Datasets for "The direct and indirect effects of the environmental factors on global terrestrial gross primary productivity over the past four decades"

The environmental changes can affect gross primary productivity (GPP) by altering not only the biogeochemical characteristics of the photosynthesis system (direct effects) but also the structure of the vegetation canopy (indirect effects). However, comprehensively quantifying the multi-pathway effects of environmental change on GPP is currently challenging. We proposed a framework to analyse the changes in global GPP by combining a nested machine-learning model and a theoretical photosynthesis model. We quantified direct and indirect effects of changes in key environmental factors (atmospheric CO2 concentration, temperature, solar radiation, vapor pressure deficit (VPD), and soil moisture) on global GPP from 1982 to 2020.  The three datasets(RF_LAI, RF_GPP, and RF_GPPlai) are derived from LAI random forest model, GPP random forest model and hierarchical nested model respectively.