Spectral characteristics of copper-stressed vegetation leaves and further understanding of the copper stress vegetation index

ABSTRACT

This study analysed the changing pattern of the spectral features of copper-stressed leaves for several vegetation types, and explored the mechanism of the Copper Stress Vegetation Index (CSVI). First, the change of seven key spectral features (Green Peak, Red Valley, Red Shoulder, NIR (Near Infrared) Reflectance Platform, Blue-Edge, Yellow-Edge, and Red-Edge) with copper stress level from low to high, were presented and analysed. Second, the chlorophyll contents in leaves were investigated to explain the spectral characteristics at the visible band. Third, the leaf structure and absorption related to copper were analysed to explore the reason of changing pattern at NIR band. The results showed that there are significant changing trends at Blue-Edge, Green Peak, and Red-Edge while the changing pattern at NIR band depends on the vegetation type. The analysis on chlorophyll content, leaf structure, and absorption related to copper, gave an overall mechanism explanation for the spectral characteristics of copper-stressed vegetation and the wavelengths used in CSVI. The results and conclusions in this paper, contribute new knowledge of copper-stressed vegetation reflectance and the CSVI, and provide mechanism basement for the remote sensing of copper-stressed vegetation.

Acknowledgements

The authors would like to thank Prof. Dr Suhong Liu and Prof. Dr Xinhui Liu at Beijing Normal University for providing the copper-stressed vegetation datasets and thank Dr Ying Qu at Northeast Normal University and Beijing Normal University for providing the original version of Figure 1 and the related data. Thank Haboudane et al. (2002) for the data of Figure 4.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University [Grant No. 18T04]; the National Natural Science Foundation of China and the STFC of the United Kingdom [Grant No. 61661136006]; and the National Natural Science Foundation of China [Grant No. 51709003]; and the Fundamental Research Funds for the Central Universities and the Training Program of Innovation and Entrepreneurship for Undergraduates of China University of Mining and Technology (Beijing) [Grant No. C201802778].