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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
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
Furuta, D. C., Yang, J., Liu, L., Jin, Z., Guan, K., Peng, B., & Li, J. (2026). Design and Test of a Lower-Cost Water-Quality Sensor for Nitrate. ACS ES&T Water. https://doi.org/10.1021/acsestwater.5c00837
2025
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
2024
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
Chen, Z., Li, J., Huang, K., Wen, M., Zhuang, Q., Liu, L., Zhu, P., Jin, Z., Xing, S., & Zhang, L. (2024). Assessment of soil total phosphorus storage in a complex topography along China’s southeast coast based on multiple mapping scales. Pedosphere, 34(1), 236–251. https://doi.org/10.1016/j.pedsph.2023.09.012
He, E., Xie, Y., Liu, L., Jin, Z., Zhang, D., & Jia, X. (2024). Knowledge Guided Machine Learning for Extracting, Preserving, and Adapting Physics-aware Features. In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), 715-723. https://doi.org/10.1137/1.9781611978032.82
2023
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
Zhang, Z., Bansal, S., Chang, K., …, Liu, L., … & Poulter, B. (2023). Characterizing performance of freshwater wetland methane models across time scales at FLUXNET‐CH4 sites using Wavelet analyses. Journal of Geophysical Research Biogeosciences, 128(11), e2022JG007259. https://doi.org/10.1029/2022JG007259
He, E., Xie, Y., Liu, L., Chen, W., Jin, Z., & Jia, X. (2023). Physics guided neural networks for time-aware fairness: an application in crop yield prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14223-14231. https://doi.org/10.1609/aaai.v37i12.26664
Liu, Z., Liu, L., Xie, Y., Jin, Z., & Jia, X. (2023). Task-adaptive meta-learning framework for advancing spatial generalizability. In Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14365-14373. https://doi.org/10.1609/aaai.v37i12.26680
Xu, S., Khandelwal, A., Li, X., Jia, X., Liu, L., Willard, J., … & Kumar, V. (2023). Mini-Batch Learning Strategies for modeling long term temporal dependencies: a study in environmental applications. In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 649-657. https://doi.org/10.1137/1.9781611977653.ch73
2022
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
Oh, Y., Zhuang, Q., Welp, L. R., Liu, L., Lan, X., Basu, S., … & Chanton, J. P. (2022). Improved global wetland carbon isotopic signatures support post-2006 microbial methane emission increase. Communications Earth & Environment, 3, 159. https://doi.org/10.1038/s43247-022-00488-5
Ma, D., Zhang, H., Song, X., Xing, S., Fan, M., Heiling, M., Liu, L., Zhang, L., & Mao, Y. (2022). Estimating soil organic carbon and nitrogen stock based on high-resolution soil databases in a subtropical agricultural area of China. Soil and Tillage Research, 219, 105321. https://doi.org/10.1016/j.still.2022.105321
Yuan, K., Zhu, Q., Li, F., Riley, W. J., …, Liu, L., … & Jackson, R. (2022). Causality guided machine learning model on wetland CH4 emissions across global wetlands. Agricultural and Forest Meteorology, 324, 109115. https://doi.org/10.1016/j.agrformet.2022.109115
2021
Kim, T., Jin, Z., Smith, T. M., Liu, L., Yang, Y., … & Zhou, W. (2021). Quantifying nitrogen loss hotspots and mitigation potential for individual fields in the US Corn Belt with a metamodeling approach. Environmental Research Letters, 16(7), 075008. https://doi.org/10.1088/1748-9326/ac0d21
Lan, X., Basu, S., Schwietzke, S., Bruhwiler, L. M. P., …, Liu, L., … & Crippa, M. (2021). Improved Constraints on Global Methane Emissions and Sinks Using δ13C‐CH4. Global Biogeochemical Cycles, 35(6), e2021GB007000. https://doi.org/10.1029/2021GB007000
Yun, H., Tang, J., D’Imperio, L., Wang, X., Qu, Y., Liu, L., Zhuang, Q., Zhang, W., Wu, Q., Chen, A., Zhu, Q., Chen, D., & Elberling, B. (2021). Warming and increased respiration have transformed an Alpine Steppe ecosystem on the Tibetan Plateau from a carbon dioxide sink into a source. Journal of Geophysical Research Biogeosciences, 127(1), e2021JG006406. https://doi.org/10.1029/2021JG006406
2020 and prior
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
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Li, D., Li, C., Yao, Y., Li, M., & Liu, L. (2020). Modern imaging techniques in plant nutrition analysis: A review. Computers and Electronics in Agriculture, 174, 105459. https://doi.org/10.1016/j.compag.2020.105459
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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
Yun, H., Wu, Q., Zhuang, Q., Chen, A., Yu, T., Lyu, Z., … & Liu, L. (2018). Consumption of atmospheric methane by the Qinghai–Tibet Plateau alpine steppe ecosystem. The Cryosphere, 12(9), 2803-2819. https://doi.org/10.5194/tc-12-2803-2018
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