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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #326395
Title: Estimating maize water stress by standard deviation of canopy temperature in thermal imagery

Author
item HAN, MING - Colorado State University
item Zhang, Huihui
item DeJonge, Kendall
item Comas, Louise
item TROUT, THOMAS - Retired ARS Employee

Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/26/2016
Publication Date: 9/1/2016
Citation: Han, M., Zhang, H., DeJonge, K.C., Comas, L.H., Trout, T. 2016. Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agricultural Water Management. 177:400-409. doi:10.1016/j.agwat.2016.08.031.

Interpretive Summary: To manage irrigation water efficiently, farmers need tools to quickly asses crop water stress. We found that water-stressed crops show a higher plant canopy temperature than well-watered crops. In this research, we are using thermal imagery to monitor crop canopy temperatures and therefore crop water stress. Field experiments in replicated plots were carried out at our deficit irrigation research farm for two growing seasons. We measured temperature distribution with high spatial resolution thermal imagery for water stress detection. Results showed that the standard deviation of canopy temperature in thermal imagery responded to irrigation events and deficit irrigation treatments. These findings are valuable for potentially using high spatial resolution thermography for crop water status assessment and for managing deficit irrigation. Ultimately, the methods used here could save irrigation water and or improve water use efficiency in irrigated farming systems.

Technical Abstract: A new crop water stress index using standard deviation of canopy temperature as an input was developed to monitor crop water status. In this study, thermal imagery was taken from maize under various levels of deficit irrigation treatments in different crop growing stages. The Expectation-Maximization algorithm was used to estimate canopy temperature distribution from thermal imagery under various crop coverage and water stress conditions. Soil water deficit (SWD), leaf water potential ('), stomatal conductance, and other crop water stress indices were used to evaluate the newly developed water stress index, named the canopy temperature variance water stress index (CTVWSI). The CTVWSI responded to irrigation events, and all water stress indices show statistical significant relationship with CTVWSI. Although CTVWSI is not sensitive to small water stress changes, the result still suggests that the index calculated from standard deviation of canopy temperature in thermal imagery could be used as a crop water stress indictor.