田野成像光谱仪中小麦叶绿素含量模型研究
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摘要
本文以国家高技术研究发展计划(863)“田间作物信息成像光谱仪的研制与应用”(2007AA10Z202)、“基于国产旋翼无人机的农业低空高光谱遥感技术”(2010AA10Z201)为研究背景,采用自主研制的“Offner田野成像光谱仪”,介绍了成像光谱分析的方法及流程,对大田冬小麦叶片进行成像光谱采集实验,并结合实验数据进行小麦叶绿素定量分析研究。论文主要内容包括以下几部分:
一、成像光谱技术用于小麦叶绿素含量无损检测的意义,成像光谱技术的发展历史,成像光谱技术在精准农业领域的应用。
二、成像光谱分析的光谱预处理方法,最优波长选择方法,常用的多元校正算法原理。
三、基于Offner田野成像光谱仪的大田冬小麦叶片成像光谱实验。介绍了实验设计方案,成像光谱仪的主要技术参数,实际田间操作成像光谱系统获取小麦叶片光谱图像的采集、处理方法。
四、基于成像光谱数据,建立了叶绿素a、叶绿素b、叶绿素总量、SPAD的偏最小二乘法回归模型。比较不同光谱预处理方法对模型的影响,从提高建模效率、减少建模变量的角度,研究了连续投影算法在高光谱建模中的作用,并与光谱全波段建立的偏最小二乘回归模型做了比较分析。
五、基于成像光谱数据的叶绿素检测模块的设计与实现。模块包含了成像光谱定量分析流程中的基本数据处理和分析方法。使用这个叶绿素检测工具模块,将使高光谱数据分析处理更加方便、准确,为从高光谱数据中提取信息提供了一个快速、便捷的基础性工具。
六、本文工作的创新点是使用Offner田野成像光谱仪对田间冬小麦进行地面观测实验,获取冬小麦的图谱信息,研究了成像光谱仪的定量分析方法。设计并实现了Offner田野成像光谱仪的叶绿素检测模块。
By using the field Offner imaging spectrometer which developed by the National High Technology Research and Development Program (863)“Development and application of information field crops imaging spectrometer”(2007AA10Z202)and“Agriculture based on domestic rotary wing UAV using low-altitude hyperspectral remote sensing technology”(2010AA10Z201) to collect the spectral and spatial information of winter wheat leaves. First describes the process of imaging spectroscopy, and then studies quantitative analysis of the chlorophyll content in wheat leaves. The main works are as follows:
(1) Describes the meaning of imaging spectrometer technology for nondestructive testing of wheat chlorophyll content, history of imaging spectroscopy, and its application in agriculture.
(2) Introduces the preprocessing methods, the optimal wavelength selection methods, and the commonly used multivariate calibration algorithm principle, and their advantages and disadvantages.
(3) The experiments for field imaging spectroscopy of winter wheat leaves based on the field Offner imaging spectrometer. Introduces the design of experiments and specifications of the imaging spectrometer, actual operation of imaging spectrometer system for acquiring the spectral image of wheat leaves, and data processing method.
(4) Based on the imaging spectral data, the partial least squares regression models of the chlorophyll a, chlorophyll b, total chlorophyll, and SPAD are established. Discusses the effect of different spectral preprocessing methods on the accuracy of the emergent model. To improve modeling efficiency and reduce modeling variables, successive projection method is introduced as the preprocessing method in hyper-spectral modeling, and compared with partial least squares regression model of full-band spectral set.
(5) Design and implementation of the chlorophyll detection module, which contains the basic data processing and analysis methods in basic quantitative analysis process. The module in Chlorophyll detection will enable hyper-spectral data analysis more convenient and accurate, and provides a quick and convenient tool for extracting information from hyper-spectral data.
(6) Innovation of this work is to use the field Offner imaging spectrometer for ground observations of winter wheat in the field experiment, get hyperspectral images information of winter wheat and study the quantitative analysis of imaging spectrometer method. Design and implementation of chlorophyll content detection module based on the field Offner imaging spectrometer.
