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Kaiyu Guan

University of Illinois at Urbana-Champaign

Earth Sciences


Chongya Jiang, Kaiyu Guan, Ming Pan, Youngryel Ryu, Bin Peng, and Sibo Wang (2020): BESS-STAIR: a framework to estimate daily, 30-meter, and all-weather crop evapotranspiration using multi-source satellite data for the U. S. Corn Belt, Hydrology and Earth System Sciences, The European Geosciences Union, Vol 24, Num 3, pp1251-1273


Yaping Cai, Kaiyu Guan, David Lobell, Andries B. Potgieter, Shaowen Wang, Jian Peng, Tianfang Xu, Senthold Asseng, Yongguang Zhang, Liangzhi You, and Bin Peng (2019): Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches, Agricultural and Forest Meteorology, Elsevier B.V., Vol 274, pp144-159
Yan Li, Kaiyu Guan, Albert Yu, Bin Peng, Lei Zhao, Bo Li, and Jian Peng (2019): Toward building a transparent statistical model for improving crop yield prediction: Modeling rainfed corn in the U.S., Field Crops Research, Elsevier B.V., Vol 234, pp55-65


Yaping Cai, Kaiyu Guan, Jian Peng, Shaowen Wang, Christopher Seifert, Brian Wardlow, and Zhan Li (2018): A High-Performance and In-Season Classification System of Field-Level Crop Types Using Time-Series Landsat Data and a Machine Learning Approach, Remote Sensing of Environment, Elsevier BV, Vol 210, pp35-47
Bin Peng, Kaiyu Guan, Ming Pan, and Yan Li (2018): Benefits of Seasonal Climate Prediction and Satellite Data for Forecasting U.S. Maize Yield, Geophysical Research Letters, The American Geophysical Union, Vol 45, Num 18, pp9662-9671
Bin Peng, Kaiyu Guan, Min Chen, David M. Lawrence, Yadu Pokhrel, Andrew Suyker, Timothy Arkebauer, and Yaqiong Lu (2018): Improving Maize Growth Processes in the Community Land Model: Implementation and Evaluation, Agricultural and Forest Meteorology, Elsevier BV, Vol 250-251, pp64-89


Kaiyu Guan, Jin Wu, John S. Kimball, Martha C. Anderson, Steve Frolking, Bo Li, Christopher R. Hain, and David B. Lobell (2017): The Shared and Unique Values of Optical, Fluorescence, Thermal and Microwave Satellite Data for Estimating Large-Scale Crop Yields, Remote Sensing of Environment, Elsevier BV, Vol 199, pp333-349


Kaiyu Guan, Jian Peng, Chongya Jiang, Bin Peng, Sibo Wang (2019): Building an Objective Seasonal Forecasting System for U.S. Corn and Soybean Yields, 2019 Blue Waters Annual Report, pp90-91
Kaiyu Guan, Jian Peng, Chongya Jiang, Bin Peng, Sibo Wang (2019): Monitoring Field-Scale Crop Water Use using a Satellite Data-Driven Mechanistic Modeling Approach, 2019 Blue Waters Annual Report, pp88-89


Kaiyu Guan, Jian Peng, Bin Peng, Yunan Luo, and Sibo Wang (2018): Forecasting Global Crop Productivity through Integrating Novel Satellite Data and Process-Based Models, Blue Waters annual-book summary slide
Kaiyu Guan, Jian Peng, Bin Peng, Yunan Luo, Sibo Wang (2018): Forecasting Global Crop Productivity through Integrating Novel Satellite Data and Process-Based Models, 2018 Blue Waters Annual Report, pp82-83


Kaiyu Guan (2016): Forecasting global crop productivity using novel satellite data and process-based models, 2016 Blue Waters Annual Report, pp92-93

Scientists propose improvements to precision crop irrigation

Apr 29, 2021

Kaiyu Guan, assistant professor in NRES, Blue Waters professor with the National Center for Supercomputing Applications, and project leader on the study, pioneered a way to fuse high-resolution and high-frequency satellite data into one integrated high spatial-temporal resolution product to help track soil and plant conditions.


U of I researchers measure photosynthesis from space

Feb 16, 2021

Research team develops model to accurately calculate gross primary productivity (GPP) in bioenergy crops using satellite data.


New satellite-based algorithm pinpoints crop water use

Mar 20, 2020

A new high-resolution mapping framework called BESS-STAIR is composed of a satellite-driven biophysical model integrating plants' water, carbon and energy cycles—the Breathing Earth System Simulator (BESS)—with a generic and fully automated fusion algorithm called STAIR (SaTellite dAta IntegRation).


Nanosatellites improve detection of early-season corn nitrogen stress

Jan 13, 2020

For corn growers, the decision of when and how much nitrogen fertilizer to apply is a perennial challenge. Scientists have shown that nanosatellites known as CubeSats can detect nitrogen stress early in the season, potentially giving farmers a chance to plan in-season nitrogen fertilizer applications and alleviate nutrient stress for crops.


