南方红壤侵蚀区长汀县不同生态恢复年限下芒萁叶绿素含量的高光谱估算模型
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摘要
芒萁是南方红壤侵蚀区生态恢复重要的地带性草本植物,对生态系统修复具有重要作用,监测其叶绿素含量能有效诊断生长健康状况。本文以福建省长汀县朱溪流域6个不同生态恢复年限下的芒萁叶片高光谱反射数据以及实测叶绿素含量为数据源,借助高光谱遥感技术分析不同恢复年限芒萁叶片原始光谱特征,筛选出光谱敏感波段并构建光谱指数,基于相关性分析,建立芒萁叶绿素单变量以及多元逐步回归模型,并确定最佳估算模型。结果表明:高光谱指数建立的单变量估算模型中,改进红边归一化植被指数(m NDVI_(705))、叶面叶绿素指数(LCI)、红边指数(Vog)、比值光谱指数(RVI_(603/407))、NDVI[603,407]高光谱指数建立的二次模型精度高,建模决定系数R~2均超过了0.8,其中以高光谱指数为自变量建立的多元回归模型拟合R~2值(0.886)最高。综合建模精度和模型验证精度,LCI指数构建的单变量模型以及基于高光谱指数的多元回归模型是估算芒萁叶片叶绿素含量最佳模型。本研究建立的叶绿素高光谱估算模型对快速、无损地监测水保植物芒萁生长具有重要意义。
Dicranopteris dichotoma is an important zonal herbaceous plant for ecological restoration in the eroded red soil areas of southern China, and is an effective control of soil erosion. Using remote sensing techniques to monitor the chlorophyll content can help diagnose the vegetation growth and healthy condition of dicranopteris dichotoma. Based on hyperspectral reflectance data and the corresponding chlorophyll contents of dicranopteris dichotoma leaf from six different ecological restoration stages in Zhuxi small watershed of Changting County, Fujian Province, this study analyzed the hyperspectral curve properties of leaf, transformed the original spectral into the first derivative, and selected the sensitive wavebands to create ratio(RVI) and normalized(NDVI, FDNDVI) hyperspectral indices. Then correlation analysis was conducted for the chlorophyll contents and hyperspectral indices which were selected from reported indices and newly constructed indcies with sensitive wavelengths. Based on the correlation coefficients, we can chose the best indices to create estimation models. The linear, exponential, multiplicative, quadratic polynomial, logarithmic, and multivariate regression models were constructed for comparison. Furthermore, the optimal estimation model was determined by the accuracy of each estimation model. Results showed that the sensitive wavelengths of the original spectral for dicranopteris dichotoma leaf at different ecological restoration stages were 407 nm, 603 nm, and that the optimal wavebands of the first derivative were 463 nm, 554 nm, 674 nm, and 739 nm. The relationship between the chlorophyll content of dicranopteris dichotoma leaf and the hyperspectral indices of red edge position(λr),NDVI[603, 407], Modified Red Edge Normalized Difference Vegetation Index(mNDVI_(705)), Vogelmann Index(Vog) were very significant, and the correlation coefficients were over 0.85. The estimation models of chlorophyll content established by hyperspectral indices of m NDVI_(705), Leaf Chlorophyll Index(LCI), Vog, RVI_(603/407), NDVI[603, 407] showed better test results, and the R~2 were over 0.8. The model established by FDNDVI[739, 463]index had the highest verification accuracy, and the R~2 reached 0.741. The multivariate regression model based on hyperspectral indices got highest test results with the highest R~2. Therefore, the LCI index and the multivariate regression model based on hyperspectral indices have the strongest ability for predicting chlorophyll concentration, which provides scientific basis for dynamic monitoring of dicranopteris dichotoma in the eroded red soil regions of southern China. It is significant for monitoring soil and water conservation plants. Meanwhile,the objective of this research was to provide effective technical support for ecological restoration by building hyperspectral estimation models of chlorophyll content, with a rapid and non-destructive method for monitoring vegetation growth.
