摘要
在植物叶绿素特征波长变量筛选过程中,与叶绿素关系较弱的波长变量极易被忽略,导致这些弱信息变量包含叶绿素的有效信息丢失,因此,确定叶片光谱中弱信息变量对揭示叶绿素高光谱响应规律具有重要意义。该研究以江汉平原大豆鼓粒期的叶片为研究对象,采集80组大豆叶片高光谱和SPAD(soil and plant analyzer development)值,分析SPAD值与大豆叶片反射率相关关系和光谱波长变量自相关关系,基于迭代和保留信息变量法(iteratively retains informative variables,IRIV)筛选大豆叶片的特征波长变量,建立偏最小二乘回归(partial least squares regression,PLSR)和支持向量机(support vector machine,SVM)模型估测SPAD值。结果表明,大豆叶片SPAD值与光谱反射率在可见光波段具有极显著负相关,在近红外波段存在不显著的正相关性(P>0.01);可见光、近红外2波段的波长变量之间相关性较弱,但2波段内变量之间的相关性较强;基于IRIV算法确定了大豆叶绿素的特征波长变量,利用特征波长变量建立的估测模型的估测能力高于仅利用强信息波长变量建立的估测模型,表明弱信息变量对估测叶片SPAD值具有重要意义;IRIV-SVM模型估测能力最优,验证集R2和相对分析误差(RPD)分别为0.73、1.82。该文尝试证明了光谱中弱信息变量的重要性,为揭示叶片高光谱响应机理提供了理论依据。
Chlorophyll is a good indicator of plant nutrition stress,photosynthetic ability and aging process.The use of hyperspectral remote sensing technology to monitor the chlorophyll status of soybean leaves is of great significance for soybean growth diagnosis and fertilization regulation.However,the information weakly correlated to chlorophyll is very easy to be neglected in the selecting process,which leads to the loss of effective information containing chlorophyll.Therefore,it is important to determine the weak information variables in leaf spectra so as to reveal the rules of hyperspectral response of chlorophyll.This study collected 80 sets of soybean leaf samples at seed-filling period in Jianghan Plain.The leaf hyperspectral data were measured by ASD HandHeld2 type spectrometer and SPAD(soil and plant analyzer development)value was measured by SPAD 502 chlorophyll meter in laboratory.The concentration gradient method was used to divide the whole sample set(80 samples)into a calibration set(54 samples)and a validation set(26 samples).Then the correlation between SPAD value and soybean leaf reflectance was analyzed,and as well as for every two spectral wavelength variables.The characteristic wavelengths of leaf spectrum were extracted from full bands based on iteratively retains informative variables(IRIV)method.Finally Partial Least Squares Regression(PLSR)model and Support Vector Machine(SVM)model were calibrated to estimate soybean leaf SPAD values by using characteristic variables and full spectral variables,respectively.The performance of the SPAD estimation model was tested using the determination coefficients(R2),root mean squared error(RMSE),and relative percent deviation(RPD).The results showed that the SPAD values of soybean leaves correlated strongly with the spectral wavelength variables in the visible bands,especially in the band of 500-650 nm and 690-730 nm,which were significantly negatively correlated with the spectral wavelength variables(P=0.01).The correlation between visible wavelength variables and near-infrared wavelength variables of soybean leaves was weak,and the correlation between internal variables of two bands was high,especially the collinear problem of near-infrared bands was prominent.Then 9 characteristic wavelength variables of soybean chlorophyll were determined based on IRIV algorithm.Among them,5 strong information variables and 3 weak information variables were extracted in the visible bands,and 1 weak information variable was extracted in the near-infrared bands.The SVM model was better than the PLSR model.The R2 of validation set in PLSR model was 0.52,but in SVM model was higher than 0.59,so the SPAD value and the reflectance tended to be nonlinear correlated.IRIV algorithm can effectively determine the characteristic wavelength variables of SPAD value of soybean leaves.The estimation ability based on IRIV characteristic wavelength variables model was better than that based on full spectral variables model and strong information variables model.The IRIV-SVM performance was the best,and the R2 and RPD in validation set were 0.73 and 1.82,respectively.This study attempts to demonstrate the importance of weak information variables in the spectra and provides a theoretical basis for revealing the hyperspectral response mechanism of the leaves.
作者
于雷
章涛
朱亚星
周勇
夏天
聂艳
Yu Lei;Zhang Tao;Zhu Yaxing;Zhou Yong;Xia Tian;Nie Yan(Hubei Provincial Key Laboratory for the Analysis and Simulation of Geographical Process,Central China Normal University,Wuhan 430079,China;College of Urban and Environmental Science,Central China Normal University,Wuhan 430079,China;Research Institute for Sustainable Development of CCNU,Wuhan 430079,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2018年第16期148-154,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(41401232)
湖北省自然科学基金创新群体项目-<湖北省水土环境变化与健康响应(2016CFA027)>
关键词
光谱分析
作物
叶绿素
高光谱
特征波长变量
迭代和保留信息变量法
大豆
SPAD
spectrum analysis
crops
chlorophyll
hyperspectral
characteristic wavelength variables
iteratively retains informative variables
soybean
soil and plant analyzer development
