Estimation of Stomatal Conductance using Crop Water Stress Index based on the Thermal Image at a Leaf Scale

Author(s):  
Hoejeong Jeong ◽  
Jae-Hyun Ryu ◽  
Sang-il Na ◽  
Jaeil Cho

<p>  In 1980s, Crop Water Stress Index (CWSI) is suggested to indicate the water stress of crops. CWSI is based on the leaf energy balance, which is closely related to leaf temperature. To calculate CWSI, meteorological factors such as air temperature and vapor pressure deficit should be measured besides leaf temperature. As recent technology has been developed, leaf temperature can be easily observed by thermal camera or infrared thermometer. Stomatal conductance (g<sub>s</sub>, mmol m<sup>-2</sup> s<sup>-1</sup>) is one of the critical factors to understand crop photosynthesis and water demand. In addition, the behaviors of g<sub>s</sub> can represent the biotic and abiotic plant stresses. In abnormal condition, such as drought, insects or disease, g<sub>s</sub> getting lower. The observation of g<sub>s</sub> will make better to evaluate and predict crop growth and conditions. Therefore, the time series data of g<sub>s</sub> is useful for the monitoring of crop growth and the quick detection of abnormal crop condition in smart-farming system but there are some limitations to measure g<sub>s</sub> continuously and easily.</p><p>  We assume that there is some relationship between CWSI and g<sub>s</sub> because both has strong relation to leaf temperature. Thus, the aim of this study is to investigate possibility of estimation of g<sub>s</sub> using CWSI which is derived from thermal image. Through the data collected from literatures, negative correlations between CWSI and g<sub>s</sub> were revealed. The slope of correlation was changed according to crop types. In addition, as a result of simulation, there is almost linear negative relationship between CWSI and g<sub>s</sub>, and the slope was determined by maximum stomatal conductance (g<sub>s_max</sub>). Field measurement in this study was also demonstrated to identify such correlation. Further, various methods to measure CWSI were tested. This relationship will contribute to not only monitoring of crop stress for irrigation scheduling in smart farm system but also estimating evapotranspiration, photosynthesis, and crop yield.</p>

Horticulturae ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 86
Author(s):  
Chen Ru ◽  
Xiaotao Hu ◽  
Wene Wang ◽  
Hui Ran ◽  
Tianyuan Song ◽  
...  

Precise irrigation management of grapevines in greenhouses requires a reliable method to easily quantify and monitor the grapevine water status to enable effective manipulation of the water stress of the plants. This study evaluated the applicability of crop water stress index (CWSI) based on the leaf temperature for diagnosing the grapevine water status. The experiment was conducted at Yuhe Farm (northwest China), with drip-irrigated grapevines under three irrigation treatments. Meteorological factors, soil moisture contents, leaf temperature, growth indicators including canopy coverage and fruit diameter, and physiological indicators including SPAD (relative chlorophyll content), stem water potential (φs), stomatal conductance (gs), and transpiration rate (E) were studied during the growing season. The results show that the relationship between the leaf-air temperature difference (Tc-Ta) and the plant water status indicators (φs, gs, E) were significant (P < 0.05), and the relationship between gs, E and Tc-Ta was the closest, with R2 values ranging from 0.530–0.604 and from 0.545–0.623, respectively. CWSI values are more easily observed on sunny days, and it was determined that 14:00 BJS is the best observation time for the CWSI value under different non-water-stressed baselines. There is a reliable linear correlation between the CWSI value and the soil moisture at 0–40 cm (P < 0.05), which could provide a reference when using the CWSI to diagnose the water status of plants. Compared with the Tc-Ta value, the CWSI could more accurately monitor the plant water status, and above the considered indictors, gs has the greatest correlation with the CWSI.


2017 ◽  
Vol 35 (6) ◽  
pp. 549-560 ◽  
Author(s):  
David A. Carroll ◽  
Neil C. Hansen ◽  
Bryan G. Hopkins ◽  
Kendall C. DeJonge

2013 ◽  
Vol 118 ◽  
pp. 79-86 ◽  
Author(s):  
N. Agam ◽  
Y. Cohen ◽  
J.A.J. Berni ◽  
V. Alchanatis ◽  
D. Kool ◽  
...  

1994 ◽  
Vol 86 (3) ◽  
pp. 574-581 ◽  
Author(s):  
H. R. Jalali‐Farahani ◽  
D. C. Slack ◽  
D. M. Kopec ◽  
A. D. Matthias ◽  
P. W. Brown

1994 ◽  
Vol 86 (1) ◽  
pp. 195-199 ◽  
Author(s):  
Donald J. Garrot ◽  
Michael J. Ottman ◽  
D.D. Fangmeier ◽  
Stephen H. Husman

Sign in / Sign up

Export Citation Format

Share Document