Dicranopteris dichotoma is an important zonal herbaceous plant for ecological restoration in the eroded red soil areas of southern China, and is an effective control of soil erosion. Using remote sensing techniques to monitor the chlorophyll content can help diagnose the vegetation growth and healthy condition of dicranopteris dichotoma. Based on hyperspectral reflectance data and the corresponding chlorophyll contents of dicranopteris dichotoma leaf from six different ecological restoration stages in Zhuxi small watershed of Changting County, Fujian Province, this study analyzed the hyperspectral curve properties of leaf, transformed the original spectral into the first derivative, and selected the sensitive wavebands to create ratio(RVI) and normalized(NDVI, FDNDVI) hyperspectral indices. Then correlation analysis was conducted for the chlorophyll contents and hyperspectral indices which were selected from reported indices and newly constructed indcies with sensitive wavelengths. Based on the correlation coefficients, we can chose the best indices to create estimation models. The linear, exponential, multiplicative, quadratic polynomial, logarithmic, and multivariate regression models were constructed for comparison. Furthermore, the optimal estimation model was determined by the accuracy of each estimation model. Results showed that the sensitive wavelengths of the original spectral for dicranopteris dichotoma leaf at different ecological restoration stages were 407 nm, 603 nm, and that the optimal wavebands of the first derivative were 463 nm, 554 nm, 674 nm, and 739 nm. The relationship between the chlorophyll content of dicranopteris dichotoma leaf and the hyperspectral indices of red edge position(λr),NDVI[603, 407], Modified Red Edge Normalized Difference Vegetation Index(mNDVI_(705)), Vogelmann Index(Vog) were very significant, and the correlation coefficients were over 0.85. The estimation models of chlorophyll content established by hyperspectral indices of m NDVI_(705), Leaf Chlorophyll Index(LCI), Vog, RVI_(603/407), NDVI[603, 407] showed better test results, and the R~2 were over 0.8. The model established by FDNDVI[739, 463]index had the highest verification accuracy, and the R~2 reached 0.741. The multivariate regression model based on hyperspectral indices got highest test results with the highest R~2. Therefore, the LCI index and the multivariate regression model based on hyperspectral indices have the strongest ability for predicting chlorophyll concentration, which provides scientific basis for dynamic monitoring of dicranopteris dichotoma in the eroded red soil regions of southern China. It is significant for monitoring soil and water conservation plants. Meanwhile,the objective of this research was to provide effective technical support for ecological restoration by building hyperspectral estimation models of chlorophyll content, with a rapid and non-destructive method for monitoring vegetation growth.
引文
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[2]王会霞,石辉,李秧秧.城市大气环境下绿化植物叶片比叶重和光合色素含量[J].中国环境科学,2011,31(7):1134-1142.[Wang H X, Shi H, Li Y Y. Leaf mass per area and photosynthetic pigments of greening plant species under different urban atmospheric environment[J]. China Environmental Science, 2011,31(7):1134-1142.]
[3] Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression[J]. Remote Sensing of Environment, 2003,86(4):542-553.
[4]刘伟东,项月琴,郑兰芬,等.高光谱数据与水稻叶面积指数及叶绿素密度的相关分析[J].遥感学报,2000,4(4):279-283.[Liu W D, Xang Y Q, Zheng L F, et al. Relationships between rice LAI, CH.D and hyperspectra data[J]. Journal of Remote Sensing, 2000,4(4):279-283.]
[5]王润生.高光谱遥感的物质组分和物质成分反演的应用分析[J].地球信息科学学报,2009,11(3):261-267.[Wang R S. Spectral identification and inversion of composition and component of objects with hyperspectral remote sensing[J]. Journal of Geo-information Science,2009,11(3):261-267.]
[6]彭羽,米凯,覃亚,等.半干旱草原不同生态恢复阶段优势植物的高光谱特征分析[J].光谱学与光谱分析,2014,34(11):3090-3094.[Peng Y, Mi K, Qin Y, et al. Spectral reflectance characteristics of dominant plant species at different eco-restoring stages in the semi-arid grassland[J]. Spectroscopy and Spectral Analysis, 2014,34(11):3090-3094.]
[7]梁亮,杨敏华,张连蓬,等.基于SVR算法的小麦冠层叶绿素含量高光谱反演[J].农业工程学报,2012,28(20):162-171,294.[Liang L, Yang M H, Zhang L P, et al. Chlorophyll content inversion with hyperspectral technology for wheat canopy based on support vector regression algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012,28(20):162-171,294.]
[8]王家强,伍维模,李志军,等.基于高光谱指数的天然胡杨叶绿素遥感建模研究[J].干旱区资源与环境,2014,28(10):95-99.[Wang J Q, Wu W M, Li Z J, et al. Remote sensing modeling research of natural Populus Euphratica chlorophyll based on hyperspectral indices[J]. Journal of Arid Land Resources and Environment, 2014,28(10):95-99.]
