# Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin
## 2 Materials and Methods
### Data  
#### 2.2.1 Past Scenario  
In this study, the annual vegetation cover map of the classification scheme based on the International Geosphere-Biosphere Programme (IGBP) was derived from the land cover product (MCD12Q1) of the moderate resolution imaging spectroradiometer (MODIS). The IGBP scheme includes five types of forestlands: evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needleleaf forest (DNF), DBF, and mixed forest (MF); seven types of grassland: closed shrublands (CSH), open shrublands (OSH), woody savannas (WSA), savannas (SAV), grasslands (GRA), barren (BRN), and permanent wetlands (PMW); and two types of croplands: croplands (CRO) and cropland/natural vegetation mosaic (CRV). Then, we combined this IGBP product into three major vegetation sets (Table 1): mixed forestland (MF: ENF, EBF, DNF, DBF, and MF), mixed grassland (MG: CSH, OSH, WSA, SAV, GRA, BRN, PMW), and mixed cropland (MC: CRO, CRV), for statistical analysis, according to [PERSON] et al. (2018). Figure 1: Location of the study area, along with 334 county boundaries. The MODIS-NDVI product (MOD13A1) at 16-day intervals, which was taken as a proxy for vegetation greening, was extracted from the Google Earth Engine (GEE) platform ([[https://code.earthegine.google.com/](https://code.earthegine.google.com/)]([https://code.earthegine.google.com/](https://code.earthegine.google.com/)), accessed on 04 December 2021). Then, the 16-day NDVI values smaller than zero were excluded, and the annual average NDVI value was aggregated from the corresponding year's data. The meteorological forcing data needed were obtained from the China meteorological forcing dataset (CMFD) at the National Tibetan Plateau/Third Pole Environment Data Center (TPDC) [35]. This data included 2-m air temperature (Tem), downward shortwave radiation (Rad), and precipitation rate (Pre) with a spatial resolution of 0.1\({}^{\circ}\) ([[http://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/](http://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/)]([http://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/](http://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/)), accessed on 04 December 2021). The CMFD dataset was developed by integrating data from multiple sources, such as remote sensing, reanalysis datasets, and in situ observations at weather stations. This is the first high-resolution meteorological forcing data created in China [36; 37] and is widely used in studies on land surface processes [38], evaporation [39; 40], machine learning [41], vegetation, and phenology [42]. Afforestation statistical data were collected from the China Forestry Statistical Yearbooks/China Forestry and Grassland Statistical Yearbook ([[https://data.cnki.net/Yearbook/Single/N2021060073](https://data.cnki.net/Yearbook/Single/N2021060073)]([https://data.cnki.net/Yearbook/Single/N2021060073](https://data.cnki.net/Yearbook/Single/N2021060073)), accessed on 04 December 2021). The yearbook provided the annual area of afforestation (AFF) in each county. Human-induced vegetation restoration projects shaped by afforestation are the main positive anthropogenic effects that have accelerated the rate of vegetation greenness in many areas. Previous studies have proved that revegetation activities enhanced vegetation coverage and had profound hydrological implications [3; 5; 9]. According to MCD12Q1 (IGBP), not all counties have forests. Therefore, the counties with MFs in 2018 were judged to have engaged in afforestation strategies. Subsequently, we collected the data on the annual afforestation areas in these counties. Given that most afroested species were perennial trees, the accumulative area of afforestation (AAF) was treated as an indicator to explore the impact of vegetation management/human land-use management policies (implemented by the government) on the increasing NDVI of the study area. NTL is an effective variable for detecting low-level anthropogenic activities [43].