# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 3 Results
### The Spatial Pattern of Global GPP  
The spatial patterns of the mean annual GPP simulated by the proposed model, BEPS, MODIS, and VPM are generally consistent (Figure 5). The proposed model estimated the mean annual GPP to range from 0 KgC m\({}^{-2}\) year\({}^{-1}\) to 4.0 KgC m\({}^{-2}\) year\({}^{-1}\), with an average of 0.837 KgC m\({}^{-2}\) year\({}^{-1}\) during the study period of 2000 to 2022 (Figure 5A). In contrast, the BEPS model produced annual GDP of 0.002 KgC m\({}^{-2}\) year\({}^{-1}\) to 3.89 KgC m\({}^{-2}\) year\({}^{-1}\), with an average of 1.02 KgC m\({}^{-2}\) year\({}^{-1}\) from 2000 to 2019 (Figure 5B). Additionally,  
Figure 4: Comparisons of annual global summed GPP estimates from various models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **R\({}^{2}\)** (Compared to** \\ **Our Study)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} \(p\) **Value (Compared** \\ **to Our Study)** \\ \end{tabular} } & \multirow{2}{*}{**Linear Regressions**} & \multirow{2}{*}{\
\begin{tabular}{c} **R\({}^{2}\)** \\ **\end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} \(p\) **Value** \\ **Value** \\ \end{tabular} } \\ \hline Our study & 1 & \(<\)0.001 & \(y=0.548x-976.06\) & 0.8633 & \(<\)0.001 \\ BEPS & 0.767 & \(<\)0.001 & \(y=0.685x-1253.23\) & 0.9758 & \(<\)0.001 \\ MODIS & 0.657 & \(<\)0.001 & \(y=0.34x-585.61\) & 0.8077 & \(<\)0.001 \\ VPM & 0.765 & \(<\)0.001 & \(y=0.536x-935.68\) & 0.9636 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical analysis of GPP for the proposed model, BEPS, VPM, and MODIS. the MODIS model simulated the mean annual GPP to span from 0 \(\text{KgC}\,\text{m}^{-2}\,\text{year}^{-1}\) to 3.5 \(\text{KgC}\,\text{m}^{-2}\,\text{year}^{-1}\), with an average of 0.711 \(\text{KgC}\,\text{m}^{-2}\,\text{year}^{-1}\) from 2003 to 2022 (Figure 5C). Lastly, the VPM model simulated the mean annual GPP to range from 0 \(\text{KgC}\,\text{m}^{-2}\,\text{year}^{-1}\) to 4.29 \(\text{KgC}\,\text{m}^{-2}\,\text{year}^{-1}\), with an average of 0.88 \(\text{KgC}\,\text{m}^{-2}\,\text{year}^{-1}\) from 2000 to 2017 (Figure 5D). We observed that the peak annual GPP values occurred predominantly in tropical regions, particularly within the evergreen broadleaf forests of the Amazon and Southeast Asia, while the lowest GPP values were primarily situated in cold and arid areas. The long-term trend of GPP was analyzed by the linear regression method (Figure 6). The global GPP showed an increasing trend of 72.7%, 85.7%, 68.1%, and 78.3% for the proposed model, BEPS, MODIS, and VPM, respectively (Figure 6). The statistically significant trends at a 95% confidence level were 46.03%, 62.03%, 30%, and 56.7% for the current model, BEPS, MODIS, and VPM, respectively. These significant increases were predominantly found in regions across Europe, Southeast Asia, and Africa. The significantly decreasing trends of global terrestrial production were 17.23%, 15.1%, 6.5%, and 21.7% for the proposed model, BEPS, MODIS, and VPM, respectively. These trends were distributed across various locations and particularly notable in tropical rainforests such as the Congo Basin and the Amazon. These spatial patterns in annual mean GPP trends, along with the updated estimates extending to 2022, are consistent with findings from previous studies [74]. The apparent discrepancy between the positive GPP trend from 2000 to 2022 (Figure 4) and the negative values in the global spatial distribution of GPP (Figure 6) could be clarified by distinguishing between temporal trends and spatial patterns. Figure 4 shows the overall global GPP trend, indicating an increase in average GPP over time. In contrast, Figure 6 shows the spatial distribution of GPP at 0.1-degree resolution, with individual pixels reflecting localized changes in structural, climatic, and physiological parameters of C3 and C4 vegetation.