甜柚叶绿素含量高光谱无损检测模型
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摘要
【目的】为监测甜柚Citrus maxima果树生长健康状况及预测甜柚产量,以赣州南康地区一片甜柚果园为研究对象,建立甜柚叶片叶绿素含量检测模型。【方法】使用Field Spec4便携式地物光谱仪和SPAD-502叶绿素仪测定甜柚叶片光谱及SPAD值,分别采用单变量回归、逐步回归及偏最小二乘法(PLS)构建其叶绿素含量高光谱无损检测模型并进行精度检验。【结果】原始光谱在553 nm处、一阶光谱在692和752 nm处的反射率与叶绿素含量相关性最高,这3个波段为甜柚叶片光谱反射率敏感波段;当主成分个数为4时,PLS具有最高的精度,且基于PLS技术所建立的模型较单变量、逐步回归模型精度更好,模型拟合度较高,其决定系数(r2)最高,为0.869,均方根误差(RMSE)和相对误差(RE)最小,分别为3.013和6.82%。对原始光谱、一阶导数光谱及PLS拟合的估测模型进行对比分析显示,PLS模型无论是从建模样本精度还是模型预测能力方面均优于前2种传统模型。【结论】PLS模型适合于利用高光谱数据进行叶绿素含量的估测,可作为甜柚叶绿素含量的最佳无损检测模型。
【Objective】To monitor the health status of fruit trees and predict the yield of sweet pomelo(Citrus maxima), a detection model of chlorophyll content in C. maxima leaves was established in a C. maxima orchard of Nankang, Ganzhou.【Method】The leaf spectrum and SPAD value of C. maxima were measured using Field Spec4 portable earth spectrometer and SPAD-502 chlorophyll meter. The hyperspectral nondestructive detection model of chlorophyll content was constructed by single variable regression, stepwise regression and partial least squares(PLS) method, and the accuracy was examined.【Result】The reflectances of original spectrum at 553 nm and the first order spectra at 692 and 752 nm had the highest correlation with chlorophyll content. These three bands were sensitive bands of spectral reflectance of C. maxima leaves. When the number of principal components was four, PLS had the highest level of precision. PLS model had higher accuracy, fitting degree and determination coefficient(r2=0.869) compared with the single variable and stepwise regression models, and PLS model had the lowest root mean square error(RMSE) being 3.013 and the lowest relative error(RE) being6.82%. Comparing and analyzing the estimation models of original spectrum, first derivative spectrum and PLS fitting, PLS model was superior to the two traditional models in terms of sample precision and prediction ability.【Conclusion】PLS model is suitable for the estimation of chlorophyll content by hyperspectral data,and the best nondestructive detection model for the chlorophyll content of C. maxima.
【Objective】To monitor the health status of fruit trees and predict the yield of sweet pomelo(Citrus maxima), a detection model of chlorophyll content in C. maxima leaves was established in a C. maxima orchard of Nankang, Ganzhou.【Method】The leaf spectrum and SPAD value of C. maxima were measured using Field Spec4 portable earth spectrometer and SPAD-502 chlorophyll meter. The hyperspectral nondestructive detection model of chlorophyll content was constructed by single variable regression, stepwise regression and partial least squares(PLS) method, and the accuracy was examined.【Result】The reflectances of original spectrum at 553 nm and the first order spectra at 692 and 752 nm had the highest correlation with chlorophyll content. These three bands were sensitive bands of spectral reflectance of C. maxima leaves. When the number of principal components was four, PLS had the highest level of precision. PLS model had higher accuracy, fitting degree and determination coefficient(r2=0.869) compared with the single variable and stepwise regression models, and PLS model had the lowest root mean square error(RMSE) being 3.013 and the lowest relative error(RE) being6.82%. Comparing and analyzing the estimation models of original spectrum, first derivative spectrum and PLS fitting, PLS model was superior to the two traditional models in terms of sample precision and prediction ability.【Conclusion】PLS model is suitable for the estimation of chlorophyll content by hyperspectral data,and the best nondestructive detection model for the chlorophyll content of C. maxima.
引文
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[3]郭洋洋,张连蓬,王德高,等.小波分析在植物叶绿素高光谱遥感反演中的应用[J].测绘通报,2010,35(8):31-33.
[4]王静,景元书,黄文江,等.冬小麦条锈病严重度不同估算方法对比研究[J].光谱学与光谱分析,2015,35(6):1649-1653.
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[7]金震宇,田庆久,惠凤鸣,等.水稻叶绿素浓度与光谱反射率关系研究[J].遥感技术与应用,2003,18(3):134-137.
[8]郭啸川,罗丽娟,陈文惠,等.基于连续统去除法的湿地松叶绿素含量高光谱估算模型[J].吉林师范大学学报(自然科学版),2012,33(4):95-101.
[9]王强,易秋香,包安明,等.基于高光谱反射率的棉花冠层叶绿素密度估算[J].农业工程学报,2012,28(15):125-132.
[10]姚付启,张振华,杨润亚,等.基于主成分分析和BP神经网络的法国梧桐叶绿素含量高光谱反演研究[J].测绘科学,2010,35(1):109-112.
