However, significant discrepancies were often found between this approach and other terrestrial ecosystems models [16]. Therefore, we need to effectively validate these products to establish their utility in regional and global GPP estimation [17], which is full of challenge because of the large uncertainties in parameters inputs of the maximum LUE (\(\epsilon_{\text{max}}\)) and FPAR in different ecosystems [18, 19]. Eddy covariance (EC) technique is the most efficient micrometeorological method to observe CO\({}_{2}\) exchange between the biosphere and the atmosphere [20, 21] thus is increasingly used for ecosystem model calibration and validation [22, 23, 24]. Recent many researches tend to concentrate on the evaluation and validation of MODIS GPP products (GPP_MOD) using GPP partitioned from the net ecosystem CO\({}_{2}\) exchange (NEE) of EC measurements [10, 17, 19, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]. Most studies suggested that GPP_MOD were able to capture seasonal and spatial GPP patterns across biomes except croplands because of human disturbances [30, 31]. However, the accuracy of GPP_MOD varied with different ecosystem and spatial-temporal scale [17, 28, 29]. For example, [PERSON] et al. (2006) proposed that GPP_MOD tended to be overestimated at low productivity site, in contrast, that tended to be underestimated in high productivity sites. [PERSON] et al. (2008) revealed that GPP_MOD accounted for 1/2-2/3 of GPP_EC for the alpine meadow, but only about 1/5-1/3 for the cropland. [PERSON] et al. (2012) demonstrated GPP_MOD was seriously underestimated in the forest ecosystems of East Asia, especially at northern sites. [PERSON] et al. (2014 and 2015) concluded that GPP_MOD had a low confidence to track inter-annual GPP variation patterns despite of their better performances in intra-annual GPP patterns. [PERSON] et al. (2015) discovered that the current MODIS product works more significantly for deciduous broadleaf forest and mixed forest, less for evergreen needle leaf forest, and least for evergreen broadleaf forest. Overall, all of these studies mainly attributed the discrepancies between GPP_MOD and GPP_EC to the quality of different upstream inputs. Further, with the vigorous development of international flux networks [36, 37], they appealed that the next efforts should have a site-specific refinement of MOD17 parameters inputs, especially the \(\epsilon_{\text{max}}\) and FPAR, which directly affected GPP estimation based on the LUE model [11, 38, 39]. Then many tower-based site-level validation studies on GPP_MOD increasingly arise in recent years [25, 27, 32, 34, 38, 40, 41]. However, the validation of GPP_MOD estimation in alpine swamp meadow is still absent. Lhasa River Basin is one typical area dominated by alpine swamp on the central Tibetan Plateau [8] and the dominant (65.45%) wetland landscape types is _Kobresia littledalei_ swamp meadow [42]. Our EC flux tower was established at Damxung, a county located at the Lhasa River Basin, surrounded by more than 30 high mountains (>600 m a.s.l) and a dense river networks, which provided favorable conditions for development of alpine wetlands due to a constant water supply from abundant snow melt in growing season [43]. 26% of the area was alpine wetland, of which has the largest patches area (~15.7 \(\times\) 10\({}^{3}\) hm\({}^{2}\)) of the _K. littledalei-B. sinocompressus_ swamp meadow [8]. Therefore, EC measurements in this alpine swamp provided the representative and effective institu data for validation and revision of the MOD17 GPP products. Based on five years MODIS data products and GPP_EC measurements from 2009 to 2013, the main objectives of our study are: (1) to evaluate the validity of GPP_MOD in temporal patterns and absolute amounts; (2) to quantify the uncertainty sources of GPP_MOD caused by inaccurate parameters inputs in the MOD17 algorithm; (3) to identify the optimized parameters inputs in the MOD17 algorithm for this alpine swamp meadow.