## 5 Discussions  
The results at the three test regions (Table 3) show that the overall accuracy of LSW maps is greater than 85% and the overall Kappa coefficient is greater than 0.70. This indicates that there were some misclassifications in the LSW maps (_i.e._, Figures 2g,j,p,r, 3g,n,r, and 4c,e,g,j,n,p,r). These misclassifications are caused by overestimating in small water bodies (e.g., farm ponds) and underestimating in the slender river channels with mixed pixels (e.g., a land-water border within one pixel). This can be address using spectral unmixing [47]. One factor that may significantly affect the efficiency of LSW maps using the normalized difference water index is the threshold selection. The threshold for LSW mapping will be influenced by the subjective judgment of the user [20]. To avoid the subjectivity of the threshold selection, Otsu'sthreshold segmentation method was used to obtain the image partition thresholds in this paper. However, for large-scale regional LSW mapping, one threshold is not sufficient to extract all the water bodies from the background due to spatially varying spectral contrast. Therefore, a multi-threshold image segmentation method is required when the methods discussed in this paper are used in large-scale regional LSW mapping. Another major factor is that there is no ground-truth water body surface distribution and acreage information. To address this problem, we selected TM data (2 May 2010) and ALI data (30 September 2010) at dates during the wet (rainy) season of the study area to comparatively analyze in Region I. Importantly, the Three Gorges Reservoir water levels on 2 May 2010 and 30 September 2010, were approximately equal. That means that the water body surface distribution and acreage information of the two times of selected data are also approximately equal. Therefore, we can select the panchromatic image with the higher spatial resolution or the multispectral false-color composite images acquired at the same time as the \"true\" water body surface distribution and acreage information for Region I. In Region II, we selected TM (9 September 2005) and ALI (21 October 2005). The Gaobazhou Reservoir downstream had similar water levels on 9 September 2005 and 21 October 2005, which means that the water body surface distribution and acreage information at the two times of selected data are also similar. Therefore, we also can selected the panchromatic image or the multispectral false-color composite images as the \"true\" water body surface distribution and acreage information for Region II. In Region III, we selected ALI data and ETM+ data acquired on 29 March 2003. We also selected the panchromatic image with the higher spatial resolution as the \"true\" water body surface distribution and acreage information for Region III. Still, the true surface water distribution information is preferable to this estimate if that information can be obtained. We also noted that the TM, ETM+, and ALI sensors all have similar spectral range bands, such as Band 2 of TM/ETM+ _vs._ Band 4 of ALI, Band 5 of TM/ETM+ _vs._ Band 9 of ALI, and Band 7 of TM/ETM+ _vs._ Band 10 of ALI. These bands were all employed in different NDWI models (Table 2 and Table 3). Consequently, it is necessary to analyze the performance of different NDWI models based on the similar spectral range bands of the TM, ETM+, and ALI sensors. The ALI and ETM+ data used in Region III acquired on the same time. For this purpose, we first compared two NDWI models, namely NDWI\({}_{\text{A4},\text{9}}\) and NDWI\({}_{\text{E2},\text{5}}\), based on the green channel and SWIR channel with the spectral range of 1.550-1.750 um in Region III. The OA and Kappa of NDWI\({}_{\text{A4},\text{9}}\) are higher than those of NDWI\({}_{\text{E2},\text{5}}\) in Region III (Table 3). Then, we compared NDWI\({}_{\text{A4},\text{10}}\) and NDWI\({}_{\text{E2},\text{7}}\), using the green channel and the SWIR channel with the spectral range of 2.080-2.350 um.