Quantifying plant physiological responses to drought and high-temperature stress in the Midwest U.S.: Through scaling from proximal hyperspectral sensing to satellite monitoring

The U.S. Corn Belt is the most productive agricultural region in the world and quantifying and predicting its crop productivity is critical to achieve and maintain global food security. To quantify crop productivity reliably, understanding crop responses to climate variability is a prerequisite, but our current knowledge and capability are limited especially regarding environmental impacts on crop physiology, which could be decoupled with canopy structural variability. This study aimed to answer a question, “what are the canopy structural and plant physiological responses of crops to droughts and high-temperature stresses in the U.S. Corn Belt?” As the first step, I specified the major environmental factor defining agricultural droughts in the U.S. Corn Belt (Chapter 2) because low soil water supply and high atmospheric water demand both can cause drought stress. Decadal dataset from Ameriflux network revealed that high atmospheric water demand played a dominant role in defining agricultural drought in the Corn Belt. In Chapter 3, canopy structural variability was reliably quantified by applying scalable estimation methods to novel satellite remote sensing datasets of a high-spatiotemporal resolution. I used a rich ground truth dataset collected from a network of 36 camera-monitoring sites, and based on the ground truth data, validated the scalable algorithms, and reliably quantified field-scale crop canopy structure. Chapter 4 and 5 investigated quantifying crop physiological responses using sun-induced chlorophyll fluorescence (SIF) from hyperspectral remote sensing techniques. Particularly, Chapter 4 evaluated soybean responses to high-temperature stress using an unprecedented experiment that includes four levels of treatments. SIF measurements from the experiment plots revealed that SIF accurately quantified crop productivity as well as non-structural stress impacts (i.e., decreases in photosynthetic rate), while conventional approaches were unable to capture such physiological impacts. Chapter 5 expanded the scope for SIF-based physiological investigations to various spatial scales (i.e., experimental plots, continuously monitored fields, and a large region, the U.S. Corn Belt), and demonstrated the advantages of disaggregating SIF into structural and physiological signals and utilizing physiology-relevant signals from SIF. This study contributes to quantifying dynamic crop responses under stresses in both structural and physiological aspects, and ultimately, contributes to reliable quantification of crop productivity and yield at large scales.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Natural Res & Env Sciences

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Kim, Hyungsuk

Contributors dc:contributor

• Guan, Kaiyu
• Bernacchi, Carl J
• Sivapalan, Murugesu
• Gentine, Pierre

• crop
• physiological stress
• drought stress
• high-temperature stress
• hyperspectral remote sensing
• chlorophyll fluorescence

Statement dc:rights

• Copyright by Hyungsuk Kim, 2021

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/110776

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/110776