全球典型植被叶片光谱特征及其对LAI反演的影响分析
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摘要
在全球范围长时间序列LAI遥感产品反演算法中,植被冠层反射率模型仅使用少量叶片光谱特征代表全球植被全年的典型植被光谱特征,叶片光谱的不确定性导致LAI遥感产品存在一定的误差。目前全球已经构建了多个典型植被叶片波谱数据集,这些数据集包含多个植被物种、不同空间地域及多时相叶片光谱数据,为定量分析叶片光谱特征提供了数据支持。主要利用LOPEX’93、ANGERS’03、中国典型地物波谱数据库和野外实测的叶片光谱数据,以黄边参数、红边参数和叶片光谱指数作为分析指标,探讨不同植被物种、不同气候区和不同物候期的叶片光谱特征差异,及其对植被冠层反射率、LAI反演的影响,为发展考虑现实叶片光谱差异的LAI反演算法提供研究基础。结果表明:植被叶片光谱存在多样性,叶片光谱特征差异主要影响MODIS传感器近红外波段和绿波段反射率值,其中,绿波段反射率值对叶片光谱变化最为敏感;在LAI反演算法中,如果只考虑植被类型而不考虑物种叶片光谱差异,可能会给LAI反演带来大于3的误差。
Long time series LAI remote sensing inversion algorithms use only a few leaves spectra to represent the global leaf spectral characteristics throughout the year.while due to the variation of leaf spectra,it may introduce uncertainties to LAI remote sensing products.An amount of spectrum databases containing leaf spectrum of different vegetation species,geographical locations and time phase and corresponding biochemical parameters have been constructed to provide support for the analysis of spectral characteristics of leaves.This paper mainly uses the leaf spectral database LOPEX'93,ANGERS'03,Spectral library of typical ground objects in China and field experimental data to analyze the effects of spectral characteristics of different plant species and different climate zones on MODIS reflectance of specific channels and further to provide prior information for the development of LAI inversion algorithms with consideration of leaf spetra differences.The result suggests that:There exists diversity in vegetation leaf spectra.The spectral differences mainly affect the reflectance in red and green band(green band is most sensitive to leaf spectra variation).Only considering vegetation types without taking leaf spectral variation into account may induce error over 3 in remote sensing LAI inversion algorithms.
Long time series LAI remote sensing inversion algorithms use only a few leaves spectra to represent the global leaf spectral characteristics throughout the year.while due to the variation of leaf spectra,it may introduce uncertainties to LAI remote sensing products.An amount of spectrum databases containing leaf spectrum of different vegetation species,geographical locations and time phase and corresponding biochemical parameters have been constructed to provide support for the analysis of spectral characteristics of leaves.This paper mainly uses the leaf spectral database LOPEX'93,ANGERS'03,Spectral library of typical ground objects in China and field experimental data to analyze the effects of spectral characteristics of different plant species and different climate zones on MODIS reflectance of specific channels and further to provide prior information for the development of LAI inversion algorithms with consideration of leaf spetra differences.The result suggests that:There exists diversity in vegetation leaf spectra.The spectral differences mainly affect the reflectance in red and green band(green band is most sensitive to leaf spectra variation).Only considering vegetation types without taking leaf spectral variation into account may induce error over 3 in remote sensing LAI inversion algorithms.
引文
[1] Chen J M,Cihlar J.Retrieving Leaf Area Index for Boreal Conifer Forests Using Landsat TM Images[J].Remote Sensing of Environment,1996,55(2):153-162.
[2] Myneni R B,Hoffman S,Knyazikhin Y,et al.Global Products of Vegetation Leaf Area and Fraction Absorbed PAR from Year One of MODIS Data[J].Remote Sensing of Environment,2002,83(1):214-231.
[3] Baret F,Hagolle O,Geiger B,et al.LAI,FAPAR and FCOVER CYCLOPES Global Products derived from Vegetation-part 1:Principles of the Algorithm[J].Remote Sensing of Environment,2007,110(3):275-286.
[4] Baret F,Weiss M,Lacaze R,et al.GEOV1:LAI and FAPAR Essential Climate Variables and FCOVER Global Time Series Capitalizing over Existing Products.Part1:Principles of Development and Production[J].Remote Sensing of Environment,2013,137:299-309.