一、成像光谱技术用于小麦叶绿素含量无损检测的意义,成像光谱技术的发展历史,成像光谱技术在精准农业领域的应用。
二、成像光谱分析的光谱预处理方法,最优波长选择方法,常用的多元校正算法原理。
三、基于Offner田野成像光谱仪的大田冬小麦叶片成像光谱实验。介绍了实验设计方案,成像光谱仪的主要技术参数,实际田间操作成像光谱系统获取小麦叶片光谱图像的采集、处理方法。
四、基于成像光谱数据,建立了叶绿素a、叶绿素b、叶绿素总量、SPAD的偏最小二乘法回归模型。比较不同光谱预处理方法对模型的影响,从提高建模效率、减少建模变量的角度,研究了连续投影算法在高光谱建模中的作用,并与光谱全波段建立的偏最小二乘回归模型做了比较分析。
五、基于成像光谱数据的叶绿素检测模块的设计与实现。模块包含了成像光谱定量分析流程中的基本数据处理和分析方法。使用这个叶绿素检测工具模块,将使高光谱数据分析处理更加方便、准确,为从高光谱数据中提取信息提供了一个快速、便捷的基础性工具。
六、本文工作的创新点是使用Offner田野成像光谱仪对田间冬小麦进行地面观测实验,获取冬小麦的图谱信息,研究了成像光谱仪的定量分析方法。设计并实现了Offner田野成像光谱仪的叶绿素检测模块。
By using the field Offner imaging spectrometer which developed by the National High Technology Research and Development Program (863)“Development and application of information field crops imaging spectrometer”(2007AA10Z202)and“Agriculture based on domestic rotary wing UAV using low-altitude hyperspectral remote sensing technology”(2010AA10Z201) to collect the spectral and spatial information of winter wheat leaves. First describes the process of imaging spectroscopy, and then studies quantitative analysis of the chlorophyll content in wheat leaves. The main works are as follows:
(1) Describes the meaning of imaging spectrometer technology for nondestructive testing of wheat chlorophyll content, history of imaging spectroscopy, and its application in agriculture.
(2) Introduces the preprocessing methods, the optimal wavelength selection methods, and the commonly used multivariate calibration algorithm principle, and their advantages and disadvantages.
(3) The experiments for field imaging spectroscopy of winter wheat leaves based on the field Offner imaging spectrometer. Introduces the design of experiments and specifications of the imaging spectrometer, actual operation of imaging spectrometer system for acquiring the spectral image of wheat leaves, and data processing method.
(4) Based on the imaging spectral data, the partial least squares regression models of the chlorophyll a, chlorophyll b, total chlorophyll, and SPAD are established. Discusses the effect of different spectral preprocessing methods on the accuracy of the emergent model. To improve modeling efficiency and reduce modeling variables, successive projection method is introduced as the preprocessing method in hyper-spectral modeling, and compared with partial least squares regression model of full-band spectral set.
(5) Design and implementation of the chlorophyll detection module, which contains the basic data processing and analysis methods in basic quantitative analysis process. The module in Chlorophyll detection will enable hyper-spectral data analysis more convenient and accurate, and provides a quick and convenient tool for extracting information from hyper-spectral data.
(6) Innovation of this work is to use the field Offner imaging spectrometer for ground observations of winter wheat in the field experiment, get hyperspectral images information of winter wheat and study the quantitative analysis of imaging spectrometer method. Design and implementation of chlorophyll content detection module based on the field Offner imaging spectrometer.
引文
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谭海珍,李少坤,王克如等. 2008.基于成像光谱仪的冬小麦苗期冠层叶绿素密度监测[J].作物学报, 34(10):1812-1817.
王伟,彭彦昆,张晓莉. 2010.基于高光谱成像的生鲜猪肉细菌总数预测建模方法研究[J].光谱学与光谱分析, 30(2): 411-415.
吴文铭. 2009.紫外可见分光光度计及其应用[J].生命科学仪器, 7(4): 61-63.
王纪华,黄文江,劳彩莲等. 2007.运用PLS算法由小麦冠层反射光谱反演氮素垂直分布[J].光谱学与光谱分析, 27(7):1319-1322.
吴传庆,童庆禧,郑兰芬. 2005.地面/图像光谱数据的预处理[J].遥感技术与应用, 20(5):506-511.
王惠文. 1999.偏最小二乘回归方法及其应用[M].北京:国防工业出版社.
