## 5 Conclusions  
In this study, by including the spatial and temporal variations in the maximum photosynthetic capacity rate (i.e., V\({}_{\text{cmax}}\)) derived from the leaf chlorophyll metric using a remote-sensing-driven process-based model (i.e., FGM), we improved the estimation of the European GPP dynamics from 2001 to 2016 at 8-day time intervals and a 500 m spatial resolution. Compared with the traditional method of fixing the V\({}_{\text{cmax}}\) as a PFT-specific constant using the empirical parameterization method, we obtained an improved model performance by modeling GPP considering the spatial and temporal variations in V\({}_{\text{cmax}}\). The FGM predictions revealed a greening and more productive Europe, consistent with the existing global-scale GPP products and recent literature reports of enhanced carbon sinks in boreal and temperate regions. Our reanalysis suggests that a process-based GPP model using [PERSON]'s photosynthesis model requires the careful parameterization of  
Figure 12: Comparison of V\({}_{\text{cmax}}\) derived from the LCC, FFT-specific V\({}_{\text{cmax}}\), and field-measurements for the following sites: **(a)** ULES94, **(b)** BERL95, **(c)** ORSA97, **(d)** ESSW94, **(e)** ALIT98, **(f)** TRIT04, **(g)** PLFR99, **(h)** NOIT01-03, and **(i)** GIGE00. The geographical map in **(j)** shows the locations of these nine sites in Europe. The violin plot in **(k)** compares the monthly mean values of V\({}_{\text{cmax}}\) measurements and corresponding V\({}_{\text{cmax}}\) derived from LCC-derived V\({}_{\text{cmax}}\) maps. The scatter plot in **(l)** validates the LCC-derived V\({}_{\text{cmax}}\) using time series field measurements collected at site NOIT01-03 during the growing season (April to October). The abbreviations in **(a–i)** denote the PFTs of each site according to field surveys of plant species. Four PFTs are included, including deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), and grasslands (GRA). V\({}_{\text{cmax}}\) to accurately represent the photosynthetic capacity of terrestrial ecosystems. This study contributes to a better understanding of the role of European vegetation in the global carbon cycle. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15051172/s1](https://www.mdpi.com/article/10.3390/rs15051172/s1)]([https://www.mdpi.com/article/10.3390/rs15051172/s1](https://www.mdpi.com/article/10.3390/rs15051172/s1)), Figure S1: Validation of the FGM model performance at different cropland sites against EC-GPP; Figure S2: A closer look at the LAI time series revealed that GLASS missed some of the second growing phases due to crop rotation at the BE-Lon site; Figure S3: Spatial and temporal patterns of LCC-based V\({}_{\text{cmax}}\) from 2001 to 2016 at an 8-day interval; Figure S4: Interannual dynamics of mean downward solar radiation, air temperature, and vapor pressure deficit during the period from 2001 to 2016. **Author Contributions:** Conceptualization, Q.W. ; methodology, S.C. and C.S. ; validation, Q.W. ; formal analysis, Q.W. ; investigation, Q.W. ; resources, Q.W. ; data curation, Q.W. ; writing--original draft preparation, Q.W. ; writing--review and editing, Y.Z., C.S., J.J., W.J., L.W. and J.J.; visualization, Q.W. ; supervision, C.S. ; funding acquisition, Q.W., L.W. and J.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China (grant No. 42201381), the open fund of the State Key Laboratory of Remote Sensing Science (grant No. OFSLRSS202209), the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (grant No. X21019), the National Key Research and Development Program of China (grant No.