## 4 Discussion
### Comparisons of GPP Estimates from MTL-LUE, MTG, and BTL  
To improve GPP estimation over mountainous areas, we propose the use of MTL-LUE, MTG, and BTL by incorporating topographic characteristics into the LUE, VI-based, and process-based models. Their improvement at site scale (more closely aligning with tower-based GPP) has been proved in previous studies [18; 22; 23], and their improvement at the watershed scale has also been illustrated in the above section. Besides discussing the improvement of these three mountain GPP models at the watershed scale, one of the other objectives of this work was to compare the GPP estimates from MTL-LUE, MTG, and BTL over mountainous watersheds. Results showed that the relationships between annual GPP estimates from BTL and MTL-LUE (\(R^{2}\) = 0.29, \(RMSE\) = 399 gC m\({}^{-2}\)yr\({}^{-1}\)) and MTL-LUE and MTG (\(R^{2}\) = 0.34, \(RMSE\) = 798 gC m\({}^{-2}\)yr\({}^{-1}\)) were closer than that between BTL and MTG (\(R^{2}\) = 0.02, \(RMSE\) = 821 gC m\({}^{-2}\)yr\({}^{-1}\)). In general, the discrepancy of annual GPP estimates among MTL-LUE, MTG, and BTL might result from model parameters and structure. The GPP discrepancies of these models may stem from differently calibrated values of sensitive parameters, such as the maximum LUE in MTL-LUE [35; 88], the slope between GPP and the combination of temperature and greenness in MTG [12; 46; 50], and the maximum carboxylation rate in BTL [89; 90]. The sensitive parameters in MTL-LUE, MTG, and BTL are different, and their ecological definitions are also different. One solution is to calibrate these sensitive model parameters with tower-based GPP data, thus increasing the uniformity of the GPP estimates from these three different models. In this work, the NEE measurements were not enough to build reliable models for tower-based GPP estimation [76; 80]. As shown in Figure 9, a standardized index for annual GPP estimates was used to address the discrepancies caused by model parameters. The standardized index of multiple annual GPP estimates was observed to present a similar spatial distribution over Wanglang Reserve, suggesting that the calibration of model parameters in different models is important for GPP estimation over mountainous areas. Further work should be carried out to obtain more reference values of the sensitive model parameters across vegetation types as there are more mountainous observations. Different model structures adopted in MTL-LUE, MTG, and BTL may be another source for the GPP discrepancies. MTL-LUE is developed from radiation conversion efficiency, mainly assuming that (1) GPP is directly related to the APAR and actual LUE [24], (2) sunlit leaves can absorb both direct and diffuse radiation whereas shaded leaves only absorb diffuse radiation [10; 29; 30; 31], (3) direct radiation varies with the geometric relationship between the tilt of ground surface and the solar position [38; 84] and diffuse radiation decreases by the tilt of the ground surface itself and surrounding terrain [38; 40; 85], and (4) the portion of sunlit and shaded canopy areas is affected by the relative positioning of geotropic trees [43; 44; 45]. The main assumptions in the MTG model are that (1) EVI can effectively describe water stress, because vegetation suffering from drought always senses or partially loses foliage to conserve water [12; 91; 92], (2) LST can well characterize  
Figure 9: Spatial distributions of the standardized index of annual GPPML-LUE (**a**), GPPTL-LUE (**b**), GPPMOD17 (**c**), GPPMTG (**d**), GPPTG (**e**), GPPPITL (**f**), and GPPBEPS (**g**) over Wanglang Reserve. The description of the standardized index can be found in Supplementary Equation (S1).