The surface water extraction algorithm based on satellite remote sensing images is advantageous as it is able to obtain surface water information in a relatively short time. However, when it is used to extract information on surface water in large-scale, long-time series and complex terrain areas, there will be a large number of misclassified pixels, and a large amount of image preprocessing work is required. The accuracy verification is time-consuming and laborious, and the results may not be accurate. The complex climatic and topographic conditions in Bosten Lake Basin make it more difficult to monitor and control surface water bodies. Therefore, based on the GEE (Google Earth Engine) cloud platform, and the studies of the effect of nine kinds of water indexes on the surface water extraction in Bosten Lake Basin, this paper adds a slope mask to remove misclassified pixels and finds the best extraction method of surface water extraction in the basin by means of accuracy verification and visual discrimination through continuous iteration of index threshold and slope mask threshold. The results show that when the threshold value is −0.25 and the slope mask is 8 degrees, the index WI2019 has the best effect on the surface water information extraction of Bosten Lake Basin, effectively eliminating the interference of shadow and snow. The effect of water extraction in the long-time series is discussed and it was found that the precision of water extraction in the long-time series is also better than other indexes. The effects of various indexes on surface water extraction under complex terrain are compared. It can quickly and accurately realize the long-time series of surface water extraction under large-area complex terrain and provides useful guiding significance for water resources management and allocation as well as a water resources ecological assessment of Bosten Lake Basin.
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