# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 2 Study Area and Data
### Impact Factor Data  
Selecting the appropriate impact factors is the key to mastering the driving and response laws between GPP and factors, and it is also the basis for accurately predicting GPP. We refer to the excellent experience of existing research [35; 41; 42], and mainly select three categories of factors for GPP prediction, including vegetation physiology factors, land  
Figure 1: Location and topography of the study area. surface feature factors, and climate environment factors. Their information is shown in Table 1.  
For vegetation physiology, we chose the enhanced vegetation index (EVI), leaf area index (LAI), and normalized difference vegetation index (NDVI). Of these, LAI is derived from the MOD15A2H dataset ([[https://lpdaac.usgs.gov/products/mod15a2](https://lpdaac.usgs.gov/products/mod15a2) hv006/]([https://lpdaac.usgs.gov/products/mod15a2](https://lpdaac.usgs.gov/products/mod15a2) hv006/), accessed on 15 April 2023), with a spatial resolution of 500 m and a temporal resolution of 8 d. EVI and NDVI are calculated from MODIS images ([[https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)]([https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)), accessed on 15 April 2023) using the following formulas. \[EVI=\frac{2.5\times(NIR-Red)}{NIR+6\times Red-7.5\times Blue+1} \tag{1}\]  
\[NDVI=\frac{NIR-Red}{NIR+Red} \tag{2}\]  
Among them, _NIR_, _Red_, and _Blue_ are the near-infrared, red, and blue bands of the MODIS image, respectively. For land surface feature, we selected the digital elevation model (DEM), land use/land cover (LULC), land surface water index (LSWI), land surface temperature (LST), and the fraction of photosynthetically active radiation (FPAR). Of these, FPAR comes from the MOD15A2H dataset and LST comes from the MOD11A2 dataset ([[https://lpdaac.usgs.gov/products/mod11a2v006/](https://lpdaac.usgs.gov/products/mod11a2v006/)]([https://lpdaac.usgs.gov/products/mod11a2v006/](https://lpdaac.usgs.gov/products/mod11a2v006/)), accessed on 15 April 2023). LULC data comes from the MCD12Q1 dataset ([[https://lpdaac.usgs.gov/products/mod12q1v006/](https://lpdaac.usgs.gov/products/mod12q1v006/)]([https://lpdaac.usgs.gov/products/mod12q1v006/](https://lpdaac.usgs.gov/products/mod12q1v006/)), accessed on 15 April 2023), with a spatial resolution of 1 km and a temporal resolution of 1 a; in this study, we used its IGBP classification system, which included 11 natural vegetation types, 3 land use types, and 3 non-grassy land types. The DEM comes from the SRTM V4 dataset ([[https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/](https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/)]([https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/](https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/)), accessed on 15 April 2023), with a spatial resolution of 90 m. LSWI is calculated from MODIS images using Formula (3). \[LSWI=\frac{NIR-SWIR}{NIR+SWIR} \tag{3}\]  
Among them, _NIR_ and _SWIR_ are the near-infrared and shortwave infrared bands of the MODIS image, respectively. For the climatic environment, we selected precipitation (P), minimum air temperature (Tmin), maximum air temperature (Tmax), evapotranspiration (ET), and potential evapotranspiration (PET). Of these, ET and PET are from the MOD16A2 dataset ([[https://lpdaac.usgs.gov/products/mod16a2v006/](https://lpdaac.usgs.gov/products/mod16a2v006/)]([https://lpdaac.usgs.gov/products/mod16a2v006/](https://lpdaac.usgs.gov/products/mod16a2v006/)), accessed on 15 April 2023). P, Tmin, and Tmax are from the fifth generation ECMWF atmospheric reanalysis of the global