2.3.3. Climate Datasets

The meteorological data were selected from the monthly cumulative precipitation, monthly mean relative humidity, and monthly mean air temperature from April to October during 2001 to 2018 for 51 nationally standard meteorological stations in and near the TRHR (Figure 2c), which were provided by the Chinese Meteorological Administration (http://data.cma.cn/ (accessed on 5 July 2020)). Some observational data were missing and had non-uniformity characteristics owing to the influence of changes in meteorological stations and in instruments used to observe. Thus, the regression equation of time series and the homogeneity test of variance were used to fill in the missing values and test for data homogeneity at first in this paper. The commonly used spatial interpolation methods include inverse distance weighted, co-kriging, and thin plate splines (TPS). After comparative experiments, the monthly accumulated precipitation and monthly mean relative humidity were interpolated by the co-kriging method in ArcGIS10.5 software (ESRI, Redlands, CA, USA), with 500 × 500 m resolution. Furthermore, the TPS method of

Anusplin software (Centre for Resource and Environmental Studies, Australian National University, Canberra, Australia) was adopted to interpolate the monthly mean temperature at a resolution of 500 × 500 m.

In this study, time-series shortwave radiation data were acquired from the European Centre for Medium-Range Weather Forecasts website (https://cds.climate.copernicus.eu/ cdsa-pp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview (accessed on 5 July 2020)), with a temporal resolution of one month and a spatial resolution of 0.1◦ × 0.1◦.

#### 2.3.4. Soil Characteristics Database

In order to explore the influence of soil physical and chemical attributes on plant phenology, we used a database of soil characteristics that was produced by the Land– Atmosphere Interaction Research Group at Sun Yat-sen University (http://globalchange. bnu.edu.cn/home (accessed on 25 October 2020)). The database included information on total N (g/100 g), total P (g/100 g), total K (g/100 k), soil organic matter (g/100 g), alkali-hydrolysable N (mg/kg), available P (mg/kg), available K (mg/kg), cation exchange capacity (me/100 g), porosity (cm3/100 cm3), bulk density (g/cm3), and pH (H2O). Furthermore, soil moisture and soil temperature data were obtained from Google Earth Engine (https://developers.google.com/earth-engine/datasets/catalog/NASA\_FLDAS\_ NOAH01\_C\_GL\_M\_V001#bands (accessed on 25 October 2020)) with a spatial resolution of 0.1◦ × 0.1◦ and temporal resolution of one month.

#### 2.3.5. Digital Elevation Model

Digital Elevation Model (DEM) data were collected from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) Version 3, which was provided by the US National Aeronautics and Space Administration's Earth Data website (https://earthdata.nasa.gov/ (accessed on 30 October 2020)), with a spatial resolution of 30 m. For this study, the DEM data were processed with ArcGIS 10.5 to obtain the slope, elevation, and aspect.
