Remote Sensing Applications and Decision Support

Thermal infrared imaging of the variability of canopy-air temperature difference distribution for heavy metal stress levels discrimination in rice

[+] Author Affiliations
Biyao Zhang, Xiangnan Liu, Meiling Liu, Dongmin Wang

China University of Geosciences, School of Information Engineering, Beijing, China

J. Appl. Remote Sens. 11(2), 026036 (Jun 23, 2017). doi:10.1117/1.JRS.11.026036
History: Received January 11, 2017; Accepted June 6, 2017
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Abstract.  This paper addresses the assessment and interpretation of the canopy-air temperature difference (Tc-Ta) distribution as an indicator for discriminating between heavy metal stress levels. Tc-Ta distribution is simulated by coupling the energy balance equation with modified leaf angle distribution. Statistical indices including average value (AVG), standard deviation (SD), median, and span of Tc-Ta in the field of view of a digital thermal imager are calculated to describe Tc-Ta distribution quantitatively and, consequently, became the stress indicators. In the application, two grains of rice growing sites under “mild” and “severe” stress level were selected as study areas. A total of 96 thermal images obtained from the field measurements in the three growth stages were used for a separate application of a theoretical variation of Tc-Ta distribution. The results demonstrated that the statistical indices calculated from both simulated and measured data exhibited an upward trend as the stress level becomes serious because heavy metal stress would only raise a portion of the leaves in the canopy. Meteorological factors could barely affect the sensitivity of the statistical indices with the exception of the wind speed. Among the statistical indices, AVG and SD were demonstrated to be better indicators for stress levels discrimination.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Topics

Metals

Citation

Biyao Zhang ; Xiangnan Liu ; Meiling Liu and Dongmin Wang
"Thermal infrared imaging of the variability of canopy-air temperature difference distribution for heavy metal stress levels discrimination in rice", J. Appl. Remote Sens. 11(2), 026036 (Jun 23, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.026036


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