玉米叶片光谱弱差信息的重金属污染定性分析

Qualitative discrimination of heavy metal contamination in corn leaf with weak spectral information

不同重金属污染下的玉米叶片光谱响应存在细微差异。设计不同浓度的铜(Cu)和铅(Pb)污染实验,并测量不同浓度铜离子和铅离子胁迫下玉米叶片的光谱反射率、Cu含量、Pb含量及对应叶绿素含量。针对光谱弱差信息甄别的难点,结合光谱微分处理、光谱角、正切函数增强和波谱分段检测等构建一种甄别玉米叶片Cu、Pb胁迫的分段微分光谱角正切(segmentation derivative spectral angle tangent,SDSAT)模型。甄别玉米叶片不同生长程度的玉米叶片在Cu、Pb胁迫下的光谱响应差异,并通过区分玉米冠层Cu、Pb胁迫光谱的差异以验证该模型有效可行。实验结果表明,SDSAT模型甄别Cu、Pb胁迫玉米光谱主要差异在"紫光""红边"和"近峰B"波段区间,这些差异也是鉴别Cu、Pb污染的定性分析标志,并可以根据模型测度结果分析玉米的污染程度。

There were slight differences in the spectral response of maize leaves under different heavy metal pollution. Experiments using varying concentrations of copper（ Cu） and lead（ Pb） were designed,and the spectral reflectance and contents of Cu,Pb,and chlorophyll of the maize leaves under different ion concentrations of Cu and Pb were measured. Because it is difficult to discriminate weak spectral information,a model of segmentation derivative spectral angle tangent（ SDSAT） was constructed to screen for Cu and Pb stress on maize leaves by combining differential spectral processing,spectral angle,tangent function enhancement,and spectral segmentation detection. The spectral response of the maize leaves at different levels of Cu and Pb was identified,and the feasibility of the model was determined by distinguishing the difference in spectra of the maize canopy under Cu and Pb stress. The results show that the SDSAT model distinguished the corn spectral differences under Cu and Pb stress efficiently,and the spectral differences were mainly in the ＂purple＂＂red edge＂and ＂near peak B＂ranges. These differences are qualitative analysis marks for identifying Cu and Pb pollution,and the pollution degree of maize can be analyzed using this model.