## 3 Results
### Comparison of 24 Global GPP Products  
A comparison of 24 global GPP products is shown in Figure 5a. 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 range of 120\(-\)130 Pg C yr\({}^{-1}\) (Figure 5a,b). The number of times that a model's GPP stays within the interquartile range (IQR) of all 24 GPP products throughout the years can be used as an indicator of model performance. Result showed that the annual GPP values simulated by the following models stayed within their annual IQRs every year: Revised_ECLUE, VPM, SVR, LPI_GUESS, and LUE-NDWI (after correcting input data biases), suggesting a better performance in simulating GPP. Although not in the category above, FLUXCOM RF, BESS, CABLE POP, and CLASS CTEM models have more than 10 years in IQR. It is worth noting that the GPP  
Figure 4: Consequences of biased input data on LUE-NDWI GPP at a global scale. (**a**) The mean annual GPP from 2000 to 2018 after data correction (reference Section 2.5). (**b**) The difference of mean annual GPP from 2000 to 2018, GPP uncorrected—GPPCorrected. Symbols indicate various levels of difference: “-”: \(-\)50%–(\(-\)30%), “-”: \(-\)30%–(\(-\)10%), \({}^{\prime}\)\(\pm\)\({}^{\prime}\): \(-\)10%–10%, “+”: 10–30%, \({}^{\prime}\)\(+\)\({}^{\prime}\): 10–30%, “++”: 30–50%. The inset bar chart shows the global distribution of the difference proportions.  
Figure 5: (**a**) Comparison of annual global GPP estimates from various models. The number after each model’s name in parentheses is the model’s number of years in the interquartile range (IQR) of all global GPP products, indicated by the shaded region. LUE-NDWI (data corrected) and LUE-NDWI1(i.e., LUE-NDWI with spatial data uncorrected) are GPP estimated by LUE-NDWI with corrected and uncorrected input data, respectively. (**b**) Boxplot of annual GPP values during the study period for each model. (**c**) Trends of annual GPP (Pg C yr\({}^{-1}\)) by model. Symbols of LUE models are in black, machine learning models in green, and biophysical models in blue.  
from the LUE-NDWI model, after data correction, was the closest to the median GPP value of the 24 global models.  
The interannual variabilities simulated by these models were also quite different (Figure 5b). The interannual variabilities of LUE and biophysical models were generally higher than the machine learning models as shown by their standard deviation or std: ECLUE (std = 2.96 Pg C yr\({}^{-1}\)), BEPS (std = 3.51 Pg C yr\({}^{-1}\)), JSBACH (std = 2.97 Pg C yr\({}^{-1}\)), JULES (std = 3.06 Pg C yr\({}^{-1}\)), LPJ-GUESS (std = 2.66 Pg C yr\({}^{-1}\)), ORCHIDEE (std = 2.91 Pg C yr\({}^{-1}\)), FLUXCOM_ANN (std = 0.53 Pg C yr\({}^{-1}\)), FLUXCOM_RF (std = 0.38 Pg C yr\({}^{-1}\)), and FLUXCOM_MARS (std = 0.23 Pg C yr\({}^{-1}\)). The standard deviation of LUE-NDWI (after correcting input data biases) model was 1.83 Pg C yr\({}^{-1}\), close to the mean standard (1.68 Pg C yr\({}^{-1}\)) of all the models within the IQR range.  
The trends of GPP simulated by the 24 models also varied greatly from \(-\)0.25 to 0.84 Pg C yr\({}^{-1}\) (Figure 5c). The trends of the machine learning models were smaller than those of other models. Most models 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).