袁艳,李立英,熊望娥等. 2009.环境减灾-1A卫星超光谱成像仪结构设计[J].航天器工程, 18(6): 97-105.
周全,吴刚,王秋平等. 2010.高分辨率推扫式成像光谱仪的设计及其田野应用[C].中国控制会议.
赵葆常,杨建峰,常凌颖等. 2009.嫦娥一号卫星成像光谱仪光学系统设计与在轨评估[J].光子学报, 38(3):479-483.
张磊,董超华,张文建等. 2008. METOP星载干涉式超高光谱分辨率红外大气探测仪(IASI)及其产品[J].气象技术, 36(5):639-642.
郑兰芬,王晋年. 1992.成像光谱遥感技术及其图像光谱信息提取的分析研究[J].环境遥感, 7(1):49-58.
Arnon DI. 1949. Copper enzymes in isolated chloroplasts, polyphenoloxidase in Beta vulgaris[J]. Plant Physiology, 24(1):1-5.
Ariana D, Lu R. 2008. Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging. Part 1[J]. Development of a prototype. Sensing and Instrumentation for Food Quality and Safety, 2:144-151.
Araújo MCU, Saldanha TCB, Galváo RKH, et al. 2001.The successive projections algorithm for variable selection in spectroscopic multicomponent analysis[J]. Chemometrics and intelligent laboratory systems, 57:65-73.
Barnes RJ, Dhanoa MS, Lister SJ. 1989. Standard normal variate transformation and detrending of near-infrared diffuse reflectance spectra[J]. Applied Spectroscopy, 43(05):772-777.
Daughtry CS, Walthall CL, Kim MS, et al. 2000. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance[J]. Remote Sensing of environment, 74:229-239.
Datt B. 1999. Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves[J]. International Journal of Remote Sensing, 20(14): 2741-2759.
Elmasry G, Wang N, Elsayed A, et a1. 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry[J]. Journal of Food Engineering, 8l(1):98-107.
Galváo RKH, Araujo MCU, Silva EC, et al. 2007. Cross validation for the selection of spectral variables using the successive projections algorithm[J]. Braz. Chem. Soc,18(8):1580-1584.
Goetz AFH.2009. Three decades of hyperspectral remote sensing of the Earth: A personal viewSession on the State of Science of Environmental Applications of Imaging Spectroscopy held in honor of Alexander FH Goetz[J]. REMOTE SENSING OF ENVIRONMENT, 113(1): 5-16.
Goetz AFH, Vane G, Solomon JE et al. 1985. Imaging spectrometry for earth remote sensing[M]. Science, 228:1147-1153.
Goetz AFH, Rowan LC. 1981. Geological remote sensing[M].Science ,211:781-791.
Gitetlson AA, Gritz Y, Merzlyak MN. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves[J]. Journal of Plant Physiology. 160: 271-282.
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高洪智,卢启鹏,丁海泉等. 2009.基于连续投影算法的土壤总氮近红外特征波长的选取[J].光谱学与光谱分析, 29(11):2951-2954.
洪添胜,乔军, Wang Ning等. 2007.基于高光谱图像技术的雪花梨品质无损检测[J].农业工程学报, 23 (2) :151- 155.
谷筱玉,徐可欣,汪曣. 2006.波长选择算法在近红外光谱法中药有效成分测量中的应用[J].光谱学与光谱分析, 26(9):1618-1620.
黄文江,王秋平,马智宏等. 2009.农业田间成像光谱仪的研制与应用[C],第七届成像光谱技术与应用研讨会.
黄文江,张东彦,马智宏等. 2010.自主研制的田间成像光谱仪农学建模研究[C],第八届成像光谱技术与应用研讨会暨交叉学科论坛.
李江波,苏忆楠,饶秀勤. 2010.基于高光谱成像及神经网络技术检测玉米含水率[J].包装与食品机械, 28(6): 001-4.
陆婉珍,袁洪福,徐广通. 2000.现代近红外光谱分析技术[M].北京:中国石化出版社.
潘明忠,亓洪兴,肖功海等. 2010.便携式地面成像光谱辐射计的设计[J].红外, 31(1): 1-7.
