Alpine swamp meadows on the Tibetan Plateau, with the highest soil organic carbon content across the globe, are extremely vulnerable to climate change. To accurately and continually quantify the gross primary production (GPP) is critical for understanding the dynamics of carbon cycles from site-scale to global scale. Eddy covariance technique (EC) provides the best approach to measure the site-specific carbon flux, while satellite-based models can estimate GPP from local, small scale sites to regional and global scales. However, the suitability of most satellite-based models for alpine swamp meadow is unknown. Here we tested the performance of four widely-used models, the MOD17 algorithm (MOD), the vegetation photosynthesis model (VPM), the photosynthetic capacity model (PCM), and the alpine vegetation model (AVM), in providing GPP estimations for a typical alpine swamp meadow as compared to the GPP estimations provided by EC-derived GPP. Our results indicated that all these models provided good descriptions of the intra-annual GPP patterns (R²>0.89, P<0.0001), but hardly agreed with the inter-annual GPP trends. VPM strongly underestimated the GPP of alpine swamp meadow, only accounting for 54.0% of GPP_EC. However, the other three satellite-based GPP models could serve as alternative tools for tower-based GPP observation. GPP estimated from AVM captured 94.5% of daily GPP_EC with the lowest average RMSE of 1.47 g C m^-2. PCM slightly overestimated GPP by 12.0% while MODR slightly underestimated by 8.1% GPP compared to the daily GPP_EC. Our results suggested that GPP estimations for this alpine swamp meadow using AVM were superior to GPP estimations using the other relatively complex models.