Prof. Kaiyu Guan

Director of Research (AI for Ag); Levenick Endowed Professor, Department of Food Science and Human Nutrition, Founding Director of Agroecosystem Sustainability Center

University of Illinois Urbana-Champaign

kaiyug@illinois.edu

I am a Levenick Endowed Professor in agroecosystem sensing and modeling at UIUC, a Blue-Waters Professor in Supercomputing at UIUC-NCSA, a University Scholar of the University of Illinois System, the Founding Director of Agroecosystem Sustainability Center (ASC) at UIUC, and also the Chief Scientist of the NASA Acres Consortium – representing NASA’s flagship program in advancing US agriculture research. My major affiliations at UIUC are with the Department of Natural Resources and Environmental Sciences (NRES), Siebel School of Computing and Data Sciences (CS), College of Agricultural, Consumer and Environmental Sciences (ACES), Institute for Sustainability, Energy, and Environment (iSEE), and National Center for Supercomputing Applications (NCSA). My research group uses advanced process models, satellite sensing technology, fieldwork, and artificial intelligence to address how climate and human practices affect crop productivity, water resource, ecosystem functioning, and environmental sustainability. We have keen interests in applying our knowledge and skills in solving real-life problems, such as large-scale crop monitoring and forecasting, quantification of environmental impacts of farming/grazing (e.g. greenhouse gas emission), agricultural policy design, water management and sustainability, and global food security.

Research Description

My research group uses advanced process models, satellite sensing technology, fieldwork, and artificial intelligence to address how climate and human practices affect crop productivity, water resource, ecosystem functioning, and environmental sustainability. We aim to bring domain knowledge (i.e. Earth system science, hydrology, plant physiology, biogeochemistry, ecology, agricultural science), satellite data, supercomputing, and AI/ML together to revolutionize the agricultural research, such that we can observe every crop field in real-time, monitor crop growth condition, water demands, and nutrient needs, forecast crop yield, risks and environmental impacts (e.g. greenhouse gas emission, soil carbon change), and provide farmers our solutions to best manage their fields as well as policy-makers improved agricultural policy design, across the U.S. and Worldwide. We have keen interests in applying our knowledge and skills in solving real-life problems, such as large-scale crop monitoring and forecasting, quantification of environmental impacts of farming/grazing (e.g. greenhouse gas emission), agricultural policy design, water management and sustainability, and global food security. We strive to achieve co-sustainability of environment quality and food security.

RECENT PUBLICATIONS

Derner, J. D., Gonzalo Irisarri, J., Raynor, E. J., Ritten, J. P., Lents, C. A., Guan, K., Peng, B., Ye, L., Thoma, G., Porensky, L. M., & Augustine, D. J. (2026). From data to decisions: the potential of real-time precision technologies to enhance adaptive grazing management for livestock ranchers. African Journal of Range and Forage Science, 43(1), 46-54. https://doi.org/10.2989/10220119.2025.2585814
Feng, S., Li, A., Zhou, R., Butterbach-Bahl, K., Guan, K., Jin, Z., Looms, M. C., Wang, S., Igel, C., Treat, C., Olesen, J. E., & Wang, S. (2026). Global daily 9 km remotely sensed soil moisture (2015–2025) with microwave radiative transfer-guided learning. Scientific Data, 13(1), Article 435. Advance online publication. https://doi.org/10.1038/s41597-026-06721-6
Furuta, D. C. R., 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 and T Water, 6(2), 709-719. https://doi.org/10.1021/acsestwater.5c00837
Guan, K., Qin, R., Ma, Z., Zhao, L., Liu, L., Wang, S., Keenan, T. F., Jia, M., Sihi, D., Potash, E., Asseng, S., Tang, J., Xu, X., Boomer, K., Peng, B., Jin, Z., Hoffman, F. M., Ogle, S., Coppess, J., … Runkle, B. R. K. (2026). Multi-Model Ensembles in Ecosystem Modeling: Challenges and Best Practices for Decision-Making. Global change biology, 32(8), Article e70987. https://doi.org/10.1111/gcb.70987
Guan, K., Peng, J., & Luo, Y. (2026). System and method to fuse multiple sources of optical data to generate a high-resolution, frequent and cloud-/gap-free surface reflectance product. (U.S. Patent No. 12555208).