Remotely sensed (RS) vegetation indices (VIs) are increasingly being employed as a direct proxy for gross primary productivity (GPP). When estimating mountain vegetation GPP from VI, efforts often focus on the RS-related topographic effect (i.e., distort VIs), while the micrometeorology-related topographic effect is so far ignored. Here, a topographically adjusted VI (TAVI) scheme was developed based on removing the RS-related effect by path length correction (PLC) first and integrating the micrometeorology-related effect associated with the topography-induced redistributions of radiation and water subsequently. The proposed TAVI scheme was applied to three VIs, namely, normalized difference VI (NDVI), enhanced VI (EVI), and near-infrared reflectance of vegetation (NIRv), at 14 eddy covariance (EC) sites. The determination coefficient (R-2) and root-mean-square-error (RMSE) between VI-estimated and EC GPP were used for evaluation. Results showed that both EVI and NIRv outperformed NDVI in GPP estimation before correction, with R-2 increased by 0.14-0.15 and RMSE decreased by 0.42-0.44 gC center dot m(-2)center dot day(-1). After correcting the RS-related topographic effect, EVI and NIRv achieved an obvious improvement (R-2 = 0.71 and RMSE = 2.00 gC center dot m(-2)center dot day(-1)), while NDVI showed little sensitivity to topography. Subsequently, EVI and NIRv showed a notable improvement ( R-2 = similar to 0.77 and RMSE = similar to 1.82 gC center dot m(-2)center dot day(-1)) after integrating the micrometeorology-related topographic effect, and the performance of NDVI was also improved R-2 = 0.73 and RMSE = 1.94 gC center dot m(-2)center dot day(-1)). This study suggests that integrating the micrometeorology-related topographic effect on vegetation photosynthesis into topographically corrected VIs (TCVIs) is an effective way to improve mountain vegetation GPP estimation.