using the TM images include the normalized vegetation index (NDVI) [11], categorical regression tree (CART) algorithm [9], linear spectral mixture analysis (LSMA) medium-resolution extraction method [10], artificial neural networks [12], and impervious surface mapping based on high spatial resolution images. Landsat imagery can provide rich spectral, spatial, and temporal features. However, the spectral and spatial features are not sufficient for understanding the surface coverage type [13] from Landsat images. These images generally have a revisit period of 16 days, but reliable observations for a ground scene are obtained less frequently than 16 days because of interference from clouds and snow and their shadows. In addition, the Landsat 7 satellite has missing bands in the ETM+ data due to scan line failures. Unreliable observations caused by clouds, cloud shadows, snow and ice, and stripes limit the availability and mapping accuracy of multi-temporal Landsat images [14]. Therefore, it is difficult to obtain multi-year impermeable surface information at large spatial scales using the TM imagery. With the advent of the Operational Line Scan System (OLS) of the Defense Meteorological Satellite Program (DMSP), nighttime lights were used in various applications. [PERSON] et al. discovered a positive correlation between the degree of impermeability and light intensity, and demonstrated the applicability of nighttime light data in impermeable surface detection [15]. Unlike the sensors that detect surface objects based on the reflective properties of solar radiation, nighttime light images have the appropriate spatial and temporal resolution to detect large spaces of impermeable surfaces and the dynamic process of impermeable surface expansion [16].