Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Licheng Liu, Wang Zhou, Kaiyu Guan (), Bin Peng, Shaoming Xu, Jinyun Tang, Qing Zhu, Jessica Till, Xiaowei Jia, Chongya Jiang, Sheng Wang, Ziqi Qin, Hui Kong, Robert Grant, Symon Mezbahuddin, Vipin Kumar and Zhenong Jin ()
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Licheng Liu: University of Minnesota
Wang Zhou: University of Illinois at Urbana-Champaign
Kaiyu Guan: University of Illinois at Urbana-Champaign
Bin Peng: University of Illinois at Urbana-Champaign
Shaoming Xu: University of Minnesota
Jinyun Tang: Lawrence Berkeley National Laboratory
Qing Zhu: Lawrence Berkeley National Laboratory
Jessica Till: University of Minnesota
Xiaowei Jia: University of Pittsburgh
Chongya Jiang: University of Illinois at Urbana-Champaign
Sheng Wang: University of Illinois at Urbana-Champaign
Ziqi Qin: University of Illinois at Urbana-Champaign
Hui Kong: University of Minnesota
Robert Grant: University of Alberta
Symon Mezbahuddin: University of Alberta
Vipin Kumar: University of Minnesota
Zhenong Jin: University of Minnesota

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

Date: 2024
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DOI: 10.1038/s41467-023-43860-5

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