## 4 Discussion  
### Comparison of Big-Leaf Lue-Gpp Models  
The estimations of the big-leaf LUE-GPP models were consistent with those derived from the EC system, confirming that the former are useful in estimating \(GPP\) of agroforestry ecosystems in humid regions. Based on the performance evaluation of the LUE-GPP models in various ecosystem types [19; 46], the LUE-GPP model could be used for deciduous broad-leaved plants because APAR is directly related to leaf area index [47], while the distinctive seasonal phenological changes in its leaves can be completely captured by satellite [46; 48]. However, for some ecosystems with close canopies and evergreen species, the canopy does not seasonally vary during the growing season, and the simulation results were not accurate [11]. Therefore, the performance of the LUE-GPP model is closely related to the vegetation index, as photosynthesis occurs in the leaves. Seasonal variations in pigment concentration and the canopy characteristics of kiwifruit are evident, which may be one of the reasons for the satisfactory simulation results of the LUE-GPP models. All six big-leaf LUE-GPP models overestimated \(GPP\) during the observation period. This may be related to the high set value of \(LUE_{\text{max}}\). For example, \(LUE_{\text{max}}\) was only set to 1.04 g C MJ\({}^{-1}\) in MOD 17 products. Previous studies considered \(LUE_{\text{max}}\) as a crop-invariant parameter and calculated it according to a site-specific calibration [49]; by contrast, we determined it according to the leaf-scale measurements. Thus, calibrated \(LUE_{\text{max}}\) values would vary owing to differences in the quantification of environmental constraints for different \(LUE\) algorithms. However, for a specific crop, \(LUE_{\text{max}}\) should be constant. Therefore, to ensure a rigorous comparison of models, we used the same \(LUE_{\text{max}}\) value across the six big-leaf LUE-GPP models. Differences in the performances of the various big-leaf LUE-GPP models were attributed to differences in the quantification of environmental constraints and combinations of the different environmental constraints. Taking the \(T_{\text{s}}\) quantification method as an example, quantification of \(T_{\text{s}}\) using the MODIS model might not fully characterize the temperature stress on photosynthesis with linear function stress, whereas that of \(T_{\text{s}}\) using the CASA and EF-LUE models may be more reasonable (Figure 2). EF was used as a measure of moisture stress in the EC-LUE model, as an increase in the energy directed towards evaporating water signifies a large potential water supply [20]. Moreover, non-linear temperature constraints compound the physiological response of photosynthesis to temperature in the EF-LUE model [22]. Therefore, the EF-LUE model had the best performance among the six big-leaf LUE-GPP models. [PERSON] and [PERSON] [50] reported that the EF-LUE model outperformed the other LUE-GPP models at the farmland sites. The high accuracy of the TEC model may be attributed to a more accurate expression of \(W_{\rm s}\) (\(W_{\rm s}=ET_{\rm a}/ET_{\rm pot}\)) than other big-leaf LUE-GPP models. Similar to EF, the equaled \(ET_{\rm a}\) and \(ET_{\rm pot}\) indicate that underlying \(ET\) was limited by available energy rather than soil moisture [51]. However, the CASA model had a slightly lower accuracy than the TEC model, although they both used similar \(W_{\rm s}\) algorithms. This may be attributed to the limited water constraint in the CASA model by introducing a fixed factor of 0.5 in \(W_{\rm s\_CASA}\), which cannot fully characterize \(W_{\rm s}\) on _LUE_ (Figure 2). In addition, the \(T_{\rm s}\) algorithm in the CASA model may affect its performance. The better performance of the VPM may be attributed to the use of _LSWI_ in quantifying \(W_{\rm s}\) and consideration of the effect of phenological change in _LUE_, since _LSWI_ could accurately represent the water status of the vegetation canopy [52].