## 5 Summary  
LUE models, VI-based models, and process-based models are the three main approaches for spatial-continuous GPP estimation. In this work, three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BTL) were adopted to obtain GPP estimates for a mountainous watershed (i.e., Wanglang Reserve). At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02-0.29 and root mean square errors ranging from 399-821 gC m\({}^{-2}\)yr\({}^{-1}\). Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancies in GPP estimates from different models. The BTL model considers the combined spatial effect of various environmental indicators and ecological processes on the vegetation photosynthesis process. The limitation of BTL over large areas is that it's complicated model structure requires a large number of input data and ecological parameters. MTL-LUE only considers the process of radiation conversion, and it also requires meteorological data as input. Although the understanding of the vegetation photosynthesis process in MTG is not as detailed as that in BTL and MTL-LUE, the MTG model takes advantage of extensive remotely sensed data and topographical information (i.e., estimating GPP without ground data), and can also be used to obtain mountain GPP over large areas. Based on the results over Wanglang Reserve, the following strategies are suggested for GPP estimation over large-scale mountainous areas: (1) if relevant ecological parameters and meteorological data are sufficient and reliable, the BTL model is the best choice, (2) if only reliable meteorological data are available in the study area, the MTL-LUE model is more suitable than BTL and MTG, and (3) if there is no reliable ground data in the study area, the MTG model can be used as the simulation tool. The following are available online at [[https://www.mdpi.com/article/10.3390/rs13183567/s1](https://www.mdpi.com/article/10.3390/rs13183567/s1)]([https://www.mdpi.com/article/10.3390/rs13183567/s1](https://www.mdpi.com/article/10.3390/rs13183567/s1)), Supplementary Figure S1: Observation towers of shrubland; Supplementary Table S1: Main model parameter values used for different vegetation types; Supplementary Equation (S1): Calculation of the standardized index of annual GPP. Conceptualization, X.X. and A.L. ; Methodology, X.X. ; Software, X.X. ; Validation, X.X. ; Formal Analysis, X.X. ; Investigation, X.X. ; Data Curation, X.X. and Z.Z. ; Writing--Original Draft Preparation, X.X. ; Visualization, X.N. ; Supervision, A.L. ; Project Administration, A.L., H.J. and J.B.; Funding Acquisition, A.L., H.J. and J.B. All authors have read and agreed to the published version of the manuscript. This study was supported from the National Key Research and Development Program of China (Grant No. 2020 YFA0608700), the National Natural Science Foundation of China (Grant No. 41631180), the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Grant No. 2019365), the National Key Research and Development Program of China (Grant No. 2016 YFA0600103), and the Chinese Academy of Sciences \"Light of West China\" Program. We extend our thanks to all the data providers. The DEM and Landsat data are available at the USGS website ([[https://ers.cr.usgs.gov/](https://ers.cr.usgs.gov/)]([https://ers.cr.usgs.gov/](https://ers.cr.usgs.gov/)), accessed on 1 December 2020).