Physiological process-based feature engineering enables robust estimation of crop gross primary production under data-limited conditions

• Integrating physiological knowledge with machine learning improves GPP estimation. • Process models convert environmental data to meaningful inputs for neural networks. • With abundant training data, the model achieved higher accuracy than baseline models. • Physiological features prevented overfitting under limited training data conditions. • Our hybrid approach shows potential for adaptation to unknown climate conditions. Estimating gross primary production (GPP) in croplands is essential fo