## 4 Conclusions  
Here, we have presented a suite of satellite data-driven models based on the LUE concept to estimate GPP globally. The models were trained (or calibrated) using a subset of eddy covariance flux tower data and evaluated using a set of independent flux tower data. The models did not have any explicit dependence upon PFT; they were trained and evaluated with data across all PFTs with the exception of sites with high SIF relative to NDVI that were trained separately and generally coincided with high fractions of C4 maize crops. These areas represent only a few percent of the total land surface. Our best satellite data-driven models outperformed more complex state-of-the-art data-driven models that may or may not also incorporate ancillary information such as meteorological data. We find that using SW\({}_{\text{TOA}}\) (similar to clear-sky SW radiation) with constant or slowly varying (on seasonal time scales) LUE effectively accounts for the dependence of LUE on incident (cloudy-sky) radiation; specifically, light use efficiency increases with cloudiness index or diffuse radiation fraction in such a way as to produce a relatively constant GPP over a wide range of cloud conditions, and our model effectively accounts for this. This may explain why our GPP estimates are higher than  
Figure 18: Evaluation using monthly data at 0.05\({}^{\circ}\) resolution for independent sites: (**a**) tropical BR-Sa3 site and (**b**) sites above 60\({}^{\circ}\)N, DK-ZaF (WET), FI-Hyy (ENF), FI-Lom (WET) and US-Prr (ENF). In (**b**), ENF sites are shown as purple (VPM) and green (FluxSat-7) and wetland (WET) sites are shown in blue (VPM) and red (FluxSat-7). other satellite-based estimates, particularly in the cloudy tropics. Our high derived GPP values in the tropics agree well with the one available tropical Tier 1 FLUXNET 2015 site that has high values. Higher values of estimated GPP in the tropics and also in the extra-tropical Northern Hemisphere lead to higher annual estimated values of GPP (140.8 Pg C y\({}^{-1}\) for 2007) with our best model. This is higher than other state-of-the-art satellite-based GPP estimates and on the high end of values from both prognostic and diagnostic models. We applied a relatively simple adjustment to NDVI data to mitigate the NDVI offset. Models that use NDVI or the two NDVI bands have the advantage that they can be applied to satellite data that date back to the early 1980s from the Advanced Very-High Resolution Radiometer (AVHRR) such as from [PERSON] et al. (2014) or to field-scale imagery from Landsat, Sentinel 2 or other commercial satellites. The linear combination of either two or seven bands provides more dynamic range than the NDVI that tends to saturate at high values. We find that inclusion of an LUE parameterization in terms of a second order polynomial function of NDVI helps to further explain some variability in GPP. This formulation presumably accounts for variations in the maximum carboxylation rate tied to the photoperiod. For available input VI-related satellite datasets, we find that better results are obtained when using BRDF-adjusted data as compared with non-adjusted data. We obtained improved results using NDVI computed from averaged reflectances or albedos rather than composited \"best-value\" NDVI data. We also confirm that in general, better statistical comparisons with the flux tower data were achieved when satellite data are used at high spatial resolution similar to the typical footprint of eddy covariance data (\(\sim\)1 km\({}^{2}\)) as compared with (5-8 km)\({}^{2}\). In terms of temporal resolution, the best results were obtained at monthly temporal resolution, as expected. However, results at eight-day or daily resolution were only marginally degraded as compared with monthly averages. The best results were obtained when our models took advantage of satellite-based SIF data to identify areas of high productivity. These models also outperformed the NIR\({}_{V}\) for estimation of global GPP.