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 (R² = 0.84) and low error and bias (RMSE = 0.1, ME = 0.01) regarding in situ data. The execution of the GPR model upscaled CUE and associated uncertainty from tower level to global scale and provided multitemporal global CUE estimates from 2001 to 2023. The GPR model reports a mean global CUE of 0.43 ± 0.08 for this period. A preliminary analysis carried out for different climatic zones and biomes illustrates the increase of mean CUE from tropical (0.36 ± 0.08) to cold (0.55 ± 0.08) zones. The lowest mean CUE is found over evergreen broadleaved forests (0.37 ± 0.04), whereas the largest mean CUE is found over open shrublands (0.53 ± 0.12). Finally, a global trend of (-1.2 ± 0.3) × 10^-3 yr^{- 1} is reported for mean global CUE from 2001 to 2023. The results of this work highlight the dependence of CUE on both climate and biome type, as well as the decreasing carbon sequestration power of vegetation at global scale, which is key to better understand the effects of climate change.