Alpine swamp meadow on the Tibetan Plateau is among the most sensitive areas to climate change. Accurate quantification of the GPP in alpine swamp meadow can benefit our understanding of the global carbon cycle. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) products (GPP_MOD) provide a pathway to estimate GPP in this remote ecosystem. However, the accuracy of the GPP_MOD estimation in this representative alpine swamp meadow is still unknown. Here

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... five years GPP_MOD was validated using GPP derived from the eddy covariance flux measurements (GPP_EC) from 2009 to 2013. Our results indicated that the GPP_EC was strongly underestimated by GPP_MOD with a daily mean less than 40% of EC measurements. To reduce this error, the ground meteorological and vegetation leaf area index (LAI G ) measurements were used to revise the key inputs, the maximum light use efficiency (ε max ) and the fractional photosynthetically active radiation (FPAR M ) in the MOD17 algorithm. Using two approaches to determine the site-specific ε max value, we suggested that the suitable ε max was about 1.61 g C MJ´1 for this alpine swamp meadow which was considerably larger than the default 0.68 g C MJ´1 for grassland. The FPAR M underestimated 22.2% of the actual FPAR (FPAR G ) simulated from the LAI G during the whole study period. Model comparisons showed that the large inaccuracies of GPP_MOD were mainly caused by the underestimation of the ε max and followed by that of the undervalued FPAR. However, the DAO meteorology data in the MOD17 algorithm did not exert a significant affection in the MODIS GPP underestimations. Therefore, site-specific optimized parameters inputs, especially the ε max and FPAR G , are necessary to improve the performance of the MOD17 algorithm in GPP estimation, in which the calibrated MOD17A2 algorithm (GPP_MODR3) could explain 91.6% of GPP_EC variance for the alpine swamp meadow. moderate decomposition rates [2] . Another more important reason is the most effectively productive capacity in this ecosystem [1, 3] . Gross primary production (GPP), a key component for carbon fixation through vegetative photosynthesis in carbon biogeochemical cycle between the biosphere and the atmosphere [4-6], represents the productive capacity of ecosystem [7] . Besides, the alpine swamp meadow is always considered as a symbol of global change [8] . Therefore, accurate quantification of the GPP in alpine swamp meadow can benefit our understanding of the global carbon cycle. MODerate resolution Imaging Spectroradiometer (MODIS) GPP algorithm, based on the light use efficiency (LUE) model [9] , provides a pathway to document the logical spatial patterns and temporal variabilities of GPP across a diverse range of biomes and climate regimes [7, 10, 11 ]. The LUE model assumes that GPP of well-watered and fertilized crops is linearly related to the amount of solar energy they absorbed (APAR) which could be calculated from the PAR and the fraction of PAR absorbed (FPAR) by vegetation [7, 12] . Consequently, in the last few decades, the LUE approach has been used to estimate GPP at various spatial and temporal scales [7, [13] [14] [15] . 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 (ε 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, Turner 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. Zhang 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. He et al. (2012) demonstrated GPP_MOD was seriously underestimated in the forest ecosystems of East Asia, especially at northern sites. Verma et al. (2014 and 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. Tang 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 ε 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 (>6000 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 (~15.7ˆ10 3 hm 2 ) of the K. littledalei-B. sinocompressus swamp meadow [8] . Therefore, EC measurements in this alpine swamp provided the representative and effective insitu 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 Remote Sens. 2016, 8, 592 3 of 19 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.