This study employs two probabilistic methods – the Bayesian model and a deep-learning-based neural network – to estimate net primary production (NPP) and quantify its uncertainties. Results indicate that both models effectively capture NPP dynamics, with the neural network model outperforming the Bayesian approach in predictive accuracy. Furthermore, these models successfully predict interannual trends in NPP variation across the study area.