## 2 Materials and Methods
### Data Acquisition and Processing  
Land cover data were downloaded from the website of the CCI-LC database ([[http://maps.elie.ucl.ac.be/CCI/viewer/download.php](http://maps.elie.ucl.ac.be/CCI/viewer/download.php)]([http://maps.elie.ucl.ac.be/CCI/viewer/download.php](http://maps.elie.ucl.ac.be/CCI/viewer/download.php))) (accessed on 29 January 2021), which is hosted by the European Space Agency. The data period is 2001-2015, and the spatial resolution of the data is 300 m. The land cover classification system (LCCS) has 22 classes for Level 1 and 31 classes for Level 2. The CCI-LC classification system was transferred to the commonly used Intergovernmental Panel on Climate Change land classification to extract data regarding restoration activities, and the results were compared with those from other land use data. CCI-LC was classified into 10 categories (Figure 1B). The overall  
Figure 1: (**A**) Study area and (**B**) land CCI-LC in 2015.  
accuracy of LCCS Level 1 for CCI-LC is 75.38%, as validated by the GlobCover 2009 validation set. The accuracy of forest, agricultural land and grassland is 83%, 87% and 44%, respectively. Land use data were downloaded from the Data Centre of Resources and Environmental Sciences (RESDC, Chinese Academy of Sciences) ([[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/))) (accessed on 29 January 2021) [42; 43] to validate their consistency from 2001 to 2015 and test the application of CCI-LC to the study area. RESDC land use data covered four periods (i.e., 2000, 2005, 2010 and 2015) with a spatial resolution of 1000 m and were interpreted from Landsat Thematic Mapper digital images and China-Brazil Earth Resource Satellite 1 data. The average interpretation accuracy for land use is approximately 92.9% in accordance with field surveys and random sample assessments [43]. Despite its high accuracy, the RESDC land use database cannot be used to extract annual land use changes because it covers only four periods. Changes from 2000 to 2015 for three primary land types, namely, agriculture, forest and grassland, were compared. Major land types in the two-source land data exhibited similar trends, i.e., a continuous decrease in agricultural lands, an increase in forests and a reduction in grasslands (Figure 2). The root-mean-square error (RMSE) of agricultural land reaches the maximum value probably due to the difference in land classification between RESDC and CCI-LC data [44]. The RMSE of forest and grassland is lower than that of agricultural land. Therefore, CCI-LC data were selected to investigate the trend of land use changes in the study area. BESS and MODIS GPPs were used as the GPP source data to avoid the uncertainty of single-GPP source data. MODIS GPP, which is a version of the Numerical Terradynamic Simulation Group MOD17A3 (version 55) with a spatial resolution of 1 km, was downloaded from [[http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/MOD17A3/](http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/MOD17A3/)]([http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/MOD17A3/](http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/MOD17A3/)) (accessed on 29 January 2021). Compared with its previous versions, MOD17A3 GPP (version 55) has been updated mostly due to the consideration of cloud effect and consistent meteorological forcing. It has been widely used in global carbon cycle analysis, ecosystem status assessment and environmental change monitoring. Additional information regard  
Figure 2: Consistency analysis of agricultural land, forest and grassland changes derived from the European Space Agency’s CCI-LC and the RESDC of the Chinese Academy of Sciences in 2000–2015. ing MODIS GPP data can be found in [PERSON] et al. [26; 27]. BESS, a process-based model that generates GPP with high continuous spatiotemporal resolution, is comparable with in situ data and satellite-derived products (e.g., Max Planck Institute for Biogeochemistry) [28; 29].