## 3 Results
### Global SIF Product: Spatial Patterns and Seasonal Cycles of SIF  
Figure 5 shows that our predictive model predicted SIF well, with the absolute estimation error within \(\pm\)0.05 W m\({}^{-2}\)\(\mu\)m\({}^{-1}\) sr\({}^{-1}\) for the majority of the grid cells. The distribution of the estimation errors was close to the normal distribution. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Biome**} & \multicolumn{2}{c}{**Without Land Cover Type**} & \multicolumn{2}{c}{**With Land Cover Type**} \\ \cline{2-5}  & \(\mathbf{R^{2}}\) & **RMSE** & \(\mathbf{R^{2}}\) & **RMSE** \\ \hline ENF & 0.66 & 0.07 & 0.67 & 0.07 \\ \hline EBF & 0.43 & 0.08 & 0.45 & 0.08 \\ \hline DNF & 0.82 & 0.07 & 0.83 & 0.07 \\ \hline DBF & 0.87 & 0.08 & 0.88 & 0.08 \\ \hline MF & 0.81 & 0.08 & 0.82 & 0.08 \\ \hline CSH & 0.62 & 0.05 & 0.63 & 0.05 \\ \hline OSH & 0.46 & 0.06 & 0.46 & 0.06 \\ \hline WSA & 0.79 & 0.07 & 0.79 & 0.07 \\ \hline SAV & 0.75 & 0.07 & 0.75 & 0.07 \\ \hline GRA & 0.69 & 0.06 & 0.69 & 0.06 \\ \hline WET & 0.54 & 0.07 & 0.56 & 0.07 \\ \hline CRO & 0.83 & 0.08 & 0.84 & 0.07 \\ \hline All & 0.79 & 0.07 & 0.80 & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Validation of the predictive SIF model for each biome: Evergreen needleleaf forests (ENF), evergreen broadleaf forests (EBF), deciduous needleleaf forests (DNF), deciduous broadleaf forests (DBF), mixed forests (MF), closed shrublands (CSH), open shrublands (OSH), woody savannas (WSA), savannas (SAV), grasslands (GRA), croplands (CRO), and wetlands (WET). The units of the RMSE are W m\({}^{-2}\)\(\mu\)m\({}^{-1}\) sr\({}^{-1}\). Figure 4: The validation of the predictive SIF model: Scatterplot of observed OCO-2 SIF versus predicted SIF in 2017. The dashed line is the 1:1 line, and the solid line is the regression line. Compared with the coarse-resolution SIF maps directly aggregated from discrete OCO-2 SIF soundings (Figure 1), GOSIF provides spatially continuous SIF estimates with much finer spatial resolution and 8-day intervals for a much longer period (2000-2017) (Figure 6). GOSIF exhibited expected spatial and temporal variations in SIF across the globe (Figure 6). In the northern hemisphere, SIF had relatively strong seasonal cycles. In the spring (March, April, and May), SIF exhibited small values at the start of the growing season but then gradually increased over time. The summer (June, July, and August) exhibited the highest values, owing to favorable temperature and soil moisture conditions and long day length. In the autumn (September, October, and November), SIF showed intermediate values first and then approached zero at the end of the season because of the gradual senescence of vegetation. The majority of the areas in mid- and high latitudes showed the lowest values in the winter (December, January, and February) as the canopies of most ecosystems were dormant. The prominently high SIF values were found in the U.S. Corn Belt region in July and August, with SIF ranging from 0.6 to 1 W m\({}^{-2}\)\(\mu\)m\({}^{-1}\) sr\({}^{-1}\). High SIF values were also observed in other agricultural regions, such as northeastern China and central Europe. In the southern hemisphere, SIF exhibited smaller seasonal fluctuations. The pan-tropics areas showed high SIF values for most of the year and had slightly lower values in the dry seasons (austral winter: June, July, and August). Low SIF values were found in a large part of Australia throughout the year. Figure 5: The probability distribution of errors (estimated SIF minus the observed SIF) in 2016. Our new 0.05\({}^{\circ}\) SIF captured the seasonal variations presented in the 1\({}^{\circ}\) OCO-2 SIF maps directly aggregated from discrete soundings at four selected EC sites fairly well (Figure 7a,c,e,g). Compared to the discrete and noisy observations (likely due to the scale mismatch) found in the coarse-resolution maps, the continuous and smooth observations in our new SIF could support finer-scale applications.