## 4 Discussion
### Uncertainties and Remaining Issues  
Both TL-LUEn and TL-LUE separate a canopy into sunlit and shaded leaves, and TL-LUEn further describe nonlinear response of their respective photosynthesis to APAR. These two models demonstrated powerful ability to simulate GPP. However, there are still some uncertainties remained. As indicated by [PERSON] and [PERSON] [105], uncertainties in meteorological and LAI data, parameters, and model structure all might induce errors of simulated GPP. The change of linear response of GPP to VPD and PAR to nonlinear one and the inclusion of a soil moisture scalar might improve GPP simulation. Similar to MOD17, TL-LUEn and TL-LUE only use VPD and minimum air temperature as environmental constraints on GPP. VPD represents the effect of atmospheric dryness on vegetation photosynthesis as a result of stomatal conductance. Soil moisture also plays an important role in regulating GPP via effects on leaf cell turgor pressure directly affecting photosynthesis or by stomatal conductance [10, 115, 116]. Because VPD and soil water availability did not co-vary, it would be most appropriate to have soil water availability as a constraint on photosynthesis in addition to VPD [117]. In MOD17, soil drought stress was approximated through the increase in the sensitivity of GPP to VPD [118]. Photosynthesis is considered to be totally shut off during periods of very high VPD, but in fact soil moisture and other environmental conditions might be still favorable to maintain photosynthetic activity at a certain level even if atmosphere is very dry [105]. Thus, the lack of soil  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Simulations**} & \multicolumn{4}{c}{**TL-LUEn**} & \multicolumn{4}{c}{**TL-LUE**} & \multicolumn{4}{c}{**MOD17**} \\ \cline{3-10}  & & \(\varepsilon_{m}\) & \(\beta\) & \(\Delta\)GPP\({}_{\text{red}}\)(\%) & \(\varepsilon_{min}\) & \(\varepsilon_{min}\) & \(\Delta\)GPP\({}_{\text{red}}\)(\%) & \(\varepsilon_{max}\) & \(\Delta\)GPP\({}_{\text{red}}\)(\%) \\ \hline \multirow{4}{*}{\(\text{LUEn}\)} & \multirow{2}{*}{1} & 1 & - & - & \(-10.00\) & - & - & \(-10.00\) & - & \(-10.00\) \\  & & 2 & + & - & \(0.78\) & + & - & \(-1.91\) & & \\  & 3 & - & + & \(-2.23\) & - & + & 1.91 & & \\  & 4 & + & + & \(10.00\) & + & + & 10.00 & + & 10 water availability as a photosynthetic constraint in all three LUE models surely increases uncertainties in simulated GPP, especially for crops and grasses with shorter roots and high dependence on shallow soil moisture. In this study, parameters in three LUE models are assumed invariant seasonally, which could induce some uncertainties in optimized parameters and calculated GPP. Many studies have shown that these parameters vary with both temperature and vegetation types [14, 119, 120]. [PERSON] _et al._[6] recently reported that with the consideration of seasonal changes of two parameters associated with temperature, the two-leaf nonlinear hyperbolic model (_i.e._, TL-LUEn) could simulate GPP as well as a process-based model. [PERSON] _et al._[121] indicated that the exclusion of seasonality of parameters in one-leaf and two-leaf LUE models is one of major drivers responsible for the failure of these models to capture the seasonality of GPP well. Thus, the proper representation of seasonal variations of these parameters needs further investigation. Simulated GPP is also affected by the quality of meteorological and LAI inputs. In the calibration and validation periods, the models were driven by tower-measured meteorological data and processed MODIS LAI. The errors caused by inaccuracies of meteorological inputs are likely relative small [41]. However, the MODIS LAI contained considerable uncertainties, especially for crops [122], mainly caused by uncertainties in land cover and surface reflectance inputs and in the LAI inversion algorithm, and by the prevalence of persistent cloud cover [105].