# Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China
## 2 Materials and Methods
### Data  
Monthly latent heat flux (_LE_), sensible heat flux (_H_) and ground heat flux (G), _ET_, surface net radiation (_R_n), and _SM_ (four layers: 0-10, 10-40, 40-100, 100-200 cm) datasets were obtained from the NASA Global Land Data Assimilation System (GLDAS) Noah model (GLDAS_NOAH025\(M\)2.0). We used the dataset from 1985 to 2014 with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) (downloaded from [[https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)]([https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)), accessed on 10 October 2020). In this study, _SM_ in the 0-100 cm soil layer was regarded as the _SM_ of root zone based on previous research [35; 36]. The monthly _NPP_ dataset (spatial resolution of 1 km \(\times\) 1 km) of China's terrestrial ecosystems north of 18\({}^{\circ}\)N from 1985 to 2014 was obtained from the Global Change Research Data Publishing & Repository of China. The _NPP_ data were calculated using the Carnegie-Ames-Stanford Approach (CASA) model based on the monthly meteorological data of China's land from 1985 to 2015, national soil texture data, and land cover and vegetation index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) remote sensing images. CASA model is a process-based remote sensing model for estimating _NPP_ of terrestrial ecosystem, which is driven by grid datasets of climate, radiation, soil, and remote sensing vegetation index [37]. A comparative analysis with the measured and simulated data from previous studies [38; 39; 40; 41; 42] showed that the _NPP_ dataset used in this study hadgood accuracy [43]. Figure 1: Sketch map of climatic zones (**left**)and the length of growing seasons (**right**) in the terrestrial ecosystemsof China. To verify the _NPP_ and _ET_ (GLDAS) data, this study collected the monthly observation data from eight flux towers in China from 2003 to 2010, which were provided by ChinaFLUX ([[http://www.chern.org.cn/](http://www.chern.org.cn/)]([http://www.chern.org.cn/](http://www.chern.org.cn/)), accessed on 15 September 2021). In addition, this study also collected the monthly observation flux data from the other four flux towers from the stations, and the collected data length of these four stations was 2004-2006, 2011-2014, 2008-2011, and 2013-2014, respectively. This study also used _SM_ data based on microwave remote sensing in China from 2002 to 2011 (provided by the National Tibetan Plateau Data Center (TPDC) of China; [[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)), accessed on 2 August 2021) to verify the _SM_ data from GLDAS. The spatial and temporal resolutions of the _SM_ dataset (from the TPDC) were 0.25\({}^{\circ}\) and monthly, respectively. The dataset was calculated based on the high spatial and temporal resolution surface meteorological dataset and an improved land surface assimilation system to drive the Simple Biosphere model (SiB2) and assimilate the brightness temperature observed by the Advanced Microwave Scanning Radiometer Earth Observing System sensor (AMSR-E) satellite [44; 45]. The verifications showed that the RMSE of this SM dataset (from the TPDC) was approximately 5% volumetric soil water content [45; 46]. The soil texture dataset was obtained from the TPDC of China ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)), accessed on 13 May 2020). The dataset was calculated based on the 1:1,000,000 scale soil map and 8595 soil profiles of China's second national soil survey and the regional land and climate simulation standard of the U.S. Department of Agriculture (soil texture classification standard is shown in Table 1).