###### Abstract
Variation in the values of the same biophysical parameters among different models is a major concern in GPP estimation. For example, the maximum light use efficiency (LUE(max)), a parameter used in LUE-based GPP models, represents the maximum efficiency of unit vegetation converting energy to photosynthates and therefore should be relatively stable [14]. However, it has taken many different values in LUE-based GPP models. In the MODIS model, LUE(max) values are biome-specific, varying from 0.604 to 1.259 g C MJ\({}^{-1}\)([PERSON] et al., 2006), and similar approaches can be found in other models [15]. The EC-LUE model, on the other hand, takes a constant value at LUE(max) = 2.25 g C MJ\({}^{-1}\), that was derived from many flux tower observations, and the authors later advocated the use of different constant LUE(max) values for C3 and C4 plants [16]. However, another study has suggested that a fixed LUE(max) value would lead to increased GPP uncertainty [17]. An analysis of the parameters of the diagnostic carbon flux model (DCFM) showed that cross-site estimation improved the representativeness and robustness of parameter estimates [18]. Studies considering a wider number of flux towers are thus necessary for a more reliable tuning of GPP model parameters. Regional to global simulations inevitably employ spatiotemporal data for initialization or as driving forces [19]. How spatial data products affect GPP simulation has rarely been assessed because users of the data products tend to take a leap of faith by assuming the quality of data has met the accuracy requirement and limited findings regarding the importance of data quality have been ignored frequently. For example, it was found that the widely used average of eight-days MODIS satellite FPAR data was unable to effectively reflect the reality [20]. Other studies have revealed that MODIS satellite FPAR products are systematically lower than ground-measured FPAR observations in winter and spring [21]. Clouds seriously affect satellite observations in humid regions such as the Amazon [22]. Even when applying a cloud correction by the CFMask algorithm [23] only 70% of PAR can be satisfactorily simulated [24]. Failure to reproduce the driving data of the models faithfully would affect the simulation and accuracy of GPP. To address the three issues mentioned above and improve the estimation accuracy of GPP at a global scale, we comprehensively appraised the structure and performance of existing LUE models against GPP estimates from 151 eddy covariance (EC) flux towers worldwide, and assessed the impacts of using currently available data products on the estimates of global GPP using newly developed LUE models. The specific objectives were to: (1) Compare existing models and address model structure deficiency, if necessary, (2) find the optimal parameter values using GPP observed at the EC towers, (3) develop a new model to taking advantage of remote sensing (RS) data as directly as possible, reducing errors of intermediate data products and algorithms, (4) evaluate errors of spatial RS data and their impacts on GPP estimation, and (5) generate a new GPP model and subsequent global product, after correcting biases in spatial data layers.