基于空间谱的玉米叶片铜铅污染区分及程度监测

doi:10.3964/j.issn.1000-0593(2018)07-2200-09

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Abstract Identifying and monitoring the heavy metal pollution information of crops isthe research focus by hyperspectral remote sensing technology today. The potted corn experiments were set up with different Cu²⁺ and Pb²⁺ stress gradients in this research, measuring the spectral data, the content of heavy metals icon and chlorophyll of corn leaves. On the basis of the collected data, the spectra were divided into six spectral characteristic intervals: purple vallley, blue edge, green peak, red valley, red edge and red shoulder, and spectral characteristic intervals were transformed and analyzed by spatial spectrum, which was constructed by first order differential and 2D multiple signal classification (2D-MUSIC) algorithm. The analyzed and processed results show, the spatial spectra of the array signals of the blue edge, green peak and red edge are double peaks under Cu²⁺ stress. However, the spatial spectra of the array signals of the blue edge, green peak and red edge were single peak under Pb²⁺ stress. Thus, the heavy metals elements categories of Cu²⁺ and Pb²⁺ in polluted corn could be quickly and visually distinguished. Azimuth spectrum peaks of array signal spatial spectra of red valley and red shoulder were gradually decreased under Cu²⁺ stress, and the correlation coefficients of azimuth spectrum peak values of red valley and red shoulder and the Cu²⁺ contents in corn leaves reached -0.954 5 and -0.964 8. It was indicated that the effect was ideal when monitoring the level of Cu²⁺ pollution; azimuth spectrum peaks of array signal spatial spectrum of purple valley were gradually decreased under Pb²⁺ stress, and the correlation coefficient of azimuth spectrum peak value of purple vallley and the Pb²⁺ contents in corn leaves reached 0.999 8, it was indicated that the effect was ideal when the level of Pb²⁺ pollution was monitored. At the same time, the application results of the spatial spectrum were analyzed and compared with the results obtained by some conventional methods such as green-peak height (GH), red edge position (REP), maximum-value of red-edge (MR) and first-derivative area of red-edge (FAR) for monitoring the crop heavy metal pollution information, the spatial spectrum theory was verified to have better effectiveness and superiority in monitoring heavy metal pollution information of the corn leaves.

Key words：Heavy metal pollution; Corn leaf spectrum; 2D multiple signal classification; Spatial spectrum; Spectral characteristic intervals

Received: 2017-07-27 Accepted: 2017-12-09

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)07-2200-09 OR https://www.gpxygpfx.com/EN/Y2018/V38/I07/2200

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