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.