## 5 Conclusions  
By applying a process-based Farquhar GPP model (FGM) with multisource datasets ranging from 2001 to 2016, we showed a widespread and pronounced increase in GPP across Europe during this period. The spatial pattern of land surface greening matched well with the spatial pattern of enhanced GPP. We included spatiotemporal information on leaf photosynthetic capacity (i.e., V\({}_{\text{cmax}}\)) derived from leaf chlorophyll content in the FGM model. Factorial simulations suggested that the observed land surface greening and rising CO2 concentration tremendously boosted the annual total GPP across Europe. In contrast, the LULCC and enhanced VPD had significant negative impacts on GPP, which were neutralized by the positive effects of temperature warming and radiation brightening. Consequently, the combined impacts of LULCC, land surface greening, CO2 fertilization, and climate change were strongly positive. Changes in V\({}_{\text{cmax}}\) with both space and time are not well represented in current TBMs and result in uncertainties in GPP estimations. Although the interannual change effect of V\({}_{\text{cmax}}\) was minor in the limited 16-year study period, considering the spatial and seasonal variations in V\({}_{\text{cmax}}\) significantly improved GPP estimations at the regional scale. Thus, the process-based model simulation approach developed in this work should improve our understanding of the terrestrial ecosystem photosynthetic response to climate change across Europe. **Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs15051372/s1](https://www.mdpi.com/article/10.3390/rs15051372/s1)]([https://www.mdpi.com/article/10.3390/rs15051372/s1](https://www.mdpi.com/article/10.3390/rs15051372/s1)): Figure S1: Model validation accuracy for nine PFTs: (a) CRO, (b) DBF, (c) SH, (d) EBF, (e) GRA, (f) ME, (g) ENF, and (h) WET; Figure S2: Intercomparison of (a) interannual dynamics in GPP estimated by the FGM and other methods, including one empirical model, four light use efficiency (LUE) models, three machining learning methods, and one process-based biophysical model. (b) Scatter plots between the FGM GPP and other GPP products. As listed in Table S2, the COSIF GPP product was derived from the empirical relationships between GPP and SIF. The CCW, MOD17, VPM, and GLASS products are estimated based on LUE models. FLUXCOM GPP products are estimated by three machine learning approaches, including an artificial neural network (ANN), the multivariate adaptive regression splines method (MARS), and the random forest method (RF). In addition, one process model-based GPP product (i.e., BEPS) is also included; Table S1. Accuracy of model calibration and validation and the calibrated g1 for nine plant functional types (PFTs); Table S2. Information on nine GPP products for intercomparison. **Author Contributions:** Conceptualization, Q.W. ; methodology, S.C.; validation, Q.W. ; formal analysis, Q.W. ; investigation, Q.W. ; resources, Q.W. ; data curation, Q.W. and X.W. ; writing--original draft preparation, Q.W. ; writing--review and editing, J.J. and L.W. ; visualization, Q.W. ; supervision, Q.W. ; funding acquisition, Q.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 State Key Laboratory of Remote Sensing Science (Grant No. OF-SLRSS202209), 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. 2021 YFE0117500), and the 2020 China-CEEC Joint Education Project of Institutions of Higher Education Project \"Ecological Environment Monitoring to Urban Areas along China-Europe Railway based on Remote Sensing and Artificial Intelligence\".