Biogeosciences
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Articles | Volume 22, issue 19
Articles | Volume 22, issue 19
https://doi.org/10.5194/bg-22-5463-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-5463-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 19
Research article
 | 
09 Oct 2025
Research article |  | 09 Oct 2025

Refining marine net primary production estimates: advanced uncertainty quantification through probability prediction models

Jie Niu, Mengyu Xie, Yanqun Lu, Liwei Sun, Na Liu, Han Qiu, Dongdong Liu, Chuanhao Wu, and Pan Wu

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Short summary
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.
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