# Evaluation of Remote Sensing-Based Evapotranspiration
## 4 Results and Discussion  
### Evaluation of Remote Sensing-Based ET Datasets  
To evaluate the utility of the selected three remote sensing-based ET datasets (namely, PML, FLUXCOM, and GLEAM) in hydrological model performance, the accuracy of these datasets firstly needs to be assessed. In situ observations of pan evaporation are used to evaluate the accuracy of areal ET estimates based on these datasets in 2004-2008, using the five evaluation indices shown in Section 3.3.  
As shown in Figure 3, the areal daily remote sensing-based ET estimates are compared with those calculated by in situ observations of pan evaporation from six hydrological stations. Because the temporal resolution of PML is 8 days, the temporal resolution of pan evaporation is redefined to 8 days. Thus, the plot dots in Figure 3 for PML are much fewer than the others. From Figure 3, it can be observed that the areal daily ET from PML, FLUXCOM, and GLEAM is rather consistent with the in situ observations, with high R and KGE values (R > 0.83, and KGE > 0.72), and low PBIAS, RMSE, and ME values (PBIAS < 24.16%, RMSE < 0.86, and abs (ME) < 0.6). This confirms the conclusions found in previous studies, which reveal that the three remote sensing-based ET datasets have high accuracy [26; 44; 45]. The comparison results reveal that GLEAM has the best performance at the Jinhua River Basin, followed by FLUXCOM and PML. All remote sensing-based ET datasets slightly underestimate the areal daily ET, especially low and high ET values. This is due to the fact that in situ observations of pan evaporation are more similar to PET in humid regions [46]. Figure 4 shows time series of areal monthly ET from the three datasets and in situ observations of pan evaporation. It can be seen that high ET values mainly occur from May to September. As for temporal variation, FLUXCOM and GLEAM present better performance in capturing the dynamics of monthly ET than PML, as PML shows wide underestimation.  
Figure 2: Flow chart of this study.  
The differences in the performance of the three remote sensing-based ET datasets are mainly owing to the differences in the forcing data and the algorithms used in the three datasets. As is well known, the three components of ET are vegetation transpiration, soil evaporation, and vaporization of intercepted rainfall. Soil moisture plays a dominant role in soil evaporation. The estimation algorithm of PML lacks the soil moisture as input [28; 29]. For FLUXCOM, the index of water availability is calculated for each forcing dataset based on daily precipitation and potential evapotranspiration, which is different from soil moisture [22]. For GLEAM, its separate soil module utilizes precipitation to drive the multi-layer soil model and simultaneously assimilates a remote sensing-based soil moisture dataset, which can increase the accuracy of ET estimates [23; 34]. Moreover, because the depth of the soil column affecting the evaporation rate depends on the rooting depth of vegetation, the soil water balance in GLEAM is calculated for each land cover type individually, which is able to improve the ET estimates for different land cover types [26].  
Figure 4: Temporal comparison of monthly ET between remote sensing-based datasets with in situ observations of pan evaporation.  
Figure 3: Comparison of the daily ET data derived from three remote sensing-based datasets with in situ observations of pan evaporation.