4.Spatial distribution of the dominant drivers on vegetation growth across China over the past three decades|植被生长数据集|空间分析数据集

4. Spatial distribution of the dominant drivers on vegetation growth across China over the past three decades<i>a. Spatial distributi</i><i>on of </i><i>the partial c</i><i>orrelation coefficient (Spearman)</i><i> between the </i><i>drivers </i><i>and </i><i>the Normalized Difference Vegetation Index (NDVI)/Leaf Area Index (LAI)/Gross Primary Productivity (GPP) across China over the 1982–2015 period</i>The drivers include air temperature (Tmp), precipitation (Pre), vapor pressure deficit (VPD), downward surface shortwave radiation (Rad), soil moisture (SM), and atmospheric carbon dioxide (CO₂). We used the 25 10-year moving windows over the 1982–2015 period to reduce the uncertainty associated with inter-annual anomalies. We calculated the partial correlation coefficient (Spearman) for each grid cell for the 25 10-year moving windows. The factor with the highest absolute value of the significant partial correlation coefficient (two-tailed t-test: p &lt; 0.05) was identified as the dominant driver of changes in vegetation growth in that grid cell. You can use the "<b>China_vegetation_cover_map.tif</b>" in the dataset "1.Spatiotemporal patterns of vegetation growth across China over the past three decades" to extract the part of China.<i>b. The MATLAB (R2023a) code for calculating the partial correlation coefficient (Spearman) for each grid cell for the 25 10-year moving windows</i>This includes "<b>Partial_Correlation_Spearman.m</b>" for calculating the partial correlation coefficient (Spearman) for each grid cell for the 25 10-year moving windows and "<b>Random forest.m</b>" for building RF models to gauge the importance of climate and atmospheric CO₂ in driving changes in NDVI/LAI/GPP. We trained RF models pixel by pixel and then extracted the dominant driver for each grid cell by ranking the feature importance.