[9]艾金泉,陈文惠,陈丽娟,等.冠层水平互花米草叶片光合色素含量的高光谱遥感估算模型[J].生态学报,2015,35(4):1175-1186.[Ai J Q, Chen W H, Chen L J, et al. Hyperspectral remote sensing estimation models for foliar photosynthetic pigment contents at canopy level in an invasive species[J]. Acta Ecologica Sinica, 2015,35(4):1175-1186.]
[10]肖汉,陈秀万,杨振宇,等.基于光谱分析的草地叶绿素含量估测植被指数[J].光谱学与光谱分析,2014,34(11):3075-3078.[Xi H, Chen X W, Yang Z N, et al. Vegetation index estimation by chlorophyll content of grassland based on spectral analysis[J]. Spectroscopy and Spectral Analysis, 2014,34(11):3075-3078.]
[11]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.[Li S, Zhao G X, Zhu X C. Hyperspectral estimation models of chlorophyll content in apple leaves[J]. Spectroscopy and Spectral Analysis, 2012,32(5):1367-1370.]
[12]张永贺,郭啸川,褚武道,等.基于红边位置的木荷叶片叶绿素含量估测模型研究[J].红外与激光工程,2013,42(3):798-804.[Zhang Y H, Guo X C, Chu W D, et al. Estimation model of schima superba leaf chlorophyll content based on red edge position[J]. Infrared and Laser Engineering, 2013,42(3):798-804.]
[13]杜华强,葛宏立,范文义,等.马尾松针叶光谱特征与其叶绿素含量间关系研究[J].光谱学与光谱分析,2009,29(11):3033-3037.[Du H Q, Ge H L, Fan W Y, et al. Study on relationships between total chlorophyll with hyperspectral features for leaves of pinus massoniana forest[J]. Spectroscopy and Spectral Analysis, 2009,29(11):3033-3037.]
[14]孙小涛,周忠发,陈全,等.重点生态功能区水土流失敏感性评价与分布研究——以贵州省雷山县为例[J].水土保持学报,2016,30(6):73-78.[Sun X T, Zhou Z F, Chen Q,et al. Sensitivity evaluation and the spatial distribution of soil erosion in keyecological function areas:A case of Leishan city of Guizhou[J]. Journal of Soil and Water Conservation, 2016,30(6):73-78.]
[15] Chen Z, Chen Z, Bai L, et al. A catastrophe model to assess soil restoration under ecological restoration in the red soil hilly region of china[J]. Pedosphere, 2017,27(4):778-787.
[16] Chen Z, Chen Z, Yan X, et al. Stoichiometric mechanisms of dicranopteris dichotoma, growth and resistance to nutrient limitation in the Zhuxi watershed in the red soil hilly region of China[J]. Plant and Soil, 2016,398(1-2):367-379.
[17]陈志强,陈志彪.南方红壤侵蚀区土壤肥力质量的突变——以福建省长汀县为例[J].生态学报,2013,33(10):3002-3010.[Chen Z Q, Chen Z B. Mutation of soil fertility quality in the red eroded area of southern China:A case study in Changting County, Fujian Province[J]. Acta Ecologica Sinica, 2013,33(10):3002-3010.]
[18]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):65-70,90.[Chu W D, Chen W H, Ai J Q, et al. Hyperspectral estimation of camphor tree leaf chlorophyll content based on partial least squares regression[J]. Journal of Fujian Normal University(Natural Science Edition), 2014,30(1):65-70,90.]
[19]宫兆宁,赵雅莉,赵文吉,等.基于光谱指数的植物叶片叶绿素含量的估算模型[J].生态学报,2014,34(20):5736-5745.[Gong Z N, Zhao Y L, Zhao W J, et al. Estimation model for plant leaf chlorophyll content based on the spectral index content[J]. Acta Ecologica Sinica, 2014,34(20):5736-5745.]
[20]李春昉,郭际,赵绍丰.多元统计分析之因子分析浅析[J].价值工程,2010,29(15):128-129.[Li C F, Guo J, Zhao S F.[The analysis of factor analysis in multivariate statistical analysis[J]. Value Engineering, 2010,29(15):128-129.]
[21] 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
[22]姚付启,张振华,杨润亚,等.基于红边参数的植被叶绿素含量高光谱估算模型[J].农业工程学报,2009,25(S2):123-129.[Yao F Q, Zhang Z H, Yang R Y, et al. Hyperspectral models for estimating vegetation chlorophyll content based on red edge parameter[J]. Transactions of the CSAE, 2009,25(s2):123-129.]