[5] Xiao Z Q,Liang S L,Wang J D,et al.Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time-series MODIS Surface Reflectance[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(1):209-223.
[6] Zhao Jing.Leaf Area Index Inversion Method Combined with Multiple Sensors[D].Beijing:University of Chinese Academy of Sciences,2013[赵静.基于多源遥感数据叶面积指数反演方法研究[D].北京:中国科学院大学,2013.]
[7] Liang Shunlin,Li Xiaowen,Wang Jindi.Quantitative Remote Sensing:Concept and Algorithm[M].Beijing:Science Press.[梁顺林,李小文,王锦地.定量遥感:理念与算法[M].北京:科学出版社,2013.]
[8] Xu Xiru.Remote Sensing Physics[M].Beijing:Peking University Press,2005.[徐希孺.遥感物理[M].北京:北京大学出版社,2005.]
[9] Hosgood B,Jacquemoud S,Andreoli G,et al.Schmuck G.Leaf Optical Properties EXperiment 93 (LOPEX93)[EB/OL].European Commission:Joint Research Centre,Ispra (Italy) (1994) EUR 16095 EN,20 pp.[http://www-gvm.jrc.it/stars/lopex.htm]
[10] Gitelson A A,Merzlyak M N.Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves[J].Advances in Space Research,1998,22(5):689-692.
[11] Wangjindi,Zhang Lixin,Liu Qinhuo,et al.Spectral Database of Typical Objects in China[M].Beijing:Science Press,2009.[王锦地,张立新,柳钦火,等.中国典型地物波谱知识库[M].北京:科学出版社,2009.]
[12] Gao Yanhua,Chen Liangfu,Zhou Xu,et al.An Ideal Band Analysis of Chlorophyll Content of Mixed Vegetation was Estimated[J].Joumal of Remote Sensing,2009,13(4):623-630[高彦华,陈良富,周旭,等.估算混合植被叶绿素含量的理想波段分析[J].遥感学报,2009,13(4):623-630.]
[13] Collins W,Raines G L,Canney F C.Airborne Spectroradiometer Discrimination of Vegetation Anomalies over Sulphide Mineralisation:A Remote Sensing Technique[J].Geological Society of America,Seattle,Washington,1977(11):932-933.
[14] Collins W.Remote Sensing of Crop Type and Maturity[J].Photogrammetric Engineering and Remote Sensing,1978,44:43-55.
[15] Horler D N H,Dockray M,Barber J.The Red Edge of Plant Leaf Reflectance[J].International Journal of Remote Sensing,1983,4(2):273-288.
[16] Zou Hongyu,Zheng Hongping.The Effect and Method of Quantitative Analysis of “Red Edge” of Vegetation[J].Remote Sensing Information,2010(4):112-116.[邹红玉,郑红平.浅述植被“红边”效应及其定量分析方法[J].遥感信息,2010(4):112-116.]
[17] Wang Yuanyuan,Chen Yunhao,Li Jing,et al.Two New Red Edge Indices Disease Severity as Indicators for Stripe Rust of Winter Wheat[J].Remote sensing,2007,11(6):875-881.[王圆圆,陈云浩,李京,等.指示冬小麦条锈病严重度的两个新的红边参数[J].遥感学报,2007,11(6):875-881.]
[18] Li Xiangyang,Liu Guoshun,Shi Zhou,et al.Predicting Leaf Maturity of Flue-cured Tobacco Using Red Edge Characteristics of Laboratory Spectrometry[J].Remote Sensing,2007,11(2):269-275.[李向阳,刘国顺,史舟,等.利用室内光谱红边参数估测烤烟叶片成熟度[J].遥感学报,2007,11(2):269-275.]
[19] Jiang Jinbao,Chen Yunhao,Huang Wenjiang.Using the Distance between Hyperspectral Red Edge Position and Yellow Edge Position to Identify Wheat Yellow Rust Disease[J].Spectroscopy and Spectral Analysis,2010,30(6):1614-1618.[蒋金豹,陈云浩,黄文江.利用高光谱红边与黄边位置距离识别小麦条绣病[J].光谱学与光谱分析,2010,30(6):1614-1618.]
[20] Adams M L,Philpot W D,Norvell W A.Yellowness Index:An Application of Spectral Second Derivatives to Estimate Chlorosis of Leaves in Stressed Vegetation[J].International Journal of Remote Sensing,1999,20(18):3663-3675.
