## 1 Introduction  
Water use efficiency (WUE), the ratio of carbon assimilation or productivity to water loss, is a valuable and commonly-used index to investigate the relationship between terrestrial carbon and water cycles [1; 2; 3]. Improving WUE is an effective way of saving plant water consumption and optimizing water management. Therefore, it has been extensively studied at different scales (leaf scale, canopy scale and regional scale) [4; 5; 6]. At the ecosystem level, WUE can be calculated by the ratio of gross primary productivity (GPP) to evapotranspiration (ET). Eddy covariance techniques provide an effective way to quantify the magnitudes, patterns and changes in WUE at the ecosystem level over varied vegetation types [7; 8; 9; 10; 11; 12]. However, it is still difficult to obtain dynamic WUE maps over a large region or even at the global level based on eddy flux data measured at a limited number of tower sites.  
Satellite-based remote sensing techniques play an important role in assessing the impacts of global change on terrestrial ecosystems for their ability to provide land surface information on regional and global scales. Great progress has been made in the field of remote sensing for Earth observation in recent years, which brings us more remote sensing products with higher spatial, temporal and spectral resolution [13]. At present, the information on the spatiotemporal patterns of ecosystem WUE could be acquired from remote sensing methods. With the availability of satellite GPP and ET products, such as MODIS products (Moderate-Resolution Imaging Spectroradiometer, MOD17 for GPP and MOD16 for ET), it provides a direct way of retrieving WUE at the regional scale. MODIS GPP and ET products have been used to investigate the regional and global WUE of vegetation [3; 14]. However, the accuracy of WUE estimated from MODIS products has not been well quantified.  
Eddy covariance measurements have been regarded as an effective way to evaluate the performance of MODIS GPP and ET products, due to their representing fluxes at the ecosystem level covering the area between one hundred meters and several kilometers. Previous studies show that MODIS GPP tends to underestimate at high productivity sites (especially for croplands) and to overestimate at low productivity sites [15; 16; 17], and MODIS ET is generally overestimated in forest and underestimated in grassland and cropland [18, 19, 20, 21]. Although the accuracy of GPP and ET may reflect the accuracy of WUE to some degree, because WUE is estimated from them, there is still a difference between individual evaluation for GPP and ET and synthetic evaluation for WUE due to WUE being the ratio of GPP to ET. The errors in WUE from GPP and ET may be canceled out or be magnified. [PERSON] _et al._[22] found that meteorological data contributes an error of 28% to MODIS annual GPP. In contrast, [PERSON] _et al._[21] argued that the impact of meteorological data on GPP estimation is little, while the parameter maximum light use efficiency and remote sensing leaf area index/fraction of photosynthetically-active radiation (LAI/FPAR) have greater effects on GPP estimations. LAI/FPAR also have a great impact on ET estimation [21]. However, it is still unclear how input meteorological and remote sensing variables and model parameters influence the estimation of ET and, thus, WUE. Therefore, the objectives of this paper are to evaluate WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET at eight sites of the Chinese flux observation and research network (ChinaFLUX) and to the identify key factors that influence the accuracy of WUE derived from MODIS data.