Accurate, Efficient, and Scalable Quantification of Field-level Terrestrial Carbon Budget in the United States using Knowledge-Guided Machine Learning
Accurate, Efficient, and Scalable Quantification of Field-level Terrestrial Carbon Budget in the United States using Knowledge-Guided Machine Learning
Licheng LIU, University of Minnesota-Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, United States, Wang Zhou, University of Illinois at Urbana Champaign, Department of Natural Resources and Environmental Sciences, Urbana, IL, United States, Shaoming Xu, University of Minnesota-Twin Cities, Department of Computer Science/Engineering, Minneapolis, United States, Jinyun Tang, Lawrence Berkeley Natl Lab, Climate and Ecosystem Sciences Division, Berkeley, United States, Xiaowei Jia, University of Pittsburgh, Department of Computer Science, Pittsburgh, PA, United States, Chongya Jiang, University of Illinois at Urbana Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, IL, United States, Kaiyu Guan, University of Illinois at Urbana Champaign, Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, Urbana, United States, Bin Peng, University of Illinois at Urbana Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, Qi Yang, University of Minnesota, St Paul, United States, Yufeng Yang, University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, Minneapolis, MN, United States, Vipin Kumar, University of Minnesota Twin Cities, Department of Computer Science/Engineering, Minneapolis, MN, United States and Zhenong Jin, University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, United States