Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology.

Tianjiao Liu,Xiangnan Liu,Ling Wu,Meiling Liu

doi:10.3390/s18030860

Tianjiao Liu, Xiangnan Liu + Show 2 more

Open Access

https://doi.org/10.3390/s18030860

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Abstract

Heavy metal pollution of croplands is a major environmental problem worldwide. Methods for accurately and quickly monitoring heavy metal stress have important practical significance. Many studies have explored heavy metal stress in rice in relation to physiological function or physiological factors, but few studies have considered phenology, which can be sensitive to heavy metal stress. In this study, we used an integrated Normalized Difference Vegetation Index (NDVI) time-series image set to extract remote sensing phenology. A phenological indicator relatively sensitive to heavy metal stress was chosen from the obtained phenological periods and phenological parameters. The Dry Weight of Roots (WRT), which directly affected by heavy metal stress, was simulated by the World Food Study (WOFOST) model; then, a feature space based on the phenological indicator and WRT was established for monitoring heavy metal stress. The results indicated that the feature space can distinguish the heavy metal stress levels in rice, with accuracy greater than 95% for distinguishing the severe stress level. This finding provides scientific evidence for combining rice phenology and physiological characteristics in time and space, and the method is useful to monitor heavy metal stress in rice.

Highlights

Heavy metal pollution in farmland has become an increasingly serious problem for modern agriculture that urgently requires a solution
The consistency between Normalized Difference Vegetation Index (NDVI) values acquired from Charge-Coupled Device (CCD) and those obtained from ETM+
Compared with other remote sensing data commonly used for monitoring vegetation phenology, CCD images have appropriate spatial and temporal resolution, which is more suitable for phenological research

Summary

Introduction

Heavy metal pollution in farmland has become an increasingly serious problem for modern agriculture that urgently requires a solution. Heavy metal pollution can cause crop growth stress, affecting the yield and quality of crops as well as severely affecting human health after entering the human body through the food chain [1,2]. Polluted land accounts for one-sixth of China’s total arable land. By understanding the distribution and severity of the pollution can remediation measures be more targeted. Accurately monitoring heavy metal stress in real time is of great importance [3,4,5]. Compared to traditional ground measurement methods, remote sensing (RS)

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Conclusion