## 4 Discussion
### Uncertainties of the SWH Model  
Our results illustrated different performances across sites and PFTs. Further analysis indicated that the model performance for ET prediction was significantly correlated with the performance for GPP prediction (\(p<0.01\), Figure S3), indicating that accurate estimate of GPP is critical for ET simulation in the SWH model. In this study, GPP was estimated using a light-use efficiency model. The remotely-sensed NDVI product was a key driving variable in GPP estimation, which may introduce large uncertainty in some regions (discussed below). Uncertainties may also be incurred by the inaccurate estimate of light-use efficiency. Many efforts have been made to solve this issue, such as improving drought constraint [51], accounting for diffusive radiation [57; 58] and updating the empirical values in the equations of environmental constraints (e.g., \(T_{\text{min}}\), \(T_{\text{opt}}\), \(T_{\text{max}}\), \(VPD_{\text{max}}\)) in the GPP model [31]. Therefore, improving GPP estimation would be anticipated with the outcome of these efforts, which thus will be a benefit for improving ET modeling with the SWH model. Our results illustrated that the model performances were poorer in EBF and grasslands. Monitoring of tropical forests using shortwave radiation from satellite-based observations is difficult due to atmospheric effects, particularly severe cloud contamination in tropical regions with EBF ecosystems [6; 51]. Therefore, the lower-quality NDVI product likely underlies the poorer model performance in EBF (Figure 3). For grasslands, the most sensitive parameters are likely responsible for \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Type** & **Complexity** & **ET Partitioning** & **GPP Simulation** & **Reference** \\ \hline PML & Empirical & low & \(\surd\) & & & [PERSON] et al. (2008) [20] \\ PT-Fi & Empirical & low & \(\surd\) & & [PERSON] et al. (2008) [3] \\ Regression model & Empirical & Low & & & & [PERSON] et al. (2010) [5] \\ Model tree ensemble & Empirical & high & & & & [PERSON] et al. (2010) [5] \\ [PERSON]’s model & Empirical & low & & & & [PERSON] et al. (2013) [56] \\ EC-LUE & process & middle & & \(\surd\) & & [PERSON] et al. (2010) [46] \\ MODIS ET & process & middle & \(\surd\) & & & [PERSON] et al. (2011) [17] \\ RS-PM & process & low & & & & [PERSON] et al. (2007) [15] \\ PM-zhang & process & low & & & & [PERSON] et al. (2010) [47] \\ BESS & process & high & \(\surd\) & & & [PERSON] et al. (2011) [48] \\ SWH & process & middle & \(\surd\) & \(\surd\) & & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of ET models driven by remote sensing and climate data. Note that the models estimating ET indirectly with the energy balance approach are not included. PML: Penman—Monteith—Leuning, PT-Fi: Priestley—Taylor—Fisher. RS-PM: Remote Sensing Penman—Monteith. BESS: Breathing Earth System Simulator. the model uncertainty. As our sensitivity analysis indicated, the model was much more sensitive to the parameters used to estimate the soil surface resistance in grassland than in cropland and forest (Table 2). In addition, inaccurate estimates of GPP may be another important source of the poorer estimates of ET in grassland. For example, we recently compared four widely-used light-use efficiency-based GPP models. The results indicated that most models yielded poorer estimates of GPP in grassland than other ecosystem types [31]. The different model performances among biome types may also partly be determined by the scheme used to estimate ET components. In the community of the two-source ET models, three different vegetation parameterization schemes are adapted, i.e., layer approach, patch approach and hybrid approach. The SW model uses the layer approach, and it does not distinguish soil evaporation from inter-canopy soil surface and under-canopy soil surface. One limitation of the layer approach is that it suits uniformly-vegetated surfaces better [59].