The high spatial and temporal resolution Sentinel-2 images are increasingly becoming the primary remote sensing data source for surface water extraction.A comparative study of the extraction effects of various water index methods based on this satellite image is a significant reference value for improving surface water’s remote sensing monitoring capability. In this study， the seven water indexes （NDWI， MNDWI， AWEInsh， AWEIsh， WI2015， CDWI and MNDWI_VIs） are used to extract surface water from four sample areas with different combinations of surface water types in North China， Northeast China， the middle and lower reaches of the Yangtze River and Northwest China.The water indexes’ accuracy is quantified using Sentinel-2 MSI images on the GEE （Google Earth Engine） platform. The results show that， all seven water indexes generally can identify surface water well， but there are some differences in performance when extracting different types of surface water bodies； the NDWI index underestimate the distribution of surface water in transient water bodies （e.g.， paddy fields， floodplains， etc.） and have a high miss-score speed； while the AWEInsh， AWEIsh and WI2015 indexes have an overall tendency to overestimate and have a high miss-score rate； the MNDWI_VIs water index maintains the highest extraction accuracy in areas with complex water index； in the field of monitoring water changes in long time series， the performance of the seven water bodies is generally consistent with the conclusions obtained based on single-view imagery. This study provides an essential scientific basis for carrying out surface water monitoring in different water bodies.