铜铅胁迫下玉米叶片弱光谱信息的LD-CR-SIDSCA<sub>tan</sub>探测模型

doi:10.3964/j.issn.1000-0593(2019)07-2091-09

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Abstract When crops are contaminated with heavy metals, their tissue structure and chlorophyll content will be destroyed, which will affect the metabolism and health of crops. People and animals will have fatal injuries if they eat the contaminated crops. Hyperspectral remote sensing is now widely used to monitor the extent of crops affected by heavy metals, and in the heavy metal pollution, the spectral crop leaves are still very similar to those of the traditional monitoring methods and spectral characteristic parameters of routine, so it is difficult to distinguish between different spectral weak information, and application of hyperspectral remote sensing is the focus and difficulty of the study. The maize leaf spectral data, chlorophyll content and relative content of heavy metals Cu²⁺ and Pb²⁺ were collected by setting different concentrations of Cu²⁺ and Pb²⁺ stress. A LD-CR-SIDSCAtan model combined with the continuum removal (CR), spectral correlation angle (SCA), spectral information divergence (SID) and tangent function (Tan) and Langmuir distance (LD) is proposed in this study, and the traditional measure, such as spectral correlation coefficient (SCC), SA (spectral angle), tangent spectrum (DSA), spectral information divergence and spectral correlation tangent (SIDSAM_tan), spectral information divergence and spectral gradient tangent (SIDSGA_tan) and conventional spectral characteristic parameters, such as the maximum value of red edge (MR), green peak height (GH) and red edge area surrounded by a first order differential (FAR), red edge derivative curve steepness (FCDR), blue (DB), red band depth (RD) compared to verify the feasibility and superiority of the model. The LD-CR-SIDSCA_tan model was applied to measure the spectral difference information about the overall waveform and the subband of maize leaves under Cu²⁺ and Pb²⁺ stress at different concentrations. The results show that the LD-CR-SIDSCA_tan model realized the qualitative analysis of heavy metal Cu²⁺ and Pb²⁺ pollution, could measure the spectral correlation coefficient of more than 0.99 of the difference information between the similar spectral information, and waveform difference information was significantly related to the leaf chlorophyll content and the relative content of heavy metals Cu²⁺ and Pb²⁺ that measured, and also found the spectra response wave band under the stress of heavy metals Cu²⁺ and Pb²⁺. When the whole spectral range of spectral data is measured, the spectral difference is more obvious when the value of the model is negative. When the value of the model is positive, the larger the value of the model is, the larger the spectral difference will be. Therefore, with the increase of heavy metals Cu²⁺ and Pb²⁺ concentration, the difference of spectra increased, which means that the heavy metal Cu²⁺ and Pb²⁺ pollution degree is more serious; maize plants suffer from heavy metal pollution in Cu²⁺ stress when measuring the local subband range of spectral data, in the “blue” and “red edge”, “near the Valley”, “at the peak of B” were specially sensitive to heavy metal Cu²⁺ stress pollution response and can be used as an effective band of monitoring heavy metal pollution Cu²⁺; when the maize plants are under heavy metal pollution in Pb²⁺ stress, in the “Purple Valley”, “blue”, “yellow” and “red Valley”, “red edge”, “near at the peak of A” were specially sensitive to heavy metal Pb²⁺ stress pollution response and can be used as an effective band of monitoring heavy metal pollution Pb²⁺. Finally, through the linear fitting analysis of the application results of LD-CR-SIDSCA_tan model and the content of Cu²⁺ and Pb²⁺ in maize leaves, the pollution degree of heavy metals Cu²⁺ and Pb²⁺ to maize plants was inversed and predicted.

Key words：Hyperspectral remote sensing; Maize leaf; Heavy metal pollution; Spectral weak information; Spectral analysis

Received: 2018-03-27 Accepted: 2018-07-09

ZTFLH:

TP75

Corresponding Authors: YANG Ke-ming E-mail: ykm69@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)07-2091-09 OR https://www.gpxygpfx.com/EN/Y2019/V39/I07/2091