Abstract: Recognized as part of China’s new generation of geostationary meteorological satellites, FY-4A/B is equipped with an advanced geostationary radiative imager (AGRI). The instrument provides high-resolution satellite measurements in near real-time for the Eastern Hemisphere. Before applying satellite data for atmospheric parameter retrieval or assimilation, it is essential to conduct a quantitative analysis of data biases. A quantitative assessment of simulated brightness temperature is offered for the surface-sensitive channels of FY-4A/AGRI, to improve the utilization of FY-4A/AGRI measurements over land in assimilation processes. The CRTM radiative transfer model, alongside land surface temperature (T_s) data from 3 land surface models (CLM4, RUC, Noah-MP) are employed, and monthly surface emissivity data are utilized to simulate brightness temperatures for 4 representative months: January, April, July, and October of 2022 over the eastern China. The research focuses on analyzing the spatial, diurnal, and surface-related variations in brightness temperature bias. Initially, the simulation performance in July is particularly superior, especially in the northern region of the eastern China. And the experiment utilizing Noah-MP model consistently provides the best and the most stable results. Secondly, results of the experiment utilizing Noah-MP model produces the most accuate simulations throughout most of 24 h cycle which is attributed to discrepancies in T_s accuracy. Further diagnostics indicate these differences in T_s simulations largely arise from variations in surface absorption of solar radiation, surface latent heat flux, surface sensible heat flux, and soil heat capacity. Additionally, considering the potential impact of land surface emissivity on simulated brightness temperatures, an analysis is conducted using 3 land surface models under varying land surface emissivity schemes. Results indicate that the simulations conducted under various land surface emissivity schemes are nearly identical. Through an analysis of relative sensitivity, it is revealed that land surface temperature is a significant factor influencing simulated brightness temperatures in AGRI surface-sensitive channels over the eastern China. Lastly, acknowledging the superior accuracy of experiment using Noah-MP model, the study statistically analyzes the brightness temperature biases and standard deviations of AGRI surface-sensitive channels across 5 typical surface types in the eastern China as a reference for quantifying errors and correcting biases in the assimilation of satellite data.