3.Spatiotemporal patterns of the relationship between vegetation growth and water availability across China over the past three decades

3. Spatiotemporal patterns of the relationship between vegetation growth and water availability across China over the past three decades<i>a. Spatial distribution of the trends in the Spearman’s rank correlation coefficient (r-value) between the Normalized Difference Vegetation Index (NDVI)/Leaf Area Index (LAI)/Gross Primary Productivity (GPP) and Palmer Drought Severity Index (PDSI) for the 25 10-year moving windows over the 1982–2015 period</i>This includes the trends in Spearman’s rank correlation coefficient (r-value) between NDVI/LAI/GPP and PDSI for each grid cell over all over the globe (e.g., using mean growing-season NDVI value, "<b>NDVI_PDSI_10year_Spearman_R_Trend.tif</b>"). 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 Spearman’s rank correlation coefficient between NDVI/LAI/GPP and PDSI for each grid cell for the 25 10-year moving windows</i>This includes "<b>Correlation_Spearman_10yr_moving_windows.m</b>" for calculating the Spearman’s rank correlation coefficient (r-value) between NDVI/LAI/GPP and PDSI for each grid cell for the 25 10-year moving windows. You can then use "<b>Theil-Sen estimator.m</b>" and "<b>Mann-Kendall test.m</b>" in the dataset "1.Spatiotemporal patterns of vegetation growth across China over the past three decades" to calculate the trends in the Spearman’s rank correlation coefficient between NDVI/LAI/GPP and PDSI (e.g., using mean growing-season NDVI value, "<b>NDVI_PDSI_10year_Spearman_R_Trend.tif</b>").