浦瑞良,宫鹏. 2000.高光谱遥感及其应用[M].北京:高等教育出版社.
宋开山,张柏,王宗明等. 2006.大豆叶绿素含量高光谱反演模型研究[J].农业工程学报, 22 (8):16-21.
邵学广,蔡文生. 2005.化学信息学[M].北京:科学出版社.
M.奥托著,邵学广等译.化学计量学:统计学与计算机在分析化学中的应用[M].北京:科学出版社, 2003:101-103.
童庆禧,薛永祺,王晋年等. 2010.地面成像光谱辐射测量系统及其应用[J].遥感学报, 14(3):000-000.
谭海珍,李少坤,王克如等. 2008.基于成像光谱仪的冬小麦苗期冠层叶绿素密度监测[J].作物学报, 34(10):1812-1817.
王伟,彭彦昆,张晓莉. 2010.基于高光谱成像的生鲜猪肉细菌总数预测建模方法研究[J].光谱学与光谱分析, 30(2): 411-415.
吴文铭. 2009.紫外可见分光光度计及其应用[J].生命科学仪器, 7(4): 61-63.
王纪华,黄文江,劳彩莲等. 2007.运用PLS算法由小麦冠层反射光谱反演氮素垂直分布[J].光谱学与光谱分析, 27(7):1319-1322.
吴传庆,童庆禧,郑兰芬. 2005.地面/图像光谱数据的预处理[J].遥感技术与应用, 20(5):506-511.
王惠文. 1999.偏最小二乘回归方法及其应用[M].北京:国防工业出版社.
袁艳,李立英,熊望娥等. 2009.环境减灾-1A卫星超光谱成像仪结构设计[J].航天器工程, 18(6): 97-105.
周全,吴刚,王秋平等. 2010.高分辨率推扫式成像光谱仪的设计及其田野应用[C].中国控制会议.
赵葆常,杨建峰,常凌颖等. 2009.嫦娥一号卫星成像光谱仪光学系统设计与在轨评估[J].光子学报, 38(3):479-483.
张磊,董超华,张文建等. 2008. METOP星载干涉式超高光谱分辨率红外大气探测仪(IASI)及其产品[J].气象技术, 36(5):639-642.
郑兰芬,王晋年. 1992.成像光谱遥感技术及其图像光谱信息提取的分析研究[J].环境遥感, 7(1):49-58.
Arnon DI. 1949. Copper enzymes in isolated chloroplasts, polyphenoloxidase in Beta vulgaris[J]. Plant Physiology, 24(1):1-5.
Ariana D, Lu R. 2008. Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging. Part 1[J]. Development of a prototype. Sensing and Instrumentation for Food Quality and Safety, 2:144-151.
Araújo MCU, Saldanha TCB, Galváo RKH, et al. 2001.The successive projections algorithm for variable selection in spectroscopic multicomponent analysis[J]. Chemometrics and intelligent laboratory systems, 57:65-73.
Barnes RJ, Dhanoa MS, Lister SJ. 1989. Standard normal variate transformation and detrending of near-infrared diffuse reflectance spectra[J]. Applied Spectroscopy, 43(05):772-777.
Daughtry CS, Walthall CL, Kim MS, et al. 2000. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance[J]. Remote Sensing of environment, 74:229-239.
Datt B. 1999. Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves[J]. International Journal of Remote Sensing, 20(14): 2741-2759.
Elmasry G, Wang N, Elsayed A, et a1. 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry[J]. Journal of Food Engineering, 8l(1):98-107.
Galváo RKH, Araujo MCU, Silva EC, et al. 2007. Cross validation for the selection of spectral variables using the successive projections algorithm[J]. Braz. Chem. Soc,18(8):1580-1584.
Goetz AFH.2009. Three decades of hyperspectral remote sensing of the Earth: A personal viewSession on the State of Science of Environmental Applications of Imaging Spectroscopy held in honor of Alexander FH Goetz[J]. REMOTE SENSING OF ENVIRONMENT, 113(1): 5-16.
Goetz AFH, Vane G, Solomon JE et al. 1985. Imaging spectrometry for earth remote sensing[M]. Science, 228:1147-1153.
Goetz AFH, Rowan LC. 1981. Geological remote sensing[M].Science ,211:781-791.
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