## 4 Discussion  
The R2 between GPPMODIS-GPP, GPPPEC-LUE, and GPPPEC showed the strong correlation between modelled GPP and measured GPP. The performance of RMSE varied at different sites. Compared with GPPMOD17, GPPPMODIS-GPP offered better GPP prediction at the US-Ton site and US-Bi2 site, and GPPPEC-LUE offered better GPP prediction at the US-Ton site, US-Var site, and US-Rws site. At the US-Ton site, GPPMODIS-GPP and GPPPEC-LUE obtained the best performance. Previous research showed that the savanna GPP estimation accuracy was smaller than other biome types because of the misclassification and sparse vegetation cover [50; 74; 80]. However, the woody savanna has a denser vegetation cover than the savanna [74; 80; 81]. This structure can reduce the influence of understory vegetation and improve GPP estimation accuracy [82]. Additionally, GPPMODIS-GPP and GPPPEC-LUE also  
Figure 8: Spatial distribution of the annual mean GPP from the MODIS-GPP algorithm (**a,d**), EC-LUE model (**b,e**), and MODIS-GPP products (**c,f**) in 2017 and 2018 and the maximum daily GPP from the MODIS-GPP algorithm (**g,j**), EC-LUE model (**h,k**), and MODIS-GPP products (**i,l**) in 2017 and 2018.  
obtained a good performance at the US-WCr site and US-PFa site. This finding was consistent with previous studies [5; 83]. For the US-WCr site, both GPPMODIS-GPP and GPPEC-LUE performed best in estimating GPP. The reasons for this can be summarized as follows. Firstly, DBF has obvious seasonal and phenological characteristics which can be monitored by satellites in real time [32]. Secondly, the vegetation structure of DBF is simple and the vegetation coverage varies greatly in different seasons, so the estimation of FAPAR is relatively simple and accurate [32]. Thirdly, DBF usually grows at mid and high latitudes with less cloudiness so that high-quality time-series images can be obtained [44]. For the US-PFa site, GPPMODIS-GPP and GPPEC-LUE represented the growth status of MF. However, compared with GPPMODIS-GPP, GPPEC-LUE was overestimated (RMSE = 3.23 gC/m\({}^{2}\)/d, bias = 2.07 gC/m\({}^{2}\)/d). The factor that caused this overestimation was the complex vegetation structure of the mixed forest, which had various \(\varepsilon_{\text{max}}\) due to abundant plant species. Moreover, the leaf shape, tree height, and light energy absorption conditions are different among tree species. Therefore, a single \(\varepsilon_{\text{max}}\) used in the input model causes certain errors [84; 85]. The performance of GPPMODIS-GPP and GPPEC-LUE at two shrulband sites varied. For the US-Rls site, GPPMODIS-GPP and GPPEC-LUE both had a moderate correlation against GPPEC, with R\({}^{2}\) = 0.63 and 0.57, respectively. However, for the US-Rws site, the accuracy of GPPMODIS-GPP was the lowest, and the performance of GPPEC-LUE was better than that of GPPMODIS-GPP. The factor that caused this phenomenon was the estimation accuracy of GPP at shrulband sites, which varied in different areas [22; 86]. The growth of shrubland is mainly affected by water, so more consideration should be given to water influence factors when estimating shrubland GPP [81; 84]. Therefore, for shrubland in temperate and frigid zones, GPP estimation accuracy is higher, but in the tropics, affected by VPD and FAPAR, the accuracy of shrubland GPP estimation is lower. At the US-Var site, the performance of GPPEC-LUE was also better than GPPMODIS-GPP, and the accuracy of the GPPEC-LUE was the second highest (R\({}^{2}\) = 0.67). The accuracy of GPPMODIS-GPP (R\({}^{2}\) = 0.49) was consistent with that of GPPMOD17 (R\({}^{2}\) = 0.46). However, the performance of GPPMODIS-GPP at the US-GLE site was far better than that of GPPEC-LUE. For the US-GLE site, the accuracy of GPPMODIS-GPP was worse than the US-Ton site (R\({}^{2}\) = 0.72).