The images of post atmospheric correction reflectance (PAC), top of atmosphere reflectance (TOA), and digital number (DN) of a SPOT5 HRG remote sensing image of Nanjing, China were used to derive four vegetation indices (VIs), i. e., normalized difference vegetation index (NDVI), transformed vegetation index (TVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and 36 VI-VFC relationship models were established based on these VIs and the VFC data obtained from ground measurement. The results showed that among the models established, the cubic polynomial models based on NDVI and TVI from PAC were the best, followed by those based on SAVI and MSAVI from DN, with the accuracy being slightly higher than that of the former two models when VFC > 0.8. The accuracy of these four models was higher in middle-densely vegetated areas (VFC = 0.4-0.8) than in sparsely vegetated areas (VFC = 0-0.4). All the established models could be used in other places via the introduction of calibration models. In VI-VFC modeling, using VIs derived from different radiometric correction levels of remote sensing image could help mining valuable information from remote sensing image, and thus, improving the accuracy of VFC estimation.