[11]岳学军,全东平,洪添胜,等.柑橘叶片叶绿素含量高光谱无损检测模型[J].农业工程学报,2015,31(1):294-302.
[12]姜丽芬,石福臣,王化田,等.叶绿素计SPAD-502在林业上应用[J].生态学杂志,2005,24(12):1543-1548.
[13]DIAN Y Y,LE Y,FANG S H,et al.Influence of spectral bandwidth and position on chlorophyll content retrieval at leaf and canopy levels[J].J Indian Soc Remote Sens,2016,44(4):1-11.
[14]PU R L,GONG P.Hyperspectral remote sensing and its applications[M].Beijing:Higher Education Press,2000:81-97.
[15]姚付启,蔡焕杰,王海江,等.冬小麦冠层高光谱特征与覆盖度相关性研究[J].农业机械学报,2012,43(7):156-162.
[16]谭琨,叶元元,杜培军.基于支持向量机的假酒近红外光谱识别分类研究[J].光子学报,2013,42(1):69-73.
[17]游士兵,严研.逐步回归分析法及其应用[J].统计与决策,2017,20(14):31-35.
[18]罗批,郭继昌,李锵,等.基于偏最小二乘回归建模的探讨[J].天津大学学报(自然科学与工程技术版),2002,35(6):783-786.
[19]刘忠华,李云梅,吕恒,等.基于偏最小二乘法的巢湖悬浮物浓度反演[J].湖泊科学,2011,23(3):357-365.
[20]褚武道,陈文惠,艾金泉,等.基于偏最小二乘法的樟树叶片叶绿素含量高光谱估算[J].福建师范大学学报(自然科学版),2014,30(1):65-70.
[2]OKUDA K,TANIGUCHI K,MIURA M,et al.Application of vegetation isoline equations for simultaneous retrieval of leaf area index and leaf chlorophyll content using reflectance of red edge band[C]//Proceedings Volume 9975:Remote Sensing and Modeling of Ecosystems for Sustainability XIII:99750C.San Diego,California,United States:SPIE Optical Engineering and Ap-plications,2016.doi:10.1117/12.2236811.
[3]郭洋洋,张连蓬,王德高,等.小波分析在植物叶绿素高光谱遥感反演中的应用[J].测绘通报,2010,35(8):31-33.
[4]王静,景元书,黄文江,等.冬小麦条锈病严重度不同估算方法对比研究[J].光谱学与光谱分析,2015,35(6):1649-1653.
[5]李欢.基于高光谱数据的柑桔叶绿素含量估算研究[D].重庆:西南大学,2009.
[6]梁爽,赵庚星,朱西存.苹果树叶片叶绿素含量高光谱估测模型研究[J].光谱学与光谱分析,2012,32(5):1367-1370.
[7]金震宇,田庆久,惠凤鸣,等.水稻叶绿素浓度与光谱反射率关系研究[J].遥感技术与应用,2003,18(3):134-137.
[8]郭啸川,罗丽娟,陈文惠,等.基于连续统去除法的湿地松叶绿素含量高光谱估算模型[J].吉林师范大学学报(自然科学版),2012,33(4):95-101.
[9]王强,易秋香,包安明,等.基于高光谱反射率的棉花冠层叶绿素密度估算[J].农业工程学报,2012,28(15):125-132.
[10]姚付启,张振华,杨润亚,等.基于主成分分析和BP神经网络的法国梧桐叶绿素含量高光谱反演研究[J].测绘科学,2010,35(1):109-112.
[11]岳学军,全东平,洪添胜,等.柑橘叶片叶绿素含量高光谱无损检测模型[J].农业工程学报,2015,31(1):294-302.
[12]姜丽芬,石福臣,王化田,等.叶绿素计SPAD-502在林业上应用[J].生态学杂志,2005,24(12):1543-1548.
[13]DIAN Y Y,LE Y,FANG S H,et al.Influence of spectral bandwidth and position on chlorophyll content retrieval at leaf and canopy levels[J].J Indian Soc Remote Sens,2016,44(4):1-11.
[14]PU R L,GONG P.Hyperspectral remote sensing and its applications[M].Beijing:Higher Education Press,2000:81-97.
[15]姚付启,蔡焕杰,王海江,等.冬小麦冠层高光谱特征与覆盖度相关性研究[J].农业机械学报,2012,43(7):156-162.
[16]谭琨,叶元元,杜培军.基于支持向量机的假酒近红外光谱识别分类研究[J].光子学报,2013,42(1):69-73.
[17]游士兵,严研.逐步回归分析法及其应用[J].统计与决策,2017,20(14):31-35.
[18]罗批,郭继昌,李锵,等.基于偏最小二乘回归建模的探讨[J].天津大学学报(自然科学与工程技术版),2002,35(6):783-786.
[19]刘忠华,李云梅,吕恒,等.基于偏最小二乘法的巢湖悬浮物浓度反演[J].湖泊科学,2011,23(3):357-365.
[20]褚武道,陈文惠,艾金泉,等.基于偏最小二乘法的樟树叶片叶绿素含量高光谱估算[J].福建师范大学学报(自然科学版),2014,30(1):65-70.