## 1 Introduction
The general classification methods can be realized by maximum likelihood supervised classification [35], decision tree classification [3, 16], support vector machine classification [31], artificial neural network classification [30, 37], _etc._ The thematic water body information extraction methods can be generally divided into two categories according to the number of bands used: the single-band method and the multi-band method. The single-band method usually involves choosing a characteristic band of water from a multispectral image, then determining a threshold at which to discriminate water from other surfaces [33]. The multi-band methods, which are based on the spectral water index [32, 34, 38], are better at the detection of LSW body information than the single-band method is because they take advantage of reflectivity differences of each involved band and extract water-body information based on the analysis of signature differences between water and other surfaces [36]. General feature classification methods are common methods for extracting water bodies and mapping LSW. However, general classification method is based on the assumption that the pixel is pure. When multiple features are present within a single pixel, the accuracy of such classification methods will depend on the complexity of the landscape and the thoroughness of an extended training process [39]. In addition, most of these approaches are highly reliant on human expertise and knowledge of the local area. They will meet difficulties when the aim is to perform a rapid and reproducible extraction of water-body information because water bodies, such as floods, tides, and storm surges, can be fast-moving [40]. Spectral water index methods, such as the normalized difference water index, namely, McFeeters's NDWI [32], which is calculated from one green-band image and one NIR-band image, and modification of the normalized difference water index, namely, Xu's NDWI or MNDWI [34], which is calculated from one green-band image and one SWIR-band image, can extract water body information more accurately, quickly, and easily than general feature classification methods. Moreover, [PERSON]'s NDWI and [PERSON]'s NDWI have been successfully used to extract water information from remotely sensed data [18, 20, 21, 41, 42]. Therefore, we used [PERSON]'s NDWI and [PERSON]'s NDWI as the primary tools for this work on LSW mapping from ALI remotely sensed images. [PERSON]'s NDWI forms two models and [PERSON]'s NDWI forms three models as the ALI sensor can supply one green band (Band 4), two NIR bands (Bands 6 and 7), and three SWIR bands (Bands 8, 9, and 10) data. We found that these five water body information detection models produce different LSW mapping results. Hence, we need to know which NDWI model among these spectral water indices performs best for LSW mapping of the ALI sensor. In other words, we need to understand which band (green, NIR, and SWIR) or band combination is the best indicator of LSW mapping from ALI remote sensing images. Thus, we employed [PERSON]'s and [PERSON]'s NDWIs to determine which model is the best indicator for LSW mapping when ALI data are used. We analyzed the performance of LSW mapping based on TM, ETM+, and ALI data using different NDWI models in three regions within the Yangtze River Basin, China.