Real time population data is crucial for urban planning, resource management, and the sustainable development of society. In order to effectively enhance existing population estimation methods based on geospatial big data, this study comprehensively compares and analyzes the population simulation performance of different open geospatial datasets, and develops a comprehensive approach integrating remote sensing and emerging social media user data to achieve high precision rapid estimation of population at the county level. Taking Chinese counties as the experimental area, multiple linear regression and geographically weighted regression methods are employed to comprehensively evaluate the population modeling capability of various geospatial remote sensing data. The data utilized include Tencent Location Based Service ( LBS) data, Amap Point of Interest ( POI) data, nighttime light remote sensing data, and land use / cover data derived from remote sensing. The research findings indicate that, in estimating population distribution, Tencent location data and POI data outperform remotely sensed land use / cover data and nighttime light satellite data, with population simulation accuracies of 81 .6% , 70 .8% , 68 .8% , and 63 .0% , respectively. Furthermore, the comprehensive use of multisource geospatial data can achieve an overall population simulation accura cy of 85 .4% . The research results and discoveries can provide data and technical support for popula tion related policies in China.