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Plant phenology is one of the main controls of variation in net ecosystem productivity (NEP). Accurate representation of vegetation phenology is important for predicting ecosystem carbon budget. Although using satellite observation to determine vegetation phenology is becoming a mature option, there are still uncertainties in its application at site scales. Our purpose is to establish a more robust phenological index to accurately predict carbon uptake phenology, which detailed results can complement the shortcomings of MODIS NDVI-derived phenology. Here we used a growing season index (GSI) phenology model to simulate carbon flux phenology (CFP) including the start of carbon uptake (CUstart), the end of carbon uptake (CUend) and the length of carbon uptake period (CUP) in an alpine meadow ecosystem on the Tibetan Plateau and to compare the results with those modeled from MODIS NDVI. We also further analyzed the main environmental factors in controlling CFP. The results indicated that the GSI model made substantially more precise prediction for CUstart, CUend and CUP (with higher correlation R-2 > 0.90) than that of the MODIS derived phenology. The GSI model was also superior to NDVI in predicting both seasonal and annual variations of net ecosystem productivity (NEP). Moreover, CUP played an important role in regulating ecosystem carbon balance in the study site because NEP was significantly positive correlated with the period of annual carbon uptake. NEP would increase by 1.63 g C m(-2) year(-1) if one CUP-day was extended. Further, CUP was influenced by variation in CUstart. Previously overlooked water variability (soil water content and VPD) played a significant role in controlling CUP and CUstart. In addition, temperature could enhance water stress to delay CUstart and shorten CUP. It is indicated that decrease in carbon uptake could be induced by accelerative water stress in the face of global warming in the alpine meadow. These results suggest that CFP is more sensitive to not only temperature but also water condition, and a combination of soil water and temperature could be a useful way to enhance the estimation of CFP in future ecosystem model.