###### Abstract
The construction of the P-index integrates the Impervious Surface Index, the Index-Based Build-Up Index (IBI), and the inverted impervious surface information, which is extracted with high accuracy, but is difficult to compute and is poorly operationalized [14]. Biophysical Composition Index (BCI), Comprehensive Cumulative Index (CBI), and IBI Construction Index belong to the triple index method, and the problem of confusing information on impervious surfaces using this algorithm still exists, making it difficult to ensure accuracy [15; 16]. The index method is simple and easy to compute, and can quickly and accurately extract impervious surfaces, but the index construction process is complicated and has low generalizability; in addition, due to the limitation of spatial resolution, there are mixed pixels in the image, which can easily lead to misclassification of features. Conventional remote sensing classification methods, such as the maximum likelihood classification method, can also be used for impervious surface extraction. Spectral mixing analysis utilizes a chunking method that allows for the determination of the proportions of the spectral components of different features in the same image element. [PERSON] et al. proposed a new surface model called the vegetation-impervious-soil (V-I-S) model [17]. The model views the surface as an assemblage of vegetation, impervious surfaces, and soils, and attributes the spectral response of different surface types to the relative proportions of these three components. Based on this idea, [PERSON] et al. developed and tested the Multiple Endmember Spectral Mixture Analysis (MESMA) [18]. [PERSON] et al. improved the linear spectral decomposition method by dividing the surface into four categories: vegetation, soil, high-albedo features, and low-albedo features, and summing the high-albedo and low-albedo features to obtain the urban distribution in the Columbus area [19]. Spectral mixing analysis solves the problem of mixing pixels on low- and medium-spatial-resolution images to a certain extent, but there are still limitations in this method: the feature types in the city are complex, the spectral heterogeneity of impervious surfaces is strong, and the selection of endpoints is subjective, especially for the selection of endpoints in large areas, which is more difficult. At the same time, there are errors in the decomposition results of different features and coverage in the hybrid image element, which can easily lead to the underestimation of areas with high coverage of impervious surfaces easily using the SMA method, and the overestimation of areas with low coverage [20]. Maximum likelihood classification is a conventional classification method based on probabilistic discriminant rules; for a particular image element in a predetermined N classification training dataset, it needs to calculate the probability that the image element belongs to each class in the dataset, and then attribute this image element to the class with the highest probability among them [21]. In summary, it can be seen that the key to ensuring the accuracy of impervious surface extraction is the ability to distinguish the impervious surface from bare soil. Utilizing the Impervious Surface Index to determine impervious surface areas in densely built-up areas effectively reduces the probability of bare soil being misclassified as an impervious surface. It can effectively ensure the accuracy of impervious surface extraction in the area. The brightness information of nighttime lighting data can directly reflect the location of built-up areas within the study area; therefore, the comprehensive application of nighttime lighting data and remote sensing image data can effectively improve the accuracy of impervious surface extraction [22]. [PERSON] et al. established a global impervious surface model based on DMSP-OLS and TM data [23]. [PERSON] et al. integrated spatial and spectral information from nighttime lights and Landsat TM images to realize the identification of impervious surfaces [24]. [PERSON] et al.