## 4 Discussion
### Uncertainties, Errors, and Accuracies  
Generally, GPP_EC values are higher than MOD17 (Figure 2), which is consistent with [PERSON] _et al._[22] and [PERSON] _et al._[11]. It is reported that one significant limitation of the MOD17 algorithm is the improper characterization of LUE, so MOD17 is found to have underestimated peak values of GPP_EC [11, 22, 30]. In MOD17 and MOD_ALG, the same FPAR and different climate \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **LST** & **LSWI** & **PAR** & **Ta** & **GPP_EC** \\ \hline \multirow{6}{*}{Croplands} & EVI & 0.52 \({}^{**}\) & 0.81 \({}^{**}\) & 0.46 \({}^{**}\) & 0.67 \({}^{**}\) & 0.82 \({}^{**}\) \\  & LST & - & 0.30 \({}^{**}\) & 0.25 \({}^{**}\) & 0.96 \({}^{**}\) & 0.64 \({}^{**}\) \\  & LSWI & - & - & 0.41 \({}^{**}\) & 0.45 \({}^{**}\) & 0.77 \({}^{**}\) \\  & PAR & - & - & - & 0.75\({}^{**}\) & 0.69 \({}^{**}\) \\  & Ta & - & - & - & - & 0.73 \({}^{**}\) \\  & GPP\_EC & - & - & - & - & - \\ \hline \multirow{6}{*}{EBF} & EVI & 0.23 \({}^{**}\) & 0.50 \({}^{**}\) & 0.34 \({}^{**}\) & 0.28 \({}^{**}\) & 0.41 \({}^{**}\) \\  & LST & - & 0.03 & 0.59 \({}^{**}\) & 0.81 \({}^{**}\) & 0.54 \({}^{**}\) \\  & LSWI & - & - & 0.21 \({}^{*}\) & 0.20 \({}^{**}\) & 0.29 \({}^{**}\) \\  & PAR & - & - & - & 0.52 \({}^{**}\) & 0.42 \({}^{**}\) \\  & Ta & - & - & - & - & 0.44 \({}^{**}\) \\  & GPP\_EC & - & - & - & - & - \\ \hline \multirow{6}{*}{ENF} & EVI & 0.52 \({}^{**}\) & 0.41 \({}^{**}\) & 0.57 \({}^{**}\) & 0.57 \({}^{**}\) & 0.55 \({}^{**}\) \\  & LST & - & 0.50 \({}^{**}\) & 0.77 \({}^{**}\) & 0.92 \({}^{**}\) & 0.80 \({}^{**}\) \\  & LSWI & - & - & 0.59 \({}^{**}\) & 0.57 \({}^{**}\) & 0.53 \({}^{**}\) \\  & PAR & - & - & - & 0.83 \({}^{**}\) & 0.80 \({}^{**}\) \\  & Ta & - & - & - & - & 0.89 \({}^{**}\) \\  & GPP\_EC & - & - & - & - & - \\  & EVI & 0.46 \({}^{**}\) & 0.09 & 0.10 & 0.69 \({}^{**}\) & 0.91 \({}^{**}\) \\  & LST & - & \(-\)0.61 \({}^{**}\) & 0.66 \({}^{**}\) & 0.90 \({}^{**}\) & 0.44 \({}^{**}\) \\  & LSWI & - & - & \(-\)0.28 \({}^{**}\) & \(-\)0.39 \({}^{**}\) & 0.14 \({}^{**}\) \\  & PAR & - & - & - & 0.55 \({}^{**}\) & 0.16 \({}^{**}\) \\  & Ta & - & - & - & - & 0.65\({}^{**}\) \\  & GPP\_EC & - & - & - & - & - \\ \hline \multirow{6}{*}{MF} & EVI & 0.74 \({}^{**}\) & 0.22 \({}^{**}\) & 0.57 \({}^{**}\) & 0.82 \({}^{**}\) & 0.95 \({}^{**}\) \\  & LST & - & \(-\)0.28 \({}^{**}\) & 0.75 \({}^{**}\) & 0.97 \({}^{**}\) & 0.74 \({}^{**}\) \\ \cline{1-1}  & LSWI & - & - & 0.03 & \(-\)0.15 \({}^{**}\) & 0.30 \({}^{**}\) \\ \cline{1-1}  & PAR & - & - & - & 0.75 \({}^{**}\) & 0.63 \({}^{**}\) \\ \cline{1-1}  & Ta & - & - & - & - & 0.83 \({}^{**}\) \\ \cline{1-1}  & GPP\_EC & - & - & - & - & - \\ \hline \hline \end{tabular}
* represents P \(<\) 0.05 and ** represents P \(<\) 0.01.  
\end{table} 
Table 6: The correlation coefficient (r) values between various factors, including EVI, LST, LSWI, PAR, air temperature (Ta), and GPP_EC. factors were used for estimating GPP. Table 3 and Table 5 show that MOD17 presents a comparable performance with MOD_ALG [6]. Through comparing flux measured climatic data with DAO data, [PERSON] _et al._[31] indicated that climatic data usually displayed good agreement for VPD and minimum temperature, but a high bias for DAO PAR. Therefore, we suggest that the uncertainty of climatic factors has little impact on MOD17. However, FPAR, as a key forcing variable of remote sensing, has a large uncertainty owing to the data qualities, thus it may be an important reason for underestimated peak values of GPP_EC [32; 33; 34; 31]. Additionally, FPAR is derived from MODIS NDVI, which is sensitive to canopy background variations and saturates in areas with dense tree canopy, especially in the growing season [35; 36]. It is known that the above GPP models are highly dependent on each climatic factor, including minimum, maximum, and mean temperatures, water vapor, and PAR, which may majorly affect the model uncertainties, errors, and accuracies.