###### Abstract  
Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from 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 BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a 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\({}^{-1}\). These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. 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 discrepancy of GPP estimates from different models. GPP estimation; mountainous areas; ecosystem models; remote sensing 2021 13 3567 1338367 1338367 1338367 1338367 1338367 138367 1338367 1338367 13838367 1338367 1383836  
Currently, the globally distributed eddy covariance (EC) systems can only provide GPP estimates over a limited spatial context due to the representation of carbon footprints [5]. Fortunately, the availability of satellite observations make it feasible to acquire important information about ecosystem components at a large scale [6; 7]. Various ecosystem models have been developed to obtain spatially-continuous GPP estimates, which can predominantly be categorized as Light Use Efficiency (LUE) models, Vegetation Index (VI) -based models, and process-based models. However, most of these GPP models overlook the effect of topography on the vegetation photosynthesis process [8; 9; 10; 11; 12], and limited attention has been given to carbon modeling over mountainous areas. The topographic characteristics of mountainous areas, such as steep slopes, altitude variation, and landscape fragmentation, greatly influence surface energy and hydrologic processes [13; 14; 15; 16], and thus affect the vegetation photosynthesis process [17; 18; 19; 20; 21]. Over the last decade, [PERSON] and [PERSON] (2020) [22; 23] and [PERSON] et al. (2009) [18] have made efforts to improve GPP estimations from LUE, VI-based, and process-based models over mountainous areas, respectively. LUE models are developed from the radiation conversion efficiency concept [24] and have been widely adopted to generate time-series of regional and global GPP products [25; 26; 27].