## 5 Conclusions  
Our results demonstrate that the addition of daily radiation information (photosynthetic photon flux density, PPFD) and the clumping index (CI) for each vegetation cover type to the NIRv algorithm improves its ability to estimate GPP. The improvement is related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use, and radiation drives productivity. Evergreen needleleaf forest is the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information. Vegetation type is more determinant in the sensitivity to PPFD changes than latitude or seasonality. Our results highlight that foliar organization and tree structure play an important role in productivity. We advocate for the incorporation of PPFD and CI into NIRv algorithms to improve GPP estimates and GPP models.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15082207/s1](https://www.mdpi.com/article/10.3390/rs15082207/s1)]([https://www.mdpi.com/article/10.3390/rs15082207/s1](https://www.mdpi.com/article/10.3390/rs15082207/s1)), Table S1. Coefficient of determination (R2) and RMSE of the relationship between GPP and the different models according to whether data from before (DOY \(<\) 182) or after (DOY \(>\) 182) the summer solstice were used. Figure S1. Relationship between the measured GPP and the GPP estimated from NIRv + PPFD + CI, for the whole dataset using the 8 days clumping index product (LIS-CI-A1) [32; 33]. Different colours identify the vegetation cover types. Figure S2. Measured GPP vs. the GPP estimated by the different models after grouping the sites according to latitude. Different colours identify the vegetation cover types. Figure S3. Measured GPP vs. GPP estimated with NIRv, NIRv + PPFD and NIRv + PPFD + CI, fitting the model with the whole dataset and after grouping ENf data according to latitude: low (30-45\({}^{\circ}\), 3 sites), middle (45-53\({}^{\circ}\), 4 sites) and high (53-62\({}^{\circ}\), 3 sites) latitudes. The root mean square error (RMSE), the coefficient of determination (R2) and the bias are indicated (statistics of the general model are shown in red). The red line corresponds to the linear regression and the black line to the 1:1 line. Different colours identify the vegetation cover types.  
**Author Contributions:** I.F.: Conceptualization, data gathering, methodology, writing--original draft, writing--review and editing. A.D.: Data gathering, methodology, writing--review and editing. M.B.: Data gathering, methodology, writing--review and editing. G.Y.: Methodology, writing--review and editing. A.V.: Methodology, writing--review and editing. H.F.: Data, writing--review and editing. J.P.: Conceptualization, methodology, writing--original draft, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was financially supported by TED2021-132627B-I00 funded by MCIN, AEI/10.13039/501100011033 and the European NextGenerationEU/PRTR, the Catalan government project SGR 2021-01333 and the Fundacion Ramon Areces project CIVP20A6621. M.B. thanks the financial support provided by the Belgian Science Policy Office (BELSPO) within the framework of the STEREO III program (ECOPROPHET project, contract number: SR/00/334).  
**Acknowledgments:** This work represents a contribution to the CSIC Thematic Interdisciplinary Platform TELEDETECT.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.