How Parallel Processing Solves Our Biggest Computational Problems

Nov 6, 2019

Researchers solved a four-decade-old mystery, proving that the innermost part of matter that orbits, then collapses into, black holes aligns with those black holes; Last year, researchers at U of I’s Department of Natural Resources and Environmental Sciences topped the feds’ industry-standard forecast by incorporating more data which proved more accurate by nearly five bushels per acre.


New research accurately predicts Australian wheat yield months before harvest

May 13, 2019

New research harnesses machine learning to accurately predict wheat yields in Australia months before harvest. The method could be translated to other crops and nations.


HPC Career Notes: May 2019 Edition

May 1, 2019

The NSF has also named Kaiya Guan a recipient of an NSF CAREER Award.


Excessive rainfall as damaging to corn yield as extreme heat, drought

Apr 30, 2019

In a new study, an interdisciplinary team from the University of Illinois linked crop insurance, climate, soil and corn yield data from 1981 through 2016.


Illinois research accurately predicts U.S. end-of-season corn yield

Sep 27, 2018

Using seasonal forecasts and satellite data, researchers develop advanced corn yield prediction system for national and county levels.


New algorithm fuses quality and quantity in satellite imagery

Jun 4, 2018

Using a new algorithm, University of Illinois researchers may have found the solution to an age-old dilemma plaguing satellite imagery – whether to sacrifice high spatial resolution in the interest of generating images more frequently, or vice versa. The team’s new tool eliminates this trade-off by fusing high-resolution and high-frequency satellite data into one integrated product, and can generate 30-meter daily continuous images going back to the year 2000.


Satellites, supercomputers, and machine learning provide real-time crop type data

Apr 4, 2018

Corn and soybean fields look similar from space -- at least they used to. But now, scientists have proven a new technique for distinguishing the two crops using satellite data and the processing power of supercomputers.


Supercomputing Better Tools for Long-Term Crop Prediction

Feb 14, 2018

NCSA Professor Kaiyu Guan and NCSA postdoc fellow, Bin Peng have implemented and evaluated a new maize growth model. The CLM-APSIM model combines superior features in both Community Land Model (CLM) and Agricultural Production Systems sIMulator (APSIM), creating one of the most reliable tools for long-term crop prediction in the U.S. corn belt.


Predicting the effect of climate change on crop yields

Jan 3, 2018

Researchers from University of Illinois are attempting to bridge two types of computational crop models to become more reliable predictors of crop production in the U.S. Corn Belt.


Harnessing rich satellite data to estimate crop yield

Aug 27, 2017

Without advanced sensing technology, humans see only a small portion of the entire electromagnetic spectrum. Satellites see the full range—from high-energy gamma rays, to visible, infrared and low-energy microwaves. The images and data they collect can be used to solve complex problems. For example, satellite data is being harnessed by researchers at the University of Illinois for a more complete picture of cropland and to estimate crop yield in the U.S. Corn Belt. “In places where we may see just the color green in crops, electromagnetic imaging from satellites reveals much more information about what’s actually happening in the leaves of plants and even inside the canopy. How to leverage this information is the challenge,” says Kaiyu Guan, an environmental scientist at the U of I and the lead author on the research. “Using various spectral bands and looking at them in an integrated way, reveals rich information for improving crop yield.”


Modeling the future of soybeans in the Midwest

Feb 3, 2017

How will the rising temperatures expected to occur with global climate change affect soybean growth in the Midwest? Rather than wait and see, researchers at the University of Illinois will use real crop data and computer modeling to better predict future impacts of higher temperatures on agricultural production and identify promising targets for adaptation. The project is being funded with a $420,000 USDA National Institute for Food and Agriculture grant. U of I environmental scientist Kaiyu Guan is the project director. Carl Bernacchi and Elizabeth Ainsworth are co-project directors. Both are plant physiologists in the U of I Department of Plant Biology and Department of Crop Sciences. The project will look at how temperature affects major plant processes such as photosynthesis and respiration.


NASA Career Award Winner Uses Blue Waters Supercomputer to Mine Crop Yield Data

Sep 22, 2016

A new faculty member at the University of Illinois who received a prestigious NASA Career award is now using the Blue Waters supercomputer on campus to gain new insights into crop yields through satellite data. Assistant professor Kaiyu Guan’s work builds off his previous research with satellite and earth system modeling. Prior to coming to Illinois, his PhD and postdoc work focused mostly on how rainfall and other components of the hydrological cycle control plant growth in tropical forests, savannas and farms.


Adaptations to climate change impact long term crop yields

Sep 7, 2016

As the globe continues to spin toward a future with higher temperatures, crop yields will likely decrease if farmers do not adapt to new management or technology practices. Establishing new strategies is particularly difficult for sorghum farmers in West Africa where seed varieties and fertilizer are scarce, while drought and unpredictable rainfall are prevalent. Using more heat-resistant sorghum varieties may yield the most benefits, research shows. “Climate change will impact both natural and agricultural ecosystems on the planet. The difference is that farmers can do things to adapt to the changing climate, and hopefully alleviate the impacts on their crops,” says Kaiyu Guan, an environmental scientist at the University of Illinois.