Sugarcane plantations are mainly distributed in mountainous areas with high land fragmentation and complex cropping structure in Yunnan Province. Frequent cloud cover reduces the good observations of the land cover by using optical remote sensing. So it is difficult to extract sugarcane plantations with a high accuracy based on satellite optical remote sensing data. Xinping Country, a typical mountainous sugarcane plantation region, was chosen to explore a suitable method for the extraction of sugarcane plantations in mountainous areas. In this study, Landsat-8 and Sentinel-2 optical imagery for Xinping from October 1, 2019 to July 1, 2021 were used as the main data sources, and the DEM data and field survey data were used as the auxiliary data. The synthetic time-series images with high spatial-temporal resolution were constructed on Google Earth Engine (GEE). Firstly, we analyzed the differences among sugarcane and evergreen vegetation, water body, impervious, and other crops in spectral index characteristics, phenological characteristics, and topographic characteristics. Secondly, we determined the optimal thresholds for extracting sugarcane plantations for the four phenological parameters including rise time, fall time, above integral of season and below integral of season, as well as elevation and slope factors based on the training samples. Thirdly, we mapped the sugarcane plantations of 2020 and the mapping accuracy was verified using the validation samples in the study area. Finally, the spatial distribution of sugarcane plantations was analyzed at town scale. The results showed that synthetic time-series images based on the Landsat-8 and Sentinel-2 optical imagery could increase the number of good observations in the study area and improve the spatial resolution of the images, which could solve the problem of low quality of remote sensing images in mountainous areas and could better monitor phenological characteristics and seasonal changes of vegetation. The resultant 2020 sugarcane map had overall, user and producer accuracy of 97.07%, 88.85% and 80.57%, respectively with the Kappa coefficient of 0.83. According to the annual sugarcane map in 2020, there was a total of 7705 hm² sugarcane in Xinping, there were more sugarcane distribution in the southeast than in the northwest, as well as the southeast terrain was lower than that in the northwest. There were significant differences in the area of sugarcane plantations between townships, the township with the largest sugarcane plantation area is Mosha Town (2786 hm²) and the township with the smallest sugarcane plantation area is Gucheng district (0.87 hm²), which is consistent with the actual research. The sensitivity analysis of phenology parameters for sugarcane mapping demonstrated that the four parameters including rise time, fall time, above integral of season, and below integral of season are all important to improve the user accuracy and reduce the commission errors in the sugarcane mapping. The sugarcane plantation mapping algorithm proposed in this study could provide a reference for the extraction of sugarcane plantations in the complex landscapes of mountainous areas in the future.