Fig.2.
Comparison of cumulative GPP estimates from the flux towers and the
models. The color lines represent the GPP value of cumulative
comparison between the EC-tower and model for each site. The red dashed
line is the 1:1 reference to the differences of modeled GPP and
EC-tower. Inset histogram shows the frequency distribution of the
percentage biases (PB). The two shadowed plots are two new models
developed in this study.
3.2 Biases in remote sensing data products and consequences
on global GPP
estimation
Evaluating the quality of input data and understanding the impact of
data biases on GPP simulation are prerequisites for improving GPP
simulation accuracy. First, various biases were found when the spatial
datasets that feed the models for global GPP simulations were evaluated
at the site scale (Fig.3; Fig.S3). For example, the spatial PAR dataset
only explained 57% of the observed PAR variation at the EC-towers, and
the slope and intercept were 1.2 and 0.57, respectively, indicating that
the PAR data fields overestimated PAR as a whole and slightly
underestimated PAR at the low value. The determination coefficients of
the global datasets of CO2, LE, and H at the EC-towers
were less than 20% (R2<0.2), only that of
the temperature data was efficient in representing site-scale variation
(R2=0.89).
Second, the biases in the spatial datasets had a significant impact on
GPP simulations. Before correcting these biases, the simulated GPP by
the LUE-EF and LUE-NDWI models explained only 49% and 61% of the
EC-tower GPP variation, and the slopes of the linear regression between
simulated and tower-estimated GPP were 1.54 and 1.31, respectively, and
the corresponding intercepts were -2.09 and -1.23. These results
indicate that both models overestimated GPP as a whole, but
underestimated low GPP values (Fig.4b and f). After correcting the
biases in the spatial datasets, the R2 of LUE-EF and
LUE-NDWI models improved to 0.80 and 0.79, with the slopes closer to 1
(1.20 and 1.18 values, respectively) and the intercepts closer to 0
(-0.59 and -0.91, respectively) (Fig.3c and g). The results also
indicated that the LUE-NDWI model was less sensitive to the biases in
the spatial data fields than the LUE-EF model, as shown by the smaller
differences in R2 before and after data correction,
probably attributed to the fact that the LUE-NDWI relies on NDWI, a
factor that can be derived directly from remote sensing data and thereby
less prone to error propagation than the LUE-EF model.
<Fig 3 roughly here>