###### Abstract
Most LUE models are big-leaf models with the assumption that the whole canopy can absorb direct and diffuse solar radiation simultaneously, such as the Moderate-resolution Imaging Spectroradiometer (MODIS) GPP model (MOD17) [8], Vegetation Photosynthesis Model [28], and EC-LUE [9]. However, many studies have suggested that the treatment of sunlit and shaded leaves should be different in LUE models [10; 29; 30; 31; 32]. Sunlit leaves, which absorb both direct and diffuse radiation, are easily light-saturated, and thus their photosynthetic capacity is more constrained by low LUE. In contrast, the photosynthetic capacity of shaded leaves, which only absorb diffuse radiation, is normally constrained by low absorbed photosynthetically active radiation (APAR). To separate the treatment of sunlit and shaded leaves, [PERSON] et al. (2013) [33] proposed a two-leaf LUE model (TL-LUE) by combining the MOD17 model and the Boreal Ecosystem Productivity Simulator (BEPS) [29; 34]. The performance of TL-LUE has been validated for various vegetation types [35; 36; 37], but neglects topographic characteristics over mountainous areas. To address this problem, [PERSON] and [PERSON] [22] developed a mountain TL-LUE model (MTL-LUE) by considering (1) the variation of direct radiation resulting from the different geometric relationships between the tilt of the ground surface and the solar position [38; 39; 40; 41], (2) the shielding of diffuse radiation caused by the tilt of the ground surface itself and surrounding terrain [38; 40; 41; 42], and (3) the alteration of sunlit canopy area due to the different relative positions of geotropic trees [43; 44; 45]. The GPP estimates from the MTL-LUE model proved to better match the tower-based GPP than those from the TL-LUE model at a mountainous site [22]. VI-based models usually estimate GPP by exploring its direct correlation with remotely sensed green vegetation indices [11], such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). If GPP was able to be calculated entirely from remotely sensed data, it would be more convenient to have spatially and temporally continuous GPP estimates over larger-scale areas due to the development of the satellite remote-sensing technology. [PERSON] et al. (2008) [12] developed a temperature and greenness (TG) model based on the direct relationship between GPP and the combination of EVI and MODIS land surface temperature (LST), and its satisfying performance has been confirmed by various studies [12; 46; 47; 48; 49; 50]. In the model application of TG, EVI can be obtained from MODIS (~250 m) or Landsat spectral bands (~30 m), while LST is always provided by MODIS products at the spatial resolution of ~1 km. However, the ~1 km resolution MODIS LST product may lose a large portion of topographic characteristics over mountainous areas [51], and thus the TG model is more suitable for relatively flat areas. To improve the GPP estimation from TG, [PERSON] and [PERSON] [23] proposed a mountain TG model (MTG) by adding (1) an elevation-corrected factor for the effect of elevation on temperature [52; 53] and (2) a radiation-corrected factor for the contribution of the incoming solar radiation to the spatial variations of temperature [52; 54]. MTG has presented a better ability to capture the GPP variations than the TG model at sixteen mountainous sites [23]. Process-based models, such as the BEPS model [29; 34], the Biome-BGC model [55], and the Integrated Biosphere Simulator (IBIS) model [56], always use biological, climatic, and soil variables to describe the major components of ecological processes related to vegetation photosynthesis. However, most process-based models overlook the large spatial variation in GPP caused by impacts of topography on received radiation, temperature, and soil water. To improve GPP estimations from process-based models over mountainous areas, [PERSON] et al.