Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

Abstract

Upscaling in situ carbon flux measurements using remotely sensed and meteorological observations in a machine learning algorithm leads to improved estimates of average uptake, and interannual variability in global drylands.