## 3 Results
### Validating Gridded GPP Estimates Derived from GOSIF  
We estimated GPP from 2000-2017 at 8-day intervals and on a 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid across the globe based on the GOSIF product and the eight SIF-GPP relationships separately. The universal SIF-GPP relationship developed at the site level estimated GPP fairly well (R\({}^{2}\) = 0.72, RMSE = 2.04 g C m\({}^{-2}\) d\({}^{-1}\)). The biome-specific relationships and other forms of relationships also estimated GPP fairly well. These different SIF-GPP relationships led to an ensemble GPP product, referred to as GOSIF GPP. For each grid cell and each interval, we calculated the ensemble mean GPP and used the ensemble standard deviation as a measure of uncertainty in GPP. Our ensemble mean GPP derived from eight SIF-GPP relationships was generally highly correlated with the flux tower GPP for 91 open-access EC sites from FLUXNET 2015 Tier-1 across the globe at the 8-day timestep (R\({}^{2}\) = 0.74, RMSE = 1.92 g C m\({}^{-2}\) d\({}^{-1}\); Figure 3). The correlations at the 8-day timescale were consistently strong among biomes with R\({}^{2}\) ranging from 0.51 to 0.85. GPP was slightly underestimated for evergreen needleleaf forests, open shrulbands, and grasslands, and was slightly overestimated for evergreen broadleaf forests. We provided the distribution map of R\({}^{2}\) and RMSE for all flux sites in Figure S1. We also aggregated our 8-day GPP estimates to monthly and annual scales, and the aggregated GPP also agreed with flux tower GPP fairly well at both monthly (R\({}^{2}\) = 0.75, RMSE = 1.90 g C m\({}^{-2}\) d\({}^{-1}\)) and annual scales (R\({}^{2}\) = 0.65, RMSE = 1.21 g C m\({}^{-2}\) d\({}^{-1}\)). The R\({}^{2}\) and RMSE between estimated GPP and tower GPP for each biome at different scales are summarized in Table 2. The ensemble mean annual GPP in 2010 was strongly correlated to EC-MOD GPP (R\({}^{2}\) = 0.87, RMSE = 0.91 g C m\({}^{-2}\) d\({}^{-1}\)). The relationship was strong for most biomes and moderately strong for certain biomes with lower spatial variability (Figure S2). The estimated GPP could capture the interannual variability of tower GPP at the site level to some extent (R\({}^{2}\) = 0.77, p < 0.001; Figure 4a). Our GPP estimates also characterized the anomalies of annual GPP in each year (R\({}^{2}\) = 0.62, p\(<\)0.001; Figure 4b). The consistency in interannual variability between estimated and tower GPP was generally strong for non-forests sites and generally weak for forests sites (Figure S3). Both figures showed that GOSIF GPP underestimated the interannual variability of tower GPP to a certain degree. The spatial and seasonal variations of GPP based on the universal SIF-GPP relationship across the globe are illustrated in Figure S4. The GOSIF GPP had relatively strong seasonal cycles in the Northern Hemisphere and smaller seasonal fluctuations in the Southern Hemisphere. The prominently high GPP values were found in some agricultural regions in summer (e.g., the U.S. Corn Belt region, northeastern China, and central Europe). Figure 3: Validation of the GOSIF GPP product. Ensemble mean GPP versus tower GPP from 91 FLUXNET 2015 Tier-1 sites at (a) 8-day (y = 1.04x+0.14, R\({}^{2}\) = 0.74, RMSE = 1.92 g C m\({}^{-2}\) d\({}^{-1}\)); (b) monthly (y = 1.06x + 0.28, R\({}^{2}\) = 0.75, RMSE = 1.90 g C m\({}^{-2}\) d\({}^{-1}\)); and (c) annual (y = 1.02x + 0.35, R\({}^{2}\) = 0.65, RMSE = 1.21 g C m\({}^{-2}\) d\({}^{-1}\)) timescales. For comparison purposes, we also used the universal SIF-GPP relationship (site-level, with slope; Table 1) to generate coarse-resolution (1\({}^{\circ}\)) GPP estimates from the coarse-resolution SIF estimates (1\({}^{\circ}\), monthly) directly aggregated from discrete OCO-2 SIF soundings. We then compared the spatial patterns of our finer-resolution GOSIF GPP maps with those of the coarse-resolution GPP in three selected geographical regions (United States, Australia, and East Asia) (Figure 5).