铜胁迫下玉米污染特征波段提取与程度监测

doi:10.3964/j.issn.1000-0593(2020)02-0529-06

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摘要：我国农田重金属污染形势不容乐观。土壤中的重金属被作物根系吸收后会影响作物正常的生长发育，降低农产品质量，进而通过食物链进入人体，危害人体健康。高光谱遥感为实时动态高效监测作物重金属污染提供了可能。设置不同浓度Cu²⁺胁迫梯度的玉米盆栽实验，并采集苗期、拔节期和穗期玉米老、中、新叶片光谱数据，测定不同生长时期叶片叶绿素含量、叶片Cu²⁺含量。基于所获取的光谱数据、叶绿素含量和叶片Cu²⁺含量，结合相关分析法、最佳指数法（OIF）和偏最小二乘法（PLS）构建OIF-PLS法提取含有Cu²⁺污染信息的特征波段。首先依据苗期、拔节期和穗期叶片叶绿素含量及穗期叶片Cu²⁺含量与相应叶片光谱的相关系数初步筛选特征波段；然后，从中选取三个波段计算最佳指数因子，并以该三个波段为自变量，对玉米叶片Cu²⁺含量进行偏最小二乘回归分析，计算均方根误差；最后根据最佳指数因子最大、均方根误差最小的原则选取最佳特征波段。基于OIF-PLS法所选取的特征波段构造植被指数OIFPLSI监测重金属铜污染，并与常规的红边归一化植被指数（NDVI₇₀₅）、改进红边比值植被指数（mSR₇₀₅）、红边植被胁迫指数（RVSI）和光化学指数（PRI）监测结果做比较，验证OIFPLSI的有效性和优越性。另外利用在相同的实验方法下获取的不同年份的数据对OIFPLSI进行检验，验证OIFPLSI的适用性和稳定性。实验结果表明，基于OIF-PLS法提取的特征波段（542，701和712 nm）比基于OIF法提取的特征波段（602，711和712 nm）能更好地反映Cu²⁺污染信息；植被指数OIFPLSI与叶片Cu²⁺含量显著正相关，相关性优于NDVI₇₀₅，mSR₇₀₅，RVSI和PRI；OIFPLSI与叶片叶绿素含量显著负相关，与土壤中Cu²⁺含量显著正相关；不同生长时期OIFPLSI与土壤中Cu²⁺含量的相关性高低依次为拔节期、穗期、苗期。基于不同年份数据验证结果表明，OIFPLSI与叶片Cu²⁺含量显著正相关，OIFPLSI具有较强的稳定性。基于OIF-PLS法所提取的特征波段构建的OIFPLSI能够较好地诊断分析玉米叶片铜污染水平，可为作物重金属污染监测提供一定的技术参考。

关键词：重金属污染；光谱分析；特征波段；植被指数；农作物

Abstract：The situation of heavy metal pollution in farmland isn’t optimistic. The heavy metals in soil can affect normal growth and development of crops after being absorbed by the roots, reduce quality of agricultural products, and then enter human body through food chain, endangering human health. Hyperspectral Remote Sensing provides possibility for a real-time, dynamic and efficient monitoring of heavy metal pollution in crops. The potted corn experiment with different Cu²⁺ stress gradients was set up, the spectral data of old, middle and new leaves in seedling, jointing and spike stages were collected, and the chlorophyll content and leaves Cu²⁺ content were determined in different growth periods. Based on the spectral data, chlorophyll content and leaves Cu²⁺ content, OIF-PLS method was constructed to extract feature bands containing Cu²⁺ pollution information by combining correlation analysis, optimal index factor (OIF) and partial least square (PLS). Firstly, the characteristic bands were preliminarily screened according to correlation coefficient between chlorophyll content in leaves at seedling stage, jointing stage and spike stage and Cu²⁺ content in leaves at spike stage and corresponding leaf spectra. Then, three bands were selected to calculate optimum index factor, and the three bands were taken as independent variables to carry out partial least squares regression analysis on Cu²⁺ content in corn leaves to calculate root mean square error. Finally, the best feature band was selected according to principle of maximum optimum index factor and minimum root mean square error. The vegetation index OIFPLSI was constructed based on the characteristic bands selected by OIF-PLS method to monitor heavy metal copper pollution, and compared with red edge normalized difference vegetation index (NDVI₇₀₅), modified red edge simple ratio vegetation index (mSR₇₀₅), red-edge vegetation stress index(RVSI) and photochemical reflectance index (PRI) monitoring results to verify the effectiveness and superiority of OIFPLSI. In addition, the applicability and stability of OIFPLSI were verified by using the data obtained from different years under same experimental method. The experimental results show that the feature bands (542, 701, 712 nm) extracted from OIF-PLS can better reflect Cu²⁺ pollution information than the feature bands (602, 711, 712 nm) based on OIF. OIFPLSI was significantly positively correlated with leaf Cu²⁺ content, and the correlation was better than NDVI₇₀₅, mSR₇₀₅, RVSI and PRI. OIFPLSI was significantly negatively correlated with leaf chlorophyll content and positively correlated with Cu²⁺ content in soil. The correlation between OIFPLSI and Cu²⁺ content in soil at different growth stages is successively higher in jointing stage, ear stage and seedling stage. Based on the data of different years, the results show that OIFPLSI is positively correlated with leaf Cu²⁺ content, and OIFPLSI has strong stability. OIFPLSI based on the characteristic bands extracted by OIF-PLS method can better diagnose and analyze copper pollution level of corn leaves, which can provide a certain technical reference for crop heavy metal pollution monitoring.

Key words：Heavy metal pollution; Spectral analysis; Feature band; Vegetation index; Crop

收稿日期: 2019-01-08 修订日期: 2019-05-25

中图分类号:

TP75

基金资助: 国家自然科学基金项目（41271436，41971401），煤炭资源与安全开采国家重点实验室2017年开放基金课题（SKLCRSM17KFA09），宁夏农林科学院科技创新引导项目（NKYG-18-01）, 宁夏农牧厅东西部合作项目资助

通讯作者: 杨可明 E-mail: ykm69@163.com

作者简介: 高鹏，1994年生，中国矿业大学（北京）硕士研究生 e-mail: 1292724050@qq.com

引用本文:

高鹏，杨可明，荣坤鹏，程凤，李燕，王思佳. 铜胁迫下玉米污染特征波段提取与程度监测[J]. 光谱学与光谱分析, 2020, 40(02): 529-534.
GAO Peng, YANG Ke-ming, RONG Kun-peng, CHENG Feng, LI Yan, WANG Si-jia. Feature Band Extraction and Degree Monitoring of Corn Pollution under Copper Stress. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 529-534.

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