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(NDVI 705)、改进红边比值植被指数(mSR 705)、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NDVI 705,mSR 705,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+�
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 Cu 2+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 Cu 2+content were determined in different growth periods.Based on the spectral data,chlorophyll content and leaves Cu 2+content,OIF-PLS method was constructed to extract feature bands containing Cu 2+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 Cu 2+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 Cu 2+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(NDVI 705),modified red edge simple ratio vegetation index(mSR 705),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 st
作者
高鹏
杨可明
荣坤鹏
程凤
李燕
王思佳
GAO Peng;YANG Ke-ming;RONG Kun-peng;CHENG Feng;LI Yan;WANG Si-jia(State Key Laboratory Coal Resources and Safe Mining,China University of Mining&Technology(Beijing),Beijing 100083,China;Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2020年第2期529-534,共6页
Spectroscopy and Spectral Analysis
基金
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
关键词
重金属污染
光谱分析
特征波段
植被指数
农作物
Heavy metal pollution
Spectral analysis
Feature band
Vegetation index
Crop
