## 3 Results
### Dominant Drivers for \(Gpp_{\mathit{summer}}\) and \(Gpp_{\mathit{autumn}}\)  
The dominant drivers for \(Gpp_{\mathit{summer}}\) showed similar spatial patterns in all three GPP products (Figure 6a-c). In summer, the arctic plant productivity was strongly correlated with temperature, whereas temperate regions showed soil moisture (SM) as the dominant driver. These temperature-dominant patterns also agreed with the patterns for positive legacy effects of \(T_{\mathit{spring}}\) on \(Gpp_{\mathit{summer}}\) (Figure 3d-f). Legacy effects from \(Gpp_{\mathit{spring}}\) were the dominant driver in many regions, but legacy effects from \(T_{\mathit{spring}}\) were not strong enough to surpass the importance of other drivers. In contrast to summer, the dominant drivers of \(Gpp_{\mathit{autumn}}\) were more diverse among the three GPP products (Figure 6d-f). GOSIF-GPP showed strong dependence on current seasonal drivers (e.g., \(T_{\mathit{autumn}}\), VPD\({}_{\mathit{autumn}}\), and SM\({}_{\mathit{autumn}}\)). For NIRv-GPP, the legacy effects were mostly dominated by \(Gpp_{\mathit{spring}}\). For FluxSat-GPP, \(Gpp_{\mathit{summer}}\), \(T_{\mathit{summer}}\), and SM\({}_{\mathit{summer}}\) were found to be the most important drivers. The Importance of Drivers on \(Gpp_{\mathit{summer}}\) and \(Gpp_{\mathit{autumn}}\) Aggregated on the Biome Level  
The important drivers for \(Gpp_{\mathit{summer}}\) and \(Gpp_{\mathit{autumn}}\) were analyzed based on the biome level (Figure 7). Both environmental variables and biological variables (i.e., pre-season GPP) have been accounted for. Generally, the largest number of pixels that showed the  
Figure 6: Dominant variables that explain variance of \(Gpp_{\mathit{summer}}\) (**a–c**) and \(Gpp_{\mathit{autumn}}\) (**d–f**) for the three GPP products (i.e., GOSIF-GPP, NIRv-GPP, and FluxSat-GPP). The dominant driver was identified based on the highest correlation coefficient (absolute value). significant correlation was found in FluxSat-GPP, followed by GOSIF-GPP and NIRv-GPP (Table 4). For the spatial patterns of the dominant driver, only the pixels with significant correlations were considered. The dominant driver varied depending on biome types, but overall, all three GPP data sets showed that among all the biomes, the smallest legacy effects from pre-season GPP were found in ABS and AG. These effects persisted in both summer and autumn. For \begin{table}
\begin{tabular}{l l l l l l l} \hline  & \multicolumn{3}{c}{**Summer**} & \multicolumn{3}{c}{**Autumn**} \\  & **GOSIF-GPP** & **NIRv-GPP** & **FluxSat-GPP** & **GOSIF-GPP** & **NIRv-GPP** & **FluxSat-GPP** \\ \hline ENF & 46.18\% & 36.48\% & 56.66\% & 48.55\% & 47.24\% & 69.47\% \\ EBF & 59.94\% & 45.12\% & 68.81\% & 78.18\% & 63.39\% & 79.33\% \\ DNF & 39.11\% & 32.87\% & 53.77\% & 57.42\% & 36.52\% & 49.64\% \\ DBF & 58.45\% & 47.54\% & 44.92\% & 70.68\% & 47.96\% & 73.17\% \\ MF & 42.02\% & 41.90\% & 53.44\% & 52.64\% & 38.39\% & 63.91\% \\ ABS & 57.10\% & 25.67\% & 73.06\% & 48.50\% & 33.46\% & 46.82\% \\ TS & 85.36\% & 40.04\% & 81.64\% & 89.61\% & 54.85\% & 81.69\% \\ SA & 51.80\% & 34.46\% & 64.13\% & 51.20\% & 43.88\% & 60.05\% \\ AG & 62.32\% & 18.18\% & 59.88\% & 36.20\% & 19.67\% & 40.11\% \\ TG & 81.17\% & 56.69\% & 76.83\% & 62.53\% & 40.58\% & 63.32\% \\ PW & 51.81\% & 18.57\% & 57.05\% & 48.99\% & 24.44\% & 62.68\% \\ CRO & 76.06\% & 55.04\% & 57.67\% & 78.99\% & 50.22\% & 73.97\% \\ \hline \end{tabular}
\end{table} 
Table 4: Percentage of pixels with significant correlations, as seen in Figure 7 divided by biome-type and season. The first three columns correspond to summer, and the following to autumn. Figure 7: Relative importance (%) of environmental and biological drivers on \(GPP_{\mathit{summer}}\) (**a–c**) and \(GPP_{\mathit{autumn}}\) (**d–f**) based on different biomes. these two biomes, temperature was the dominant driver for \(G\mathit{PP}_{summm}\).