[21] Gong P,Pu R,Heald R C.Analysis of in Situ Hyperspectral Data for Nutrient Estimation of Giant Sequoia[J].International Journal of Remote Sensing,2002,23(9):1827-1850.
[22] Gong Zhaoning,Zhao Yali,Zhao Wenji,et al.Estimation Model for Plant Leaf Chlorophyll Content based on the Spectral Index Content[J].Acta Ecologica Sinica,2014,34(20):9-15.[宫兆宁,赵雅莉,赵文吉,等.基于光谱指数的植被叶片叶绿素含量的估值模拟[J].生态学报,2014,34(20):9-15.]
[23] Fang M H,Ju W M,Zhan W F.A New Spectral Similarity Water Index for the Estimation of Leaf Water Content from Hyperspectral Data of Leaves[J].Remote Sensing of Environment,2017(4):13-27.
[24] Luo Dan,Chang Qingrui,Qi Yanbing,et al.Estimation Model for Chlorophyll Content in Winter Wheat Canopy based on Spectral Indices[J].Journal of Triticeae Crops,2016,36(9):1225-1233.[罗丹,常庆瑞,齐雁冰,等.基于光谱指数的冬小麦冠层叶绿素含量估算模型研究[J].麦类作物学报,2016,36(9):1225-1233.]
[25] Verhoef W,Li J,Qing X,et al.Unified Optical-thermal Four-stream Radiative Transfer Theory for Homogeneous Vegetation Canopies[J].IEEE Transactions on Geoscience and Remote Sensing,2007(45):1808-1822.
[26] Baret F,Champion I,Guyot G,et al.Monitoring Wheat Canopies with a High Spectral Resolution Radiometer[J].Remote Sensing of Environment,1987,22(3):367-378.
[27] Feng Y,Miller J R.Vegetation Green Reflectance at High Spectral Resolution as a Measure of Leaf Chlorophyll Content[C]//Proceedings of the 14th Canadian Symposium on Remote Sensing.Calgary Alberta,1991:351-355
[28] Zhao Yingshi.Principle and Method of Remote Sensing Application Analysis[M].Beijing:Science Press,2003.[赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003]
[29] Liang Shouzhen,Shi Ping,Ma Wandong,et al.Relational Analysis of Spectral and Red-edge Characteristics of Plant Leaf and Leaf Biochemical Constituent[J].Chinese Journal of Eco-Agriculture,2010,18(4):804-809.[梁守真,施平,马万栋,等.植被叶片光谱及红边特征与叶片生化组分关系的分析.中国生态农业学报,2010,18(4):804-809.]
[30] Peňuelas J,Inoue Y.Reflectance Indices Indicative of Changes in Water and Pigment Contents of Peanut and Wheat Leaves[J].Photosynthetica,1999,36(3):355-360.
[31] Song Yingbo.Predicting Model of Soybean Leaf Nitrogen Content by Leaf Reflectance Spectra Under Different Nitrogen Supply Levels[J].Soybean Science,2010,8(29):641-644.[宋英博.不同施肥水平下大豆反射光谱预测叶片氮含量模型[J].大豆科学,2010,08(29):641-644.]
[32] Verhoef W.Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling:The SAIL Model[J].Remote Sensing of Environment,1984,16(2):125-141.
[33] Verhoef W,Jia L,Xiao Q,et al.Unified Optical-thermal Four-stream Radiative Transfer Theory for Homogeneous Vegetation Canopies[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(6):1808-1822.
[34] Myneni R B,Knyazikhin Y,Zhang Y,et al.MODIS Leaf Area Index (LAI) And Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product[J].Transactions of the Institute of Electronics Information & Communication Engineers B,1999,84:902-911.
[35] Shabanov N V,Huang D,Yang W,et al.Analysis and Optimization of the MODIS Leaf Area Index Algorithm Retrievals over Broadleaf Forests[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(8):1855-1865.
[2] Myneni R B,Hoffman S,Knyazikhin Y,et al.Global Products of Vegetation Leaf Area and Fraction Absorbed PAR from Year One of MODIS Data[J].Remote Sensing of Environment,2002,83(1):214-231.
[3] Baret F,Hagolle O,Geiger B,et al.LAI,FAPAR and FCOVER CYCLOPES Global Products derived from Vegetation-part 1:Principles of the Algorithm[J].Remote Sensing of Environment,2007,110(3):275-286.
