Licheng Liu

Google Scholar: https://scholar.google.com/citations?user=EJXyL_0AAAAJ

Program Affiliations

Biological Systems Engineering

Education and Certificates

• Ph.D. 2020, Earth Science, Purdue University
• B.Sc. 2014, Atmospheric Science, Peking University

Fields of Interest

•  Knowledge-Guided Machine Learning
• Agricultural and natural ecosystem modeling
• Carbon, nutrient, and water cycles
• Process-based modeling
• AI-Ready benchmarking system
• Trustworthy AI modeling and interfaces

Publications

Please check my Google Scholar for all publications: https://scholar.google.com/citations?user=EJXyL_0AAAAJ

• Jin, Z., Liu, L., Yang, Q., Jia, X., Tao, S., Guo, Y., Ghosh, R., Wang, S., Zhu, Q., Jung, M., Guan, K., Kumar, V., Reichstein, M., Fang, J., & Luo, Y. (2026). Knowledge-guided machine learning for global change ecology research. Global Change Biology, 32(2), e70742. https://doi.org/10.1111/gcb.70742
• Yang, J., Liu, L., Yang, Q., Jia, X., Peng, B., Guan, K., & Jin, Z. (2026). Knowledge-guided graph machine learning improves corn yield mapping in the US Midwest. Remote Sensing of Environment, 335, 115287. https://doi.org/10.1016/j.rse.2026.115287
• Yang, J., Peng, B., Wang, Y., Ma, Z., Zhao, Q., Liu, L., Jia, X., Kumar, V., Pan, M., Jia, M., Li, X., Nieber, J., Jin, Z., & Guan, K. (2026). Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics. Water Research, 125613. https://doi.org/10.1016/j.watres.2026.125613
• Yuan, Y., Zhuang, Q., Zhao, B., & Liu, L. (2025). Improving the quantification of global free-living and symbiotic nitrogen fixation in natural terrestrial ecosystems: present-day estimates and 21st century projections. Environmental Research Letters, 20 124005. https://doi.org/10.1088/1748-9326/ae198c
• Liu, L., Zhou, W., Guan, K., Peng, B., Xu, S., Tang, J., … & Jin, Z. (2024). Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems. Nature Communications, 15, 357. https://doi.org/10.1038/s41467-023-43860-5
• Chen, S., Liu, L., Ma, Y., Zhuang, Q., & Shurpali, N. (2024). Quantifying global wetland methane emissions with in situ methane flux data and machine learning approaches. Earth’s Future, 12(11), e2023EF004330. https://doi.org/10.1029/2023EF004330
• Yang, Q., Liu, L., Zhou, J., Rogers, M., & Jin, Z. (2024). Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision. Computers and Electronics in Agriculture, 220, 108911. https://doi.org/10.1016/j.compag.2024.108911
• Yang, Q., Liu, L., Zhou, J., Ghosh, R., Peng, B., Guan, K., Tang, J., Zhou, W., Kumar, V., & Jin, Z. (2023). A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest. Remote Sensing of Environment, 299, 113880. https://doi.org/10.1016/j.rse.2023.113880
• Zhou, J., Yang, Q., Liu, L., Kang, Y., Jia, X., Chen, M., Ghosh, R., Xu, S., Jiang, C., Guan, K., Kumar, V., & Jin, Z. (2023). A deep transfer learning framework for mapping high spatiotemporal resolution LAI. ISPRS Journal of Photogrammetry and Remote Sensing, 206, 30–48.https://doi.org/10.1016/j.isprsjprs.2023.10.017
• Guan, K., Jin, Z., Peng, B., Tang, J., …, Liu, L., … & Yang, S. (2023). A scalable framework for quantifying field-level agricultural carbon outcomes. Earth-Science Reviews, 243, 104462. https://doi.org/10.1016/j.earscirev.2023.104462
• Liu, L., Xu, S., Tang, J., Guan, K., … & Jin, Z. (2022). KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments. Geoscientific Model Development, 15(7), 2839–2858. https://doi.org/10.5194/gmd-15-2839-2022
• Yang, Y., Liu, L., Zhou, W., Guan, K., Tang, J., … & Jin, Z. (2022). Distinct driving mechanisms of non-growing season N2O emissions call for spatial-specific mitigation strategies in the US Midwest. Agricultural and Forest Meteorology, 324, 109108. https://doi.org/10.1016/j.agrformet.2022.109108
• Liu, L., Zhuang, Q., Oh, Y., Shurpali, N. J., Kim, S., & Poulter, B. (2020). Uncertainty Quantification of global net methane emissions from terrestrial ecosystems using a mechanistically based biogeochemistry model. Journal of Geophysical Research Biogeosciences, 125(6), e2019JG005428. https://doi.org/10.1029/2019JG005428
• Oh, Y., Zhuang, Q., Liu, L., Welp, L. R., …, & Elberling, B. (2020). Reduced net methane emissions due to microbial methane oxidation in a warmer Arctic. Nature Climate Change, 10(4), 317–321. https://doi.org/10.1038/s41558-020-0734-z
• Saunois, M., Stavert, AR., Poulter, B., …,Liu, L., … & Zhuang, Q. (2020). The Global Methane Budget 2000–2017. Earth System Science Data, 12(3), 1561–1623. https://doi.org/10.5194/essd-12-1561-2020
• Liu, L., Zhuang, Q., Zhu, Q., Liu, S., Van Asperen, H., & Pihlatie, M. (2018). Global soil consumption of atmospheric carbon monoxide: an analysis using a process-based biogeochemistry model. Atmospheric Chemistry and Physics, 18(11), 7913–7931. https://doi.org/10.5194/acp-18-7913-2018
• Zhang, L., Liu, L., Zhao, Y., Gong, S., Zhang, X., Henze, D. K., … & Wang, Y. (2015). Source attribution of particulate matter pollution over North China with the adjoint method. Environmental Research Letters, 10(8), 084011. http://dx.doi.org/10.1088/1748-9326/10/8/084011

Expertise Summary

My research centers on enhancing our understanding of carbon, nutrient, and water cycles in agricultural and natural ecosystems, to explain their roles in dynamic climate and provide actionable insights for sustainable solutions, by integrating advanced analytical tools such as process-based models, knowledge-guided machine learning (KGML), multi-source data from modern sensing techniques, and AI-accelerated optimization algorithms in decision making.