## 3 Results
### Comparison between VIs-Based and Ecosystem Model-Simulated GPP Datasets  
A set of three global gridded GPP products for 2003-2018 were generated for each vegetation index based on universal models with optimal configurations, i.e., the optimal combination of the VIs and the other associated variables using the RF algorithm. We compared our Vis- and machine learning-based GPP datasets with three other GPP datasets (FLUXCOM GPP, GOSIF GPP, and TRENDY GPP).  
The spatial patterns of annual mean GPP for the six GPP products are similar: the highest values are found in tropical rainforest regions, with generally lower GPP values found in arid regions such as Australia, Central Asia, the southwestern United States, and southwestern Africa, as well as the cold regions at high northern latitudes (Figure 3). All products' latitudinal profiles also have good consistency (Figure S6).  
Global land is divided into the northern hemisphere (NH: 30\({}^{\circ}\)N-90\({}^{\circ}\)N) and tropical and southern hemisphere (SH+Trp: 90\({}^{\circ}\) S-30\({}^{\circ}\)N). Among our three sets of GPP products, both globally and regionally, LAI-based GPP had the highest annual mean GPP, whereas NIR\({}_{\
u}\)-based was the lowest; the interannual variability of the three sets is quite similar (Figure S7). Both SIF-based GPP and GOSIF-GPP were generated from GOSIF, and their annual mean GPP values are relatively close in the SH+Trp area; however, the former has higher annual mean and interannual variability in GPP than the latter, both globally and over the NH (Figures 4 and S7). The rest of the products are within the range of the 10 process-based models for each study scale, except for global LAI-based annual mean GPP (Figure 3). Interannual variability in FLUXCOM is substantially lower than that of other products because it did not account for the CO\({}_{2}\) fertilization effect (CFE) (Figure S8). Despite GOSIF-GPP not considering rising CO\({}_{2}\) concentrations, its interannual variability  
Figure 2: Universal models’ performance for each PFT along different latitude gradients using the RF method. (**a–c**) The R\({}^{2}\) values of the universal models that each vegetation index used as one of the model predictors. (**d–f**) The RMSE values of the universal models that each vegetation index used as one of the model predictors. The gray squares represent that there is no flux tower for the corresponding PFT and latitudinal band.  
was closer to TRENDY MMEM and our three GPP products. GOSIF may reflect the role of CO\({}_{2}\) in GPP to some extent.  
On the other hand, the linear trends of the various GPP products are quite different (Figure S7). Except for FLUXCOM, the remaining GPP products have an increasing trend in annual mean values (Figure S7a-c). Regarding interannual variability of anomalies and trends, our three GPP products are very close to each other (Figure S7d--Global: 0.52-0.54 Pg C\(\cdot\)yr\({}^{-2}\); NH: 0.27-0.29 Pg C\(\cdot\)yr\({}^{-2}\); SH+Trop: 0.24-0.26 Pg C\(\cdot\)yr\({}^{-2}\)). GOSIF-GPP and TRENDY MMEM all capture the significant increase in GPP in northern Europe, East Asia, South Asia, and northern North America (Figure S9). However, the Amazon region displays a wide divergence: our products and GOSIF-GPP show a decreasing trend in GPP (Figure S9a-d), but TRENDY MMEM shows a significant increase (Figure S9f). Furthermore, for eastern Siberia, LAI-based GPP, GOSIF-GPP, and TRENDY-MMEM consistently indicate a decreasing GPP trend (Figure S9c,d,f), but SIF- and NIR\({}_{v}\)-based GPP regularly show considerable GPP increases (Figure S9a,b).  
Figure 3: Spatial patterns of global annual mean GPP for 2003–2018. (**a–c**) represent GPP estimates based on each vegetation index with optimal configurations. (**d**) GOSIF-GPP; (**e**) FLUXCOM GPP. (**f**) represents the average of the multi-model ensemble mean.