In this study, we use the ratio of GPP to ET to define WUE, which is the most widely used method in most studies. There are three ways to obtain measurements of ecosystem WUE, namely, eddy covariance (EC), process-oriented ecosystem models [4; 6; 27; 28], and remote sensing measurements [29]. Of these three methods, EC measurements have the highest accuracy, but applications are limited by its small scale [30; 31]. Modeling methods can provide long-term and large-scale WUE estimates, but errors caused by model uncertainty are a problem. In recent years, an increasing number of studies have used remote sensing data to calculate WUE. On the one hand, the WUE obtained via remote sensing exhibits a high correlation with the WUE measured by EC, and has been verified in both global and region studies [4]. On the other hand, remote sensing data can solve problems related to the finite scale of EC observations and the errors of ecological model simulations [31; 32]. Therefore, the WUE in this study is calculated by GPP/ET based on remote sensing data. The temperature vegetation dryness index (TVDI) has been applied as a very popular drought index in many studies [33; 34; 35], and the accuracy of the TVDI has been verified in different areas and different vegetation landscapes. [PERSON], et al. [36] constructed a TVDI model using the triangular method from moderate resolution imaging spectroradiometer (MODIS) satellite images over two states of the United States, and the result showed a reasonable relationship between the TVDI and the soil moisture content at different depths. [PERSON], et al. [37] compared the relationship between the TVDI calculated from the MODIS data and the underground soil moisture of farmland and native grassland in Argentina. A high correlation was found (R\({}^{2}\) > 0.69), indicating that the combination of surface temperature and reflectance data can be used to monitor the underground soil moisture in vegetation areas. [PERSON], et al. [38] investigated the drought characteristics in arid regions of Mongolia using MODIS satellite remote sensing images. The comparison of the drought index and the soil moisture content at a depth of 10 cm revealed a correlation coefficient of 0.74. Therefore, previous studies have shown that the TVDI based on MODIS satellite remote sensing data can adequately monitor and quantify the impacts of drought on ecosystems. In this study, we chose the TVDI based on MODIS data as the drought index, as MODIS data can maintain a spatial resolution consistent with that of the ecosystem WUE. Central Asia is one of the largest arid regions in the world, and includes Kazakhstan (KAZ), Turkmenistan (TKM), Uzbekistan (UZB), Kyrgyzstan (KGZ), Tajikistan (TJK), and the Xinjiang Uygur Autonomous Region of China (CHN) [39]. In recent years, climate change and frequent human activities have caused tremendous changes in Central Asia. A recent study showed that increasing temperature and decreasing precipitation on a global scale led to a severe drought in Central Asia, and these changes will potentially impact vegetation growth and ecosystems in Central Asia [40]. However, this condition also provides an opportunity to study the response mechanisms of ecosystems to drought. In this study, MODIS GPP and ET measurements were used to calculate the WUE, and the relationships between ecosystem WUE and drought were examined in Central Asia from 2000 to 2014. The objectives of our study were as follows: (1) calculate and compare the mean WUE values from 2000 to 2014 at different drought levels, biome types, and countries; (2) calculate the spatiotemporal patterns of WUE and drought characteristics; (3) quantify the relationship between WUE and the drought index in drought and post-drought periods and attempt to determine the potential impact of drought on ecosystems; and (4) evaluate the response of WUE to variations in dry-wet conditions after drought.