Ecosystem carbon use efficiency at global scale from upscaling eddy-covariance data with machine learning and MODIS products.

Carbon use efficiency (CUE) is a key indicator in coupled biological-abiotic systems that accounts for their capacity of effectively retain carbon, offering insights of ecosystem functioning and the dynamics of carbon cycle. Generally, CUE assessments have been limited to an autotrophic perspective, quantifying plant efficiency while neglecting carbon losses from heterotrophic respiration. This provides an incomplete view of ecosystem carbon retention. To address this critical gap, we offer a global quantification of a more holistic ecosystem-level CUE, that incorporates all respiratory fluxes. This paper proposes a methodology for mapping ecosystem CUE at global scale from in situ data, remote sensing observations and machine learning. This data-driven approach exploits a Gaussian Processes Regression (GPR) model trained with CUE from eddy-covariance towers and concomitant observations from the Moderate resolution Imaging Spectroradiometer (MODIS). The performance of the model shows high correspondence (R