新型铜胁迫植被指数NCSVI探索铜污染下玉米叶片光谱敏感区间

目前我国土壤重金属污染日趋严重，高光谱遥感因具有光谱分辨率高、图谱合一等特点成为农作物重金属污染研究的热点。农作物受重金属污染后其光谱会发生细微的改变，如何探寻叶片光谱中对重金属污染敏感的波段是目前的一种研究方向。提出了一种新型铜胁迫植被指数（NCSVI）来探索铜胁迫下玉米光谱敏感区间。通过设计不同梯度下的玉米铜胁迫实验，测定每个铜胁迫浓度下玉米叶片的光谱和Cu2+的含量。首先，将玉米叶片光谱分为11个子区间，以每个子区间的中间波长对应的光谱反射率构建各自的NCSVI。然后，计算NCSVI与玉米叶片中Cu2+含量的相关性系数R及均方根误差RMSE，结合水波段指数（WBI）、改进的叶绿素吸收率指数（MCARI）和归一化水指数（NDWI）这三种常规植被指数进行对比。最后，选用其他年份相同实验条件下获取的玉米叶片光谱进行验证，确认NCSVI的稳定性和有效性。结果表明，11个子区间中只有绿峰、红边、近谷和近峰A这四个子区间对应的NCSVI与玉米叶片Cu2+含量相关性系数的绝对值高于0.9，分别为-0.94，-0.97，-0.94和-0.96，均方根误差均低于15，分别为12.57，8.71，12.71和10.06，而WBI，MCARI和NDWI的相关性系数最高的仅达到0.75，均方根误差最小的为24.21，说明四个子区间对应的NCSVI对玉米叶片铜污染有着更好的指示性。利用不同年份相同条件下的玉米实验对以上结果进行验证，发现11个子区间中，R绝对值大于0.9、RMSE小于1.55的只有绿峰、红边、近谷和近峰A这四个子区间，其中R分别为-0.9，-0.97，-0.97和-0.93，RMSE分别为1.50，0.85，0.78和1.29，均优于WBI，MCARI和NDWI，与2016年实验得出的敏感子区间一致，说明NCSVI能探测铜胁迫下玉米光谱的敏感区间，具备效率高、稳定性好的特点。所提出的NCSVI指数可作为监测玉米叶片铜污染的一种方法，并为其他农作物重金属污染研究提供一定的理论支持。

A New Copper Stress Vegetation Index NCSVI Explores the Sensitive Range of Corn Leaves Spectral Under Copper Pollution

At present, heavy metal pollution in the soil is becoming more and more serious in China. Hyperspectral remote sensing has become a hot spot in the research of heavy metal pollution in crops by reason of its characteristics such as high spectral resolution and integrated maps with spectral. The spectral of crops will change slightly after being contaminated by heavy metals, how to explore the sensitive bands in the leaves spectral stresses by heavy metal pollution is a current research direction. In this study, a new copper stress vegetation index (NCSVI) was proposed to explore the sensitive range of corn leaves spectral under copper stress. By designing corn stress experiments with different gradients, the spectral and the contents of Cu2+ in corn leaves under each copper stress concentration were determined. First, the spectral of corn leaves were divided into 11 sub-band intervals, NCSVI were constructed by spectral reflectance corresponded to the middle wavelength of each sub-band interval. Then, the Pearson correlation coefficient and RMSE (Root Mean Square Error) between NCSVI and the contents of Cu2+ in each corn leaves was calculated, combined with three conventional vegetation indexes of water band index (WBI), modified chlorophyll absorption ratio index (MCARI) and normalized water index (NDWI). Finally, the corn leaves spectral which obtained under the same experimental conditions in other year were selected for verification to confirm the stability and effectiveness of NCSVI. The results show that among the 11 sub-band intervals, only the four sub-band intervals of a green peak, red edge, near the valley, and near peak A, the absolute value of the correlation coefficient between NCSVI and Cu2+ contents of corn leaves were higher than 0.9, respectively to -0.94, -0.97, -0.94，-0.96, as for RMSE, the root mean square error were less than 15, reached to 12.57, 8.71, 12.71 and 10.06. However, the highest correlation coefficient of WBI, MCARI and NDWI only reached to 0.75. The smallest RMSE was 24.21. Indicating that NCSVI corresponded to the four subintervals had a better indicator of copper pollution in corn leaves. The above results were verified by corn experiments under the same conditions in a different year, and it was found that among the 11 subintervals, only four subintervals of a green peak, red edge, near the valley, and near peak A had its absolute value of the coefficient R between NCSVI and the contents of Cu2+ in corn leaves were greater than 0.9, respectively to -0.9, -0.97, -0.97 and -0.93, as for RMSE, the root mean square error were less than 1.55, reached to 1.50, 0.85, 0.78 and 1.29, which were higher than WBI, MCARI and NDWI, and with the same sensitive sub-band intervals in the experiment of 2016, indicating that NCSVI could detect the sensitive range of corn leaves spectral stressed by Cu2+, with the characteristics of high efficiency and good stability. The NCSVI index proposed in this paper can be used as a method to monitor copper pollution in corn leaves, and provide some theoretical supports for the research of heavy metal pollution in other crops.