Introduction

Terrestrial gross primary production (GPP) or the total photosynthetic uptake of carbon by plants plays a critical role in maintaining the global carbon balance between the biosphere and the atmosphere. However, the estimation of terrestrial GPP by existing models remains highly uncertain, with global estimates ranging widely between 92.7 to 168.7 Pg C yr-1 (1, 2). This large uncertainty poses a serious obstacle to quantifying and understanding the global carbon cycle (3). It is broadly agreed that, to reduce the uncertainty of GPP estimation and advance carbon cycle science, it is crucial to consider: (1) the impacts of model structure, (2) the determination of parameter values, and (3) the quality of data feeding in GPP models (4, 5).
Model structure has been considered as one of the most important factors that affect model performance (6). Yet, large structural differences can be observed among GPP models. For example, the fraction of photosynthetic active radiation (FPAR), an important parameter in the light use efficiency (LUE) models, has been treated in disparate ways, either approximated by enhanced vegetation index (EVI) in the vegetation photosynthesis model (VPM) (7), as a linear function of normalized difference vegetation index (NDVI) in the eddy covariance-light use efficiency (EC-LUE) model (9), or as a nonlinear function of leaf area index (LAI) according to Beer’s Law, among others(10). A similar situation exists for representing temperature stress (TS), water stress (WS), and their interactions among models. The moderate resolution imaging spectroradiometer (MODIS) model and the VPM model adopt a multiplicative structure to represent the collective influences of WSand TS on GPP (12, 7). The EC-LUE model, on the other hand, considers that the Liebig’s law is ecologically more reasonable in representing the effects of WS and TS (8).
The estimation of model parameters often affect the simulation accuracy of the model, thus rigorous model parameterization and calibration should be adopted in GPP modeling (13). 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 (12, 15), and similar approaches can be found in other models (16). 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 (17). However, another study has suggested that fixed LUE(max) value would lead to increased GPP uncertainty (18). Analysis of the parameters of the diagnostic carbon flux model (DCFM) showed that cross-site estimation improved the representativeness and robustness of parameter estimates (19). Studies considering a wider number of flux towers are thus necessary for more reliable tuning of GPP model parameters.
Regional to global simulations inevitably employ spatiotemporal data for initialization or as driving forces (20). 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 reflect the reality effectively (22). Other studies have revealed that MODIS satellite FPAR are systematically lower than ground-measured FPAR observations in winter and spring (22). Clouds seriously affect satellite observations in humid regions such as the Amazon (23). Even when applying a cloud correction by the CFMask algorithm (24) only 70% of PAR can be satisfactorily simulated (25). Failure to reproduce the driving data of the models faithfully would affect the simulation of GPP, resulting in the uncertainty of GPP.
To address the three issues mentioned above and to improve the estimation accuracy of GPP at the 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: (1) to compare existing models and address the model structure deficiency, if necessary, (2) to find the optimal parameter values using GPP observed at the EC towers, (3) to 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) to evaluate errors of spatial RS data and their impacts on GPP estimation, and (5) to generate new GPP model and subsequent global product, after correcting biases in spatial data layers.