Fig.3 Comparison of (a) spatial PAR and site PAR, (b) tower GPP
and LUE-EF GPP (uncorrected spatial data), (c) tower GPP and LUE-EF GPP
(corrected spatial data), (d) LUE-EF GPP with uncorrected and LUE-EF GPP
corrected spatial data, (e)spatial EVI (500m resolution) and spatial EVI
(10km resolution), (f) tower GPP and LUE-NDWI GPP (uncorrected spatial
data), (g) tower GPP and LUE-NDWI GPP (corrected spatial data), and (h)
LUE-NDWI GPP with uncorrected and LUE-NDWI GPP corrected spatial data.
All comparisons are based on site scale.
At the global scale, the biases in spatial data inputs had a great
impact on the simulated GPP even for the less sensitive model LUE-NDWI.
Fig.4a shows the global average annual GPP distribution from 2000 to
2018, simulated by the LUE-NDWI model using corrected input data layers.
The spatial pattern of GPP agrees well with previous studies. However,
the impact of data biases on the spatial pattern of simulated GPP was
obvious and not uniform across space (Fig.4b). The area overestimated is
much larger than underestimated area when the data biases were not
attended, and the area fractions with GPP biases at (-50%)-(-30%),
(-30%)-(-10%),10%-30%, and 30%-50%, were 8%, 19%, 27%, 31% and
15%, respectively. After data correction, area of GPP serious reduction
occurs in the mountain systems of the Tibetan plateau in Asia, northern
Africa and South America region. Area of serious growth were observed in
Australia, northwest North America and Siberia. The global annual
average GPP estimated by the LUE-NDWI, after input data correction, was
about 125.6Pg C yr-1. Without data correction, the LUE-NDWI model would
overestimate global GPP by 18% (Fig 4c and 4d). The corresponding
global growth rate of GPP decreased from 0.34 to 0.17 Pg C yr-1 after
input data correction (Fig. 4e).
The comparison of global GPP products simulated by 22 models is shown in
Fig 4c. The GPP products were from TRENDY and other studies. Large
differences can be seen from these models with long-term GPP averages
varying from 92.7 to 178.9 Pg C yr-1 with more GPP estimates
concentrated in the 120-130 Pg C yr-1 (Fig 4d). The interannual
variabilities simulated by these models were also quite different. In
general, the inter-annual variabilities of LUE and biophysical models
were greater than those of the machine learning models. It is worth
noting that the GPP simulations from the Revised-ECLUE, PR, LPJ-GUESS,
and data LUE-NDWI (after correcting input data biases) models were all
within the interquartile range (IQR) of the 22 GPP products throughout
all years. In addition, the GPP from the LUE-NDWI model, after data
correction, was the closest to the median GPP value of the 22 global
models. The trends of GPP simulated by the 22 models also varied greatly
from -0.25 to 0.84 Pg C yr-1 (Fig 4e). The trends of the machine
learning models were smaller than those of other models. Most model
showed positive trends, only the revised-ECLUE and CLASS-CTEM models
showed downward trends, and some models demonstrated no significant
trends (i.e., MODIS, FLUXCOM_ANN, FLUXCOM_MARS, and FLUXCOM_RF).
<Fig 4 roughly here>