## 1 Introduction  
Impervious surfaces are artificial surfaces that water cannot infiltrate to reach the soil, such as parking lots, streets, and highways [1]. Changes in land use and land cover caused by the expansion of impervious surfaces are likely to influence the regional climate [2]. [PERSON] et al. and [PERSON] et al. pointed out that the impervious surface area and land surface temperature are positively correlated, and the expansion of impervious surfaces will result in the increase of the land surface temperature which may cause uneven distribution of urban heat and trigger the urban heat island effect [3,4]. Accurate and fast extraction methods for impervious surface distribution are important which is helpful for detecting regional environmental changes in urban areas and achieving sustainable urban development. They can be divided into two categories, namely, field surveys and remote sensing [1]. Field surveys can provide more detailed information on impervious surface distribution but are time-consuming, laborious, and difficult to apply to the assessment of large areas. In contrast, widely-used remote sensing methods can extract impervious surface distribution quickly, and sources of commonly used remote sensing images include the QuickBird [5], WorldView-2 [6], and Landsat satellites [7], etc. When extracting impervious surface distribution in a large urban area, Landsatimagery is often the primary choice because of its medium spatial resolution and free availability, while remote sensing imagery with fine spatial resolution is subject to economic constraints. Remote sensing methods used in the extraction of impervious surface distribution include index methods, spectral mixture analysis (SMA), and classification methods. Firstly, index methods construct a feature index based on the spectral difference between impervious surfaces and other ground objects and have been a popular research focus because of their simplicity in calculation. [PERSON] et al. constructed the normalised difference built-up index (NDBI) on the basis of the near-infrared and shortwave-infrared bands [7]. [PERSON] proposed the index-based built-up index (IBI), which is based on the NDBI, soil-adjusted vegetation index (SAVI) and modified normalised difference water index (MNDWI) [8]. [PERSON] constructed the normalised difference impervious surface index (NDISI) on the basis of the thermal band, MNDWI, near-infrared band, and shortwave-infrared band [9]. [PERSON] et al. devised the normalised difference impervious index (NDII), which is based on the visible and thermal bands [10]. [PERSON] et al. developed the combinational built-up index (CBI) by combining the first principal component (PC1), normalised difference water index (NDWI), and SAVI [11]. Secondly, SMA calculates the fractions of land cover types in a pixel and models a mixed spectrum as a combination of the spectra for pure types of land cover, and it includes linear SMA (LSMA) and nonlinear SMA (NLSMA). [PERSON] et al. utilised LSMA to extract the impervious surface distribution [12]. [PERSON] et al. revised LSMA using principal component analysis and the NDBI [13]. [PERSON] et al. proposed multiple-endmember spectral mixture analysis [14], which was improved by [PERSON] et al. to achieve higher efficiency and precision [15]. Finally, classification methods divide pixels into impervious and permeable surfaces according to the features of the image, and commonly used classifiers include support vector machines (SVMs) [16], artificial neural networks (ANNs) [17], and random forest (RF) [18], etc. [PERSON] et al. extracted impervious surface distribution in Malaysia by an SVM [19]. [PERSON] and [PERSON] used the ANN method [20]. [PERSON] et al. utilised the RF method to extract impervious surface distribution in France and compared it with the SVM method [21].