A novel memory-based deep learning framework for reliable joint estimation of daily vegetation carbon fluxes

• A novel memory-based deep learning framework was proposed for joint estimation of daily GPP, RECO, and NEE. • The proposed model achieved robust daily carbon flux estimations with enhanced reliability. • The linear probes revealed the increased reliability of the proposed model through better correlation in predicting non-target variables, i.e. vegetation transpiration and canopy conductance. • The importance of antecedent climate drivers on daily GPP was evaluated. Reliable estimation of ecos