Separating the surface contribution is crucial in aerosol optical depth (AOD) inversion. Traditional retrieval algorithms developed for single-angle sensors typically assume that the surface is homogeneous Lambertian, which ignores surface anisotropy and introduces errors. However, considering surface properties can create an ill-posed inversion, so it is necessary to introduce a priori knowledge to characterize the surface anisotropy parameters. Using the mean value over a certain time range as a fixed value for the surface parameter is a common practice. However, surface conditions vary over time, and averaging can lead to inaccuracies in estimating the surface contribution. This paper analyzes the anisotropic scattering kernel coefficients of various surface cover types to evaluate the effect of the mean value on inversion. The analysis is based on the MODIS bidirectional reflectance distribution function product (MCD43C2) for 2022 and the MODIS surface classification product (MCD12Q1). The statistical analysis indicates that when forested land is the dominant cover, the parameter is generally lower than 0.05. In nearly 90% of the cases where grassland, cropland, and towns are the dominant covers, the parameter is lower than 0.1. This paper presents a simulation of the potential errors in apparent reflectance under diverse surface types, intending to invert the AOD based on the simulation findings. A comparison of the results with the preset “true value” of the AOD revealed that the quarterly mean value of the BRDF shape parameter was less prone to producing errors in the location of the quarterly mean value of the BRDF shape parameter than the bright surfaces with less vegetation cover and the more highly covered surfaces. The shape parameter of the bi-directional reflectance distribution function (BRDF) at this location produces fewer errors than brighter surfaces with less vegetation cover. In other words, at a solar zenith angle of 50° and a true value of AOD of 0.4, the mean absolute error of retrievals is 0.053 for evergreen broadleaf forests and 0.089/0.083/0.113 for nearly 90% grasslands, croplands, and towns, respectively. However, the mean absolute error of retrievals of AOD is significantly higher in the bright surface area, reaching a maximum value of 0.145. This is the case for nearly 90% of the grassland, cropland, and urban regions. However, the mean absolute error of retrievals is only 0.078/0.107 for the remaining areas. This indicates that using quarterly-averaged BRDF as a surface constraint in AOD retrievals becomes less reliable with increasing surface reflectivity. This is due to the inherent uncertainty in using scalar satellite observations, where the surface signal dominates. Consequently, the synergistic inversion of BRDF and AOD using multi-angle polarization observations represents a promising avenue for enhancing the accuracy of aerosol inversion in the future. The findings of this study offer insights into the distribution of BRDF kernel coefficients across different surface types globally. Additionally, this research delves into the sources of errors in AOD inversion based on the non-Lambertian forward radiative transfer model. The study also presents the potential error ranges associated with this process.