Discussion
4.1 Adequacy of model structure in representing
processes
The understanding of the processes involved in GPP is fundamental to
building a reliable GPP model. For example, we found that failed
incorporation of the effect of clouds on GPP in some existing models
significantly underestimated GPP in areas with frequent cloudy cover.
Under clear sky conditions, the upper canopy leaves are close to light
saturation, while the lower canopy leaves are shaded and have limited
light (26). In contrast, under cloudy conditions, a higher proportion of
the light in the form of diffuse radiation can reach the lower parts of
the canopy, thus increasing the total photosynthetic use of PAR by the
vegetation (27). Some studies indicated that a 1% increase in diffuse
radiation induces a 0.94% increase in GPP (29, 30). In addition, many
studies have shown that CO2 fertilization has a
significant effect on vegetation production, a dominant factor
contributing to the 31% increase in global GPP since 1990 (30).
Nevertheless, many LUE models have not explicitly accounted for the
effect of increasing atmospheric CO2 concentration(32,
33). In our study, after incorporating the impacts of both cloud cover
and CO2, the performance of the LUE-EF and LUE-NDWI
models improved compared with the original EC-LUE model:
R2 improved from 0.61 to 0.68 for LUE-NDWI and from
0.61 to 0.74 for LUE-EF, respectively.
Water
availability is an important factor that affects GPP(33). In this study,
we adopted two alternative parameterized expressions to represent the
impact of water stress on GPP. First, the evaporative fraction (EF) of
total energy, closely related to the Bowen ratio, was used in the LUE-EF
model. The relevance of LUE-EF is grounded on the fact that less energy
used for ecosystem evaporation (i.e., smaller
evaporative
fraction) implies a stronger water limitation (35), which has largely
been verified using flux tower measurements(35). Nevertheless, the
application of the LUE-EF model is hindered by the derivation of EF that
required multiple steps and input data layers and therefore prone to
error propagation (36). In order to get a direct measure of water
stress, and therefore minimize error propagation, from satellite
observations we replaced the EF using NDWI in the LUE-NDWI model, based
on the evidence that NDWI is closely related to the plant water content
and thus a good proxy for plant water stress (37). The direct use of
NDWI in the LUE-NDWI model makes it ideal for mapping GPP at the
regional to global scales.
4.2 input data biases and possible impacts on GPP
simulations
In this study, we found that the
spatial data at the EC-tower sites had various systematic deviations,
which seriously affected GPP estimates. For example, the spatial data
fields of PAR explained only 57% of the PAR variation observed at the
EC-tower sites. Reasons for the data biases are mainly rooted in the
influence of sensor errors and atmospheric factors (e.g., cloud and
snow) (39, 40, 41). In addition, attempts unifying data from different
spatial and temporal resolutions also bring biases as we found that data
at different resolutions sometimes had poor correlations. The reason for
the existence of resolution mismatch is mainly caused by mixed pixels
and/or different time scales (e.g., daily, 8-day, or monthly data),
compared to the spatio-temporal resolution that applies to ground
conditions (42, 43). In general, spatial data errors are an important
cause of the uncertainty of GPP simulation, which might have contributed
to the large differences in GPP estimates among existing GPP models
(44). Thus, improving the quality of input data fields should be a major
research component in reducing the uncertainty in GPP simulations at
regional to global scales.
The comparison of existing global GPP products shows a huge variation
from 92.7 to 178.9 Pg C yr-1, which might be related
to data biases (45). One should realize that the sensitivities of models
to data biases are usually different, depending on model structure. In
our study, it was found that the LUE-EF model is more susceptible to
data errors, while the LUE-NDWI model is less affected by data biases
(Fig.3d). There are many GPP models in the world, and the sensitivity of
each model to data biases has not been effectively evaluated and
compared. The sensitivities of the models to data deviations should be
systematically investigated in future research. Another research area
that deserves more attention is how to improve data quality. Data bias
should be a primary concern in modeling GPP from site to region scales.
The existence bias of spatial data not only effects the GPP simulation,
but more importantly it hinders the observation and understanding of the
earth system. Therefore, effective correction of spatial data is
critical for reducing the uncertainty in GPP simulations, and research
on improving data quality should be encouraged. In this regard, our
research cautions using currently available spatial data for relevant
research.
4.4 Improving GPP simulation capability: the ways
forward
In this study, after data
correction, the coefficients of determination for the LUE-EF and
LUE-NDWI models at site scale reached 0.80 and 0.79, respectively. The
evaporative fraction parameter of the LUE-EF model involves more steps
than the NDWI of the LUE-NDWI model, and consequently the risk of the
error accumulation increases. The direct observation and continuous
recording of NDWI as a remote sensing product is one of the main
advantages of the LUE-NDWI model. The risk of error propagation of the
LUE-NDWI model at the regional to global scale is small. Therefore, the
LUE-NDWI model is more practical and attractive in spatially-explicit
simulations of GPP. In addition, many studies now show that
Sun-Induced chlorophyll Fluorescence
(SIF) has a strong near linear relationship with GPP (43), indicating
SIF can be used as a direct indicator of GPP. In principle, the simple
forms of GPP models and widely available inputs, as compared with more
complex global models, make them more practical for applications over
large areas and better suited for attribution and uncertainty analysis
(46, 47).
Should a balance or combination of multiple approaches be considered in
the study of GPP simulation improvement? The comparison of global GPP
products shows that there are differences among the three types of GPP
models. The biophysical models showed the greatest inter-annual
variability, followed by LUE models, and the machine learning models had
minimal. Some research shows the machine learning models rely on
empirical relationships between forcing variables and fluxes, which
causes them barely captured the inter-annual variation of GPP (45). In
contrast, LUE models assume that GPP is only related to environmental
factors (e.g., temperature, water, etc.) and light use efficiency in
relatively straightforward forms. Compared with the biophysical models,
LUE models have less parameters, and simpler structures. This means that
biophysical models have higher probability of error expansion in the
processes of GPP simulations. In addition, studies have shown that the
combination of machine learning models with other type of models have
more potential to improve GPP estimation, which implies a balance or
combination of multiple approaches might be the choice to reduce the
uncertainty in GPP simulation.
Vastly different GPP products, as
shown by the means, trends, and interannual variabilities of GPP,
generated by the 22 models suggest our current ability in simulating
global GPP is not encouraging (Fig.4c). For nearly 40 years committed to
the global simulation of GPP, there does not seem to have a clear
direction of the improvement of GPP simulation. This is mainly reflected
in the fact that the models did not converge in GPP simulations with the
advance of time. GPP model development is not explicitly directed,
despite the constant emergence of new models. Our research indicates
that neither the structure of the model nor the quality of the input
data are error prone. Therefore, specific standards need to be developed
to optimize model structures as well as sufficient validation and
calibration of the input data. Research into efficacy of model
structures and the precision of input data may be more important than
the development of new models for global GPP estimation.