# Spatio-Temporal Changes in Water Use Efficiency and Its Driving Factors in Central Asia (2001-2021)
As an alternative, remote sensing has been shown to be a reasonable means of calculating WUE [18], as it not only makes up for the lack of covariance flux tower data for dryland ecosystems, but also allows the study of WUE to be extended to larger scales (e.g., global scales) [19; 20]. For example, by considering the effect of diffuse radiation, [PERSON] et al. developed a two-leaf light use efficiency model (TL-LUE) [21]. The Priestley Taylor jet propulsion laboratory model (PT-JPL) performs well in most ecosystem types [22]. The FLUXNET dataset, which is continuously refined and updated from flux tower data, can also be used with the MODIS dataset [23]. A number of regional- and global-scale studies of spatial and temporal changes in WUE have used GPP and ET products from the global MODIS [24; 25; 26; 27; 28; 29]. As a result, the spatial and temporal patterns of WUE in dryland ecosystems can be updated and captured in a timely manner by means of remote sensing. Even though data from remote sensing can effectively fill data gaps, it is inefficient to process large amounts of data. More recently, with the spread of GEE, free access to large amounts of satellite data combined with cloud computing has improved the efficiency of data processing, making it easier to study WUE changes at large regional to global scales [30]. Mutual responses between the atmospheric environment, hydrological elements, and biological factors can affect the carbon sink as well as the water depletion of ecosystems [31; 32]. Previous studies have shown that atmospheric, hydrological, and biological factors can all affect WUE indirectly by influencing vegetation photosynthesis, and directly by influencing vegetation transpiration and soil evaporation; the combined effect of vegetation distribution status and topography leads to significant spatial heterogeneity in the influence of atmospheric and hydrological factors on WUE [6; 33]. For example, high temperatures (TEM) and water-pressure deficits (VPD) in dryland ecosystems can lead to the closure of some stomata of vegetation and reduce water consumption by reducing vegetation transpiration, thus leading to an increase in WUE [34; 35]. Precipitation (PRE) can cause a decrease in WUE in dry areas with sparse vegetation due to strong surface evapotranspiration [24]. In ecosystems under drought stress, vegetation survives by reducing its own leaf area or by promoting root growth to absorb deeper groundwater [36]. Therefore, an increase in the leaf area index (LAI) may lead to stronger evapotranspiration than transpiration, resulting in a decrease in WUE [37]. In addition, [PERSON] et al. found that aerosols affected the photosynthesis and transpiration of vegetation due to aerosol-influenced diffuse radiation affecting photosynthetic effective radiation, LAI, and soil evaporation, causing the WUE of vegetation to increase in most areas but decrease in some arid areas [38]. The above phenomena indicate that the carbon-water cycle of dryland ecosystems depends on atmospheric, hydrological, and biological factors, and the WUE of dryland ecosystems is vulnerable to climate change and environmental changes [39]. In addition, the special characteristics of the dryland environment (e.g., fragile ecological environment, abundant dust aerosol disturbance, relatively few available water resources, deep groundwater distribution, and subjection to extreme drought stress [40]) may lead to an amplification of the effect of atmospheric and hydrological factors as well as biological factors on the WUE of dryland ecosystems. Therefore, it is necessary to explore the drivers of WUE in dryland ecosystems and promote ecological improvement based on maintaining water availability in drylands. Our research has important implications for the management of dryland ecosystems and for understanding WUE and climate responses.