[23] Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing and Environment, 1979,8(2):127-150.
[24] Gitelson A, Merzlyak M N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. spectral features and relation to chlorophyll estimation[J]. Journal of Plant Physiology, 1994,143(3):286-292.
[25] Datt B. A new reflectance index for remote sensing of chlorophyll content in higher plants:Tests using Eucalyptus leaves[J]. Journal of Plant Physiology, 1999,154(1):30-36.
[26] Sims D A, Gamon J A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance:A comparison of indices based on liquid water and chlorophyll absorption features[J]. Remote Sensing of Environment, 2003,84(4):526-537.
[27] Pe?uelas J, Gamon J A, Fredeen A L, et al. Reflectance indices associated with physiological changes in nitrogen and water-limited sunflower leaves[J]. Remote Sensing of Environment, 1994,48(2):135-146.
[28] Merzlyak M N, Chivkunova O B, Gitelson A A, et al.Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening[J]. Physiologia Plantarum, 2010,106(1):135-141.
[29] Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales:An evaluation of some hyperspectral approaches[J]. Remote Sensing of Environment,1998,66(3):273-285.
[30] Vogelmann J E, Rock B N, Moss D M. Red edge spectral measurements from sugar maple leaves[J]. International Journal of Remote Sensing, 1993,14(8):1563-1575.
[31] Haboudane D, Miller J R, Tremblay N, et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture[J]. Remote Sensing of Environment, 2002,81(2-3):416-426.
[32]焦全军,张霞,张兵,等.基于叶片光谱的森林叶绿素浓度反演研究[J].国土资源遥感,2006,18(2):26-30.[Jiao Q J,Zhang X, Zhang B, et al. The petrieval of forest chlorophyll concentration based on foliar spectra[J]. Remote Sensing for Land and Resources, 2006,18(2):26-30.]
[2]王会霞,石辉,李秧秧.城市大气环境下绿化植物叶片比叶重和光合色素含量[J].中国环境科学,2011,31(7):1134-1142.[Wang H X, Shi H, Li Y Y. Leaf mass per area and photosynthetic pigments of greening plant species under different urban atmospheric environment[J]. China Environmental Science, 2011,31(7):1134-1142.]
[3] Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression[J]. Remote Sensing of Environment, 2003,86(4):542-553.
[4]刘伟东,项月琴,郑兰芬,等.高光谱数据与水稻叶面积指数及叶绿素密度的相关分析[J].遥感学报,2000,4(4):279-283.[Liu W D, Xang Y Q, Zheng L F, et al. Relationships between rice LAI, CH.D and hyperspectra data[J]. Journal of Remote Sensing, 2000,4(4):279-283.]
[5]王润生.高光谱遥感的物质组分和物质成分反演的应用分析[J].地球信息科学学报,2009,11(3):261-267.[Wang R S. Spectral identification and inversion of composition and component of objects with hyperspectral remote sensing[J]. Journal of Geo-information Science,2009,11(3):261-267.]
[6]彭羽,米凯,覃亚,等.半干旱草原不同生态恢复阶段优势植物的高光谱特征分析[J].光谱学与光谱分析,2014,34(11):3090-3094.[Peng Y, Mi K, Qin Y, et al. Spectral reflectance characteristics of dominant plant species at different eco-restoring stages in the semi-arid grassland[J]. Spectroscopy and Spectral Analysis, 2014,34(11):3090-3094.]
[7]梁亮,杨敏华,张连蓬,等.基于SVR算法的小麦冠层叶绿素含量高光谱反演[J].农业工程学报,2012,28(20):162-171,294.[Liang L, Yang M H, Zhang L P, et al. Chlorophyll content inversion with hyperspectral technology for wheat canopy based on support vector regression algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012,28(20):162-171,294.]
[8]王家强,伍维模,李志军,等.基于高光谱指数的天然胡杨叶绿素遥感建模研究[J].干旱区资源与环境,2014,28(10):95-99.[Wang J Q, Wu W M, Li Z J, et al. Remote sensing modeling research of natural Populus Euphratica chlorophyll based on hyperspectral indices[J]. Journal of Arid Land Resources and Environment, 2014,28(10):95-99.]
[9]艾金泉,陈文惠,陈丽娟,等.冠层水平互花米草叶片光合色素含量的高光谱遥感估算模型[J].生态学报,2015,35(4):1175-1186.[Ai J Q, Chen W H, Chen L J, et al. Hