Evaluating Heavy-Metal Stress Levels in Rice Using a Theoretical Model of Canopy-Air Temperature and Leaf Area Index Based on Remote Sensing

Rapid and accurate analysis of heavy-metal contamination in crops is important with regard to human health, which has been made possible by the development of remote sensing technology. The objective of this study is to develop a theoretical model based on physical energy balance equations and remote sensing data to evaluate heavy-metal stress in rice based on physiological function responses. Heavy-metal stress can induce a series of variations in the physiological functions of plants, and canopy temperature is an indicator of these changes. Air temperature and leaf area index (LA!) can also affect the canopy temperature, and thus, these parameters were included in the model. Three extreme states of heavy-metal stress in rice were analyzed: (1) No stress just after transplant; (2) no stress; and (3) severe stress corresponding to the maximum possible LA! value. The theoretical model was based on the shape of a triangle, and a normalized heavy-metal stress index (HMS!) was proposed to describe the stress levels quantitatively. Validation by field experimentation showed that the HMS! is a reliable indicator of stress levels in rice. Because canopy temperature and LA! values can be obtained easily using remote sensing data, this method was further validated at the regional scale, and the distribution of heavy-metal stress levels across Zhuzhou County, Hunan Province, China, was rapidly determined.