Dataset for paper "Explainable Machine Learning Confirms the Global Terrestrial CO2 Fertilization Effect From Space"

The carbon dioxide (CO2 ) fertilization effect has captured worldwide attention, owing to its tremendous potential to challenge existing predictions of future climate. However, quantifying the CO2 fertilization effect (CFE) has proven to be challenging, given that it is closely entangled with other ecological and environmental processes. Recent years have wit- nessed significant advances with breakthroughs using theoretical methods to infer the CFE from eddy covariance tower mea- surements. Building on earlier findings, this study presents an innovative approach that utilizes explainable machine-learning techniques—describing the partial dependence of the response variable to each explanatory variable—to quantify the global CFE from remote-sensing platforms with an averaged R2 of 0.85. This study provides the first data-driven evidence of the global CFE and confirms the potential for extrapolation to the globe.