[4] Baret F,Weiss M,Lacaze R,et al.GEOV1:LAI and FAPAR Essential Climate Variables and FCOVER Global Time Series Capitalizing over Existing Products.Part1:Principles of Development and Production[J].Remote Sensing of Environment,2013,137:299-309.
[5] Xiao Z Q,Liang S L,Wang J D,et al.Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time-series MODIS Surface Reflectance[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(1):209-223.
[6] Zhao Jing.Leaf Area Index Inversion Method Combined with Multiple Sensors[D].Beijing:University of Chinese Academy of Sciences,2013[赵静.基于多源遥感数据叶面积指数反演方法研究[D].北京:中国科学院大学,2013.]
[7] Liang Shunlin,Li Xiaowen,Wang Jindi.Quantitative Remote Sensing:Concept and Algorithm[M].Beijing:Science Press.[梁顺林,李小文,王锦地.定量遥感:理念与算法[M].北京:科学出版社,2013.]
[8] Xu Xiru.Remote Sensing Physics[M].Beijing:Peking University Press,2005.[徐希孺.遥感物理[M].北京:北京大学出版社,2005.]
[9] Hosgood B,Jacquemoud S,Andreoli G,et al.Schmuck G.Leaf Optical Properties EXperiment 93 (LOPEX93)[EB/OL].European Commission:Joint Research Centre,Ispra (Italy) (1994) EUR 16095 EN,20 pp.[http://www-gvm.jrc.it/stars/lopex.htm]
[10] Gitelson A A,Merzlyak M N.Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves[J].Advances in Space Research,1998,22(5):689-692.
[11] Wangjindi,Zhang Lixin,Liu Qinhuo,et al.Spectral Database of Typical Objects in China[M].Beijing:Science Press,2009.[王锦地,张立新,柳钦火,等.中国典型地物波谱知识库[M].北京:科学出版社,2009.]
[12] Gao Yanhua,Chen Liangfu,Zhou Xu,et al.An Ideal Band Analysis of Chlorophyll Content of Mixed Vegetation was Estimated[J].Joumal of Remote Sensing,2009,13(4):623-630[高彦华,陈良富,周旭,等.估算混合植被叶绿素含量的理想波段分析[J].遥感学报,2009,13(4):623-630.]
[13] Collins W,Raines G L,Canney F C.Airborne Spectroradiometer Discrimination of Vegetation Anomalies over Sulphide Mineralisation:A Remote Sensing Technique[J].Geological Society of America,Seattle,Washington,1977(11):932-933.
[14] Collins W.Remote Sensing of Crop Type and Maturity[J].Photogrammetric Engineering and Remote Sensing,1978,44:43-55.
[15] Horler D N H,Dockray M,Barber J.The Red Edge of Plant Leaf Reflectance[J].International Journal of Remote Sensing,1983,4(2):273-288.
[16] Zou Hongyu,Zheng Hongping.The Effect and Method of Quantitative Analysis of “Red Edge” of Vegetation[J].Remote Sensing Information,2010(4):112-116.[邹红玉,郑红平.浅述植被“红边”效应及其定量分析方法[J].遥感信息,2010(4):112-116.]
[17] Wang Yuanyuan,Chen Yunhao,Li Jing,et al.Two New Red Edge Indices Disease Severity as Indicators for Stripe Rust of Winter Wheat[J].Remote sensing,2007,11(6):875-881.[王圆圆,陈云浩,李京,等.指示冬小麦条锈病严重度的两个新的红边参数[J].遥感学报,2007,11(6):875-881.]
[18] Li Xiangyang,Liu Guoshun,Shi Zhou,et al.Predicting Leaf Maturity of Flue-cured Tobacco Using Red Edge Characteristics of Laboratory Spectrometry[J].Remote Sensing,2007,11(2):269-275.[李向阳,刘国顺,史舟,等.利用室内光谱红边参数估测烤烟叶片成熟度[J].遥感学报,2007,11(2):269-275.]
[19] Jiang Jinbao,Chen Yunhao,Huang Wenjiang.Using the Distance between Hyperspectral Red Edge Position and Yellow Edge Position to Identify Wheat Yellow Rust Disease[J].Spectroscopy and Spectral Analysis,2010,30(6):1614-1618.[蒋金豹,陈云浩,黄文江.利用高光谱红边与黄边位置距离识别小麦条绣病[J].光谱学与光谱分析,2010,30(6):1614-1618.]
