articleNature CommunicationsJan 8, 2024GOLD OA

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

LLLicheng LiuWZWang ZhouKGKaiyu GuanBPBin PengSXShaoming Xu

University of Minnesota · University of Illinois Urbana-Champaign · +8 more institutions

PubMed
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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…

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Topics & keywords

Topics
Keywords
  • Computer science
  • Testbed
  • Carbon cycle
  • Biogeochemical cycle
  • Process (computing)
  • Machine learning
  • Remote sensing
  • Ecosystem
UN Sustainable Development Goals
  • Zero hunger
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