Land cover data are important basic data for activities such as geographic monitoring of national condition, ecosystem assessment, and land spatial planning. Remote sensing computing cloud platforms such as GEE, PIE, and Microsoft Planetary Cloud have rich data sources and powerful computing power. Using the GEE cloud platform to integrate multiple sets of public products to produce training samples can significantly reduce the cost and cycle of product updates, which has important research value. The Huaihe River Basin was taken as a research area. The 2020 10m resolution land cover data stored on the GEE platform by European Space Agency (ESA) and Environmental Systems Research Institute (ESRI) were used as training sample data sources. Sentinel-1 radar and Sentinel-2 multispectral images were selected to construct the feature space, and random forest classification method was used to generate a 10m resolution land cover data. To validate the effectiveness of the method, two sets of comparative experiments were conducted. In experiment 1, totally 1116 randomly selected sample points with consistent categories from ESA and ESRI products were used as training samples, and the accuracy of the product and multiple sets of public products were verified through visual interpretation. The results showed an overall accuracy of 80.35% for the product, with an improvement of 2.89~8.94 percentage points compared with the overall accuracy of the public products. It also provided a more detailed depiction of partial characteristics. By incorporating radar imagery with Sentinel-2, the overall accuracy was improved by 3.52 percentage points, indicating the clear benefits of radar imagery as an auxiliary data source. Experiment 2 set up eight sets of training samples with different numbers, and used manual interpretation, ESA, ESRI, DW, and GlobeLand30 as reference data sources to study the impact of different training sample sizes and reference data sources on the overall accuracy of classification products. The results showed that as the training sample size was increased, the improvement in overall accuracy based on the five different reference data sources gradually was decreased and reached a relatively stable level. The research results indicated that by utilizing public land cover data and massive remote sensing images on the GEE platform, high-quality training samples can be quickly extracted to produce higher quality 10m resolution land cover data. The method had significant practical and promotional value.