[20] Adams M L,Philpot W D,Norvell W A.Yellowness Index:An Application of Spectral Second Derivatives to Estimate Chlorosis of Leaves in Stressed Vegetation[J].International Journal of Remote Sensing,1999,20(18):3663-3675.
[21] Gong P,Pu R,Heald R C.Analysis of in Situ Hyperspectral Data for Nutrient Estimation of Giant Sequoia[J].International Journal of Remote Sensing,2002,23(9):1827-1850.
[22] Gong Zhaoning,Zhao Yali,Zhao Wenji,et al.Estimation Model for Plant Leaf Chlorophyll Content based on the Spectral Index Content[J].Acta Ecologica Sinica,2014,34(20):9-15.[宫兆宁,赵雅莉,赵文吉,等.基于光谱指数的植被叶片叶绿素含量的估值模拟[J].生态学报,2014,34(20):9-15.]
[23] Fang M H,Ju W M,Zhan W F.A New Spectral Similarity Water Index for the Estimation of Leaf Water Content from Hyperspectral Data of Leaves[J].Remote Sensing of Environment,2017(4):13-27.
[24] Luo Dan,Chang Qingrui,Qi Yanbing,et al.Estimation Model for Chlorophyll Content in Winter Wheat Canopy based on Spectral Indices[J].Journal of Triticeae Crops,2016,36(9):1225-1233.[罗丹,常庆瑞,齐雁冰,等.基于光谱指数的冬小麦冠层叶绿素含量估算模型研究[J].麦类作物学报,2016,36(9):1225-1233.]
[25] Verhoef W,Li J,Qing X,et al.Unified Optical-thermal Four-stream Radiative Transfer Theory for Homogeneous Vegetation Canopies[J].IEEE Transactions on Geoscience and Remote Sensing,2007(45):1808-1822.
[26] Baret F,Champion I,Guyot G,et al.Monitoring Wheat Canopies with a High Spectral Resolution Radiometer[J].Remote Sensing of Environment,1987,22(3):367-378.
[27] Feng Y,Miller J R.Vegetation Green Reflectance at High Spectral Resolution as a Measure of Leaf Chlorophyll Content[C]//Proceedings of the 14th Canadian Symposium on Remote Sensing.Calgary Alberta,1991:351-355
[28] Zhao Yingshi.Principle and Method of Remote Sensing Application Analysis[M].Beijing:Science Press,2003.[赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003]
[29] Liang Shouzhen,Shi Ping,Ma Wandong,et al.Relational Analysis of Spectral and Red-edge Characteristics of Plant Leaf and Leaf Biochemical Constituent[J].Chinese Journal of Eco-Agriculture,2010,18(4):804-809.[梁守真,施平,马万栋,等.植被叶片光谱及红边特征与叶片生化组分关系的分析.中国生态农业学报,2010,18(4):804-809.]
[30] Peňuelas J,Inoue Y.Reflectance Indices Indicative of Changes in Water and Pigment Contents of Peanut and Wheat Leaves[J].Photosynthetica,1999,36(3):355-360.
[31] Song Yingbo.Predicting Model of Soybean Leaf Nitrogen Content by Leaf Reflectance Spectra Under Different Nitrogen Supply Levels[J].Soybean Science,2010,8(29):641-644.[宋英博.不同施肥水平下大豆反射光谱预测叶片氮含量模型[J].大豆科学,2010,08(29):641-644.]
[32] Verhoef W.Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling:The SAIL Model[J].Remote Sensing of Environment,1984,16(2):125-141.
[33] Verhoef W,Jia L,Xiao Q,et al.Unified Optical-thermal Four-stream Radiative Transfer Theory for Homogeneous Vegetation Canopies[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(6):1808-1822.
[34] Myneni R B,Knyazikhin Y,Zhang Y,et al.MODIS Leaf Area Index (LAI) And Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product[J].Transactions of the Institute of Electronics Information & Communication Engineers B,1999,84:902-911.
[35] Shabanov N V,Huang D,Yang W,et al.Analysis and Optimization of the MODIS Leaf Area Index Algorithm Retrievals over Broadleaf Forests[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(8):1855-1865.