基于空间谱的玉米叶片铜铅污染区分及程度监测

利用高光谱遥感技术监测并识别农作物受重金属污染信息是当今热点，研究设置了不同浓度铜离子(Cu2+)、铅离子(Pb2+)胁迫梯度的玉米盆栽实验，并测取了玉米叶片的光谱及叶片中重金属离子与叶绿素含量。基于获取的光谱数据，将光谱划分为紫谷、蓝边、绿峰、红谷、红边和红肩六个光谱特征区间，通过光谱的一阶微分和二维多重信号分类(2D-MUSIC)算法构造空间谱，对各光谱特征区间进行变换分析。实验结果表明：蓝边、绿峰和红边阵列信号的空间谱在Cu2+胁迫下为双高峰，在Pb2+胁迫下为单高峰，以此能够快速、直观地区分玉米叶片所受重金属污染的Cu2+和Pb2+元素类别。红谷和红肩阵列信号空间谱的方位角谱峰值与玉米叶片中Cu2+含量的相关系数分别达到-0.954 5和-0.964 8，说明用于监测Cu2+污染程度时效果理想；紫谷阵列信号空间谱的方位角谱峰值与玉米叶片中Pb2+含量的相关系数达到-0.999 8，说明用于监测Pb2+污染程度时效果理想。同时通过与常规重金属污染监测方法绿峰高度(GH)、红边位置(REP)、红边最大值(MR)、红边一阶微分包围面积(FAR)的应用结果进行比较分析，空间谱法的应用结果与玉米叶片中重金属离子含量的相关性较高，从而验证了空间谱应用于玉米重金属污染信息监测具有更好的有效性和优越性。

Differentiation and Level Monitoring of Corn Leaf Stressed by Cu and Pb Derived from Spatial Spectrum

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.