## 3 Results and Discussion
### Comparison with Other Satellite Data-Driven GPP Estimates  
Next, we compare FluxSat-7 and NIR\({}_{V}\)-based GPP with that from state-of-the-art data-driven FLUXCOM-RS and VPM. Figure 7 shows 2D histograms of FluxSat-7, NIR\({}_{V}\)-based and FLUXCOM-RS eight-day GPP estimates at 0.0833\({}^{\circ}\) resolution versus independent FLUXNET 2015 data. At this spatial resolution, all three generally overestimated GPP at low values and underestimated at high values. FluxSat-7 had the best performance at the highest values of FLUXNET 2015 GPP. All correlations were statistically different (\(p\)-values \(<\) 0.0001). FluxSat-7 showed the highest overall correlations with respect to FLUXCOM 2015. As shown above, comparisons with FLUXCOM 2015 at the full spatial  
Figure 6: Similar to Figure 4 (same data sample), but showing two-dimensional histograms of eight-day averaged GPP estimates with the (**a**) FluxSat-7, (**b**) NIR\({}_{V}\)-based, and (**c**) FluxSat-N models using the highest spatial resolution MCD43D dataset (0.0083\({}^{\circ}\)) versus collocated FLUXNET 2015 GPP estimates. Units of GPP are \(\text{g}\,\text{C}\,\text{m}^{-2}\,\text{d}^{-1}\).  
resolution of MCD43D (0.0083\({}^{\circ}\), Figure 6) provided superior results as compared with results at the spatial resolution of FLUXCOM-RS (0.083\({}^{\circ}\)).  
We note that FLUXCOM-RS was not trained with FLUXNET 2015 data, but rather with a different processing of an overlapping subset of eddy covariance flux tower data from the La Thuile dataset [10]. This may explain the bias of FLUXCOM-RS with respect to FLUXNET 2015 that was not present with respect to the La Thuile training dataset [10]. The bias may be easily removed using a linear regression with respect to FLUXNET 2015. When this was done, we obtained statistics that were similar to the single fit models presented in Section 3.2 applied at the same spatial resolution; the highest GPP values from the US-Ne3 site were still not well matched with FLUXCOM-RS after this adjustment.  
Figure 8 similarly shows 2D histograms for monthly GPP averages for VPM, FluxSat-7 and NIR\({}_{V}\)-based versus FLUXNET 2015. Again, all models reproduced the tower-based GPP estimates reasonably well. As expected, results were improved as compared with those at lower spatial and higher temporal resolution in Figure 7. FluxSat-7 showed a somewhat lower spread and a bit less underestimation at high GPP values with statistically-significant improvements in correlations over the other two models.  
Figure 8: Similar to Figure 7, but showing two-dimensional histograms of monthly averaged GPP estimates with the (**a**) FluxSat-7, (**b**) NIR\({}_{V}\)-based and (**c**) VPM models, with VPM at 0.05\({}^{\circ}\) spatial resolution and others at 0.083\({}^{\circ}\) resolution versus collocated FLUXNET 2015 GPP estimates. Results are for 64 sites and 3702 individual data points. Units of GPP are g C m\({}^{-2}\) d\({}^{-1}\).  
Figure 7: Similar to Figure 4 (same data sample), but showing two-dimensional histograms of eight-day averaged satellite datasets (**a**) FluxSat-7, (**b**) NIR\({}_{V}\)-based, and (**c**) FLUXCOM-RS at a lower spatial resolution (0.0833\({}^{\circ}\)) versus collocated FLUXNET 2015 GPP estimates. Units of GPP are g C m\({}^{-2}\) d\({}^{-1}\).