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Kaiyu Guan

University of Illinois at Urbana-Champaign

Environmental Biology

2018

Luo, Y., Guan, K., and Peng, J (2018): STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud -/gap-free surface reflectance product, Remote Sensing of Environment (in revision)
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, 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

2017

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

2016

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

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.”


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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.


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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.


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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.


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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.


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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.


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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.


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