## 3 Results  
The comparison between ground-measured and satellite-estimated GPP using the different models shows that the GPP estimates improved after adding radiation information to the NIRv-GPP model (from R\({}^{2}\) = 0.57, RMSE = 2.51, Akaike information criterion (AIC) = 49,522 to R\({}^{2}\) = 0.62, RMSE = 2.36, AIC = 46,212), and even more so after adding the CI for each biome (R\({}^{2}\) = 0.73, RMSE = 1.97, AIC = 36,622) (Figure 2). The improvement was lower (R\({}^{2}\) = 0.69, RMSE = 2.23, AIC = 37,756) (Figure S1) when using the 8 days 500 m LIS-CI-A1 product in the model instead of using a fixed CI value per biome type from [PERSON] et al. [24].  
The addition of PPFD information to NIRv improved the GPP estimates for all except two sites and for all vegetation cover types except grasslands and woody savanna (Figure 3). This improvement in the estimates after adding PPFD information was significantly correlated with the CI (Figure 3); the higher the foliage organization (lower CI), the greater the improvement in terms of correlation and certainty (higher increase in R\({}^{2}\) and greater decrease in RMSE, respectively). ENF, the vegetation cover type with the lowest CI (i.e., the highest foliage organization), was the cover type that improved the most after adding the PPFD information (Figure 3).  
When adding the CI information for each biome to the NIRv + PPFD model, the bias decreased, above all in ENF, for all vegetation cover types except WET (the improvement ranged from \(-1.97\) in ENF to \(-0.06\) in EBF; see Figure 4 and Figure S2).  
The GPP estimates improved at all latitudes after adding the radiation information, although bias increased, and no differences were detected between latitudes (Figure S2). After adding CI information, both uncertainty and bias decreased in all latitudinal groups. Of the vegetation cover types, ENF contributed most to the decrease in dispersion in all latitudinal ranges after adding the CI information (Figure S2).  
Finally, we checked the performance of each model when the data of evergreen needle-leaf forests--the most sensitive vegetation cover type to PPFD and CI--were separated in the three latitudinal ranges. The improvement in the GPP estimates was similar at all latitudinal ranges when PPFD was added to the model. Similarly, the improvements in the evergreen needleleaf GPP estimates when the CI information was added were not latitudinally dependent, and the bias decreased from 2.05 to 0.09 as evaluated over the whole dataset (Figure S3). The predictive ability of the three models fell as the latitude decreased. We found no differences in the improvements to the GPP estimates after adding radiation before and after the summer sol  
Figure 2: Relationship between the measured GPP and the GPP estimated from (i) NIRv, (ii) NIRv + PPFD and (iii) NIRv + PPFD + CI, for the whole dataset. CI used in the model was the CI per vegetation cover type from [PERSON] et al. [24]. Data correspond to daily mean data from 26 sites from 2000 to 2014. The red line corresponds to the linear regression and the black line to the 1:1 line. Different colours identify the vegetation cover types.  
Figure 3: Relationship between the clumping index and the variation in R\({}^{2}\) (\(\Delta\)R\({}^{2}\)) and RMSE (\(\Delta\)RMSE) between the ground-measured GPP and the satellite-estimated GPP after adding PPFD information to the NIRv model (GPPnirppfd vs. GPPnirv) in black for the different studied sites (Table 1) (CI calculated as the average annual value per site from the CI product LIS-CI-A1), and in red for the different vegetation cover types (CI for vegetation cover type as in [PERSON] et al. (2018)): evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), mixed forest (MF), open shrubland (OSH), grassland (GRA), woody savanna (WSA) and wetland (WET).