## 1 Introduction  
Soil moisture (SM) is usually expressed as a percentage of soil water content in dry soil weight. As one of the main driving forces of the water, energy, and carbon cycles in land surface and atmosphere [1], soil moisture plays an important role in promoting photosynthesis and ecosystem dynamics [2]. Water use efficiency (WUE) is defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET) of plants for the same period [3]. WUE is an indicator of the adjustment of vegetation photosynthesis to water loss [4], and quantitatively characterizing WUE can help us understand the interaction between the carbon and the water cycles of terrestrial ecosystems [5]. Soil moisture is affected by evapotranspiration, runoff, groundwater, etc. [6]. In arid grassland and shrubland, strong linear relationships were found between evapotranspiration and soil moisture [7]. During the late growing season, GPP is mainly affected by the soil moisture [8]. GPP is highly sensitive to the variation of surface soil moisture, especially in dry areas [9]. Soil moisture could be a good proxy of the rate of evapotranspiration at a large regional scale; therefore, it could be used to analyze the WUE of an ecosystem. Soil moisture use efficiency (SWUE), defined as the ratio between GPP and SM, was proposed as a new measure of WUE [9]. SWUE may depict the rate of carbon assimilation rate per unit of soil water consumption and reflect the interaction between soil water and ecosystem productivity [6]. A deeper understanding of how SWUE varies and its response to environmental and biotic factors is critical to accurately forecast terrestrial carbon and water cycles and feedback to climate change. Traditionally, soil moisture data can be obtained via in situ measurement, which hardly meets the demands for continuous, large-scale monitoring. Due to the lack of understanding of the feedback mechanism of the atmosphere and insufficient explanation of the model errors, the retrieval of soil moisture data based on modeling may have a great impact on related research [10]. Satellite remote sensing in the microwave range constitutes a powerful source of information for monitoring large-scale variability of biomass, soil and vegetation moisture, and soil water use efficiency. [PERSON] et al. used the vegetation optical depth (VOD) derived from the Soil Moisture and Ocean Salinity (SMOS) mission to quantify inter-annual changes of aboveground carbon stocks in sub-Saharan Africa [11]. [PERSON] et al. found good consistency between live fuel moisture content (LFMC) and the root-zone soil moisture (ECV_RZSM) derived from ECV_SM (the Essential Climate Variable near-surface soil moisture), the microwave polarization difference index (MPDI), and VOD in the south-eastern region of France [12]. The climate change initiative (CCI) project released by the European Space Agency (ESA) provides global daily soil moisture (SM) data to capture changes in precipitation and soil moisture models [13]. [PERSON] et al. evaluated the ECV_SM products from 1988 to 2010 and found that the trends of ECV_SM products were consistent with other products [13]. The merged and individual soil moisture products (ECV_SM) were assessed using ground-based soil moisture data from 596 global stations during 1979-2010. It was found that ECV_SM merged products performed better than individual products except for ASCAT (Advanced Scatterometer) products and the quality improved over time [14]. [PERSON] et al. used the CCI soil moisture and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP to evaluate the variations in SWUE among biomes, climate conditions, and latitudes from 2000 to 2014 at a global scale [9]. Most existing studies on soil water use efficiency focused on crops at the field scale [15; 16]. To our knowledge, the analysis of the relationships of large-scale SWUE and geographical, environmental factors, including elevation, soil types, vegetation types, and land surface temperature, is still unavailable.