Baseflow is an essential component of runoff, which is the dominant water resource for the dry season. To better manage water resources, it is vital to investigate the links between the multiple influencing factors and the baseflow for better prediction in light of global changes. Previous studies have seldom separated these influencing factors in the analysis, making it difficult to determine their effect on the baseflow. In this study, based on the analysis datasets generated by the Soil and Water Assessment Tool (SWAT) model, the control single variables, correlation analysis, and multiple linear regression (MRL) methods were firstly combined to analyze the influences of the chosen factors (land use, topography, and soil type) on the baseflow. The findings revealed that the ability of precipitation to replenish the baseflow was better in areas with a higher slope. The ability of precipitation to recharge the baseflow for different land uses was ranked as “forest land > grass land > agricultural land > urban land”; land use factors should be added to the baseflow prediction equation. The hydrological group is the main property of soil affecting the baseflow recharge. A regression model established using publicly acquired remote sensing data had a good performance (R² = 0.84) on baseflow prediction on an annual scale. As a result of this information, relevant government officials and environmentalists may better manage water supplies in drought years. In addition, this regression model frame has the potential to be used for a baseflow inquiry inside an ungauged zone for a better ecological assessment.