## 1 Introduction  
Due to a combined influence of natural processes and human activities, the global land cover is changing rapidly [1]. With the advancement in multisource remote sensing data, land cover and its changes have been closely studied in recent years [2]. Accurate and reliable land cover data is indispensable, as it provides basic information related to scientific research in areas such as agricultural, environmental protection, and global change [3; 4; 5; 6]. However, accurate land cover mapping and its changes are still facing many challenges due to land surface heterogeneity and spectral confusion, especially in higher-resolution mapping. Since the late 1990s, many land cover data products with different resolutions have been developed, including the University of Maryland (UMD) classification [7], the International Global Biosphere Programme (IGBP) DISCover [8], Global Land Cover 2000 (GLC2000) [9], MCD12 QI [10], GlobCover [11], and the Climate Change Initiative-Land Cover (CCI-LC) [12]; however, the resolution of these land cover products are low, ranging from 300 m to 1 km. Copernicus Global Land Service-Land Cover 100 (CGLS-LC100) delivers a global land cover map at 100 m spatial resolution. Since 2015, three collections of CGLS-LC100 have been released [13]. CORINE Land Cover (CLC) is a standard dataset for land cover in Europe. There are now five versions, including CLC-1990, CLC-2000, CLC-2006, CLC-2012, and CLC-2018. The quality and accuracy of datasets have been greatly improved [14]. The Google Earth Engine (GEE) is a global geospatial analysis platform that was developed based on cloud technology [15]. Due to its powerful computing functions and the advantages of online data calculations and visual analysis, it has a wide range of applications in land cover mapping research. Based on the high spatial and temporal resolution of the remote sensing data provided by the GEE, different high-resolution land cover data products have been developed. The representative land cover products on a global scale include the following. [PERSON] et al. used a Landsat time series and HJ-1 satellite remote sensing images to produce a global land cover product (GlobeLand30) with a spatial resolution of 30 m [16]. [PERSON] et al. used Landsat TM/ETM+ data and Sentinel-2 data, respectively, for the fine resolution (30 m) observation and monitoring of the global land cover (FROM-GLC30) [17] and the fine resolution (10 m) observation and monitoring of the global land cover (FROM-GLC10) [18] based on the GEE platform. [PERSON] et al. produced two global land cover products with high spatial resolutions; i.e., the Global Land Cover with Fine Classification System in 2015 (GLC_FCS30-2015) and the Global Land Cover with Fine Classification System in 2020 (GLC_FCS30-2020) [19; 20]. [PERSON] et al. used the open validation dataset (LUCAS) to evaluate the performance of the GLC_FCS30-2015 and compared it with the GlobeLand30-2010 and FROM_GLC-2015 [21]. Among the land cover products listed above, MCD12Q1, GlobCover, CGLC-LC100 collection 3, and CORINE Land Cover are available in GEE. Although there are a variety of widely used land cover products, such as those mentioned above, due to the differences in the data sources, classification schemes, and classification methods of the different data products, their adaptability and accuracy in some specific areas tend to be uncertain. Therefore, it is vital to produce more precise and accurate land cover products for a certain area. For example, [PERSON] et al. used the Sentinel-2 time series based on the tile model and the RF algorithm based on the GEE platform to automatically generate a high-resolution land cover map of Madagascar, with a high overall accuracy (OA) of 89.2% [22]. [PERSON] et al. used Landsat 8 image data and the RF algorithm based on the GEE platform to analyze watershed land cover mapping in Nzhele and Levhuvu, with an OA of 76.43% [23]. [PERSON] et al.