Package of 39 covariates, a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m for the period of 2015 to 2020 from google earth engine (GEE) to assemble a nationwide geospatial dataset of Mexico. Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall (Hijmans, 2005); time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset (Hansen et al., 2013); 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution (Myneni, Ranga et al., 2015) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) (Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014). All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE.