# Evaluation of Remote Sensing-Based Evapotranspiration
## 1 Introduction
[21] compared several parameter calibration techniques, including calibration with only runoff, calibration with runoff and remote sensing-based ET data, and calibration with runoff and spatially distributed remote sensing-based ET data, and the results demonstrated that incorporating ET datasets into multi-variable calibration was indeed able to improve ET simulation and maintain runoff simulation. As there are various remote sensing-based ET datasets, it is difficult to select a suitable ET dataset incorporated into multi-variable calibration for a specific region, which seriously affects hydrological model performance. Different ET datasets have different characteristics, even within a specific study area, owing to the diverse remote sensing input datasets and diverse ET estimate models emphasizing different physical and physiological controlling factors [22; 23; 24]. Therefore, it is still meaningful to evaluate the effect of incorporating various remote sensing-based ET datasets into the multi-variable calibration of hydrological models. In this study, three datasets are selected, namely FLUXCOM, GLEAM, and PML. These datasets have been widely used, and their accuracy has been evaluated in many regions. As for FLUXCOM, this dataset was obtained via machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensingand meteorological data [22]. The use of FLUXNET lays the foundation for the accuracy of the FLUXCOM dataset. Furthermore, [PERSON] et al. [25] evaluated the accuracy of FLUXCOM with observation-based data around the world and they revealed that the performance of FLUXCOM is very good. As for GLEAM, [PERSON] et al. [26] evaluated its accuracy via eight sites from the Chinese Flux Observation and Research Network (ChinaFlux), and it achieved reasonable performance. The PML dataset has high accuracy when compared with ground observations from 95 global flux stations located across the globe [27; 28; 29]. In several studies, the GLEAM dataset has been applied in parameter calibration, and they obtained acceptable performance for both runoff and ET simulations [8]. Nevertheless, fewer studies have investigated the utility of PML and FLUXCOM in the multi-variable calibration of hydrological models. Furthermore, ET simulation has a greater impact on runoff simulation in humid regions than dry regions [24]. Additionally, the performance of low flow, intermediate flow, and high flow simulations based on multi-variable calibration with both runoff and ET is also worth exploring, and it is substantial for flood and drought forecasting. This study aims to (1) evaluate the accuracy of three selected remote sensing-based ET datasets (namely PML, GLEAM, and FLUXCOM) against in situ observations of pan evaporation; (2) investigate the utility of remote sensing-based ET datasets for improving hydrological model performance (runoff and ET simulations) in multi-variable calibration (Scheme II) by comparing them to those from calibration with only runoff (Scheme I); (3) explore the performance of low flow, intermediate flow, and high flow simulations based on Scheme II. To this end, this paper is structured as follows: Section 2 describes the study area and data sources. The description of the methodology for this study is presented in Section 3. Section 4 provides the results and discussion, and the conclusions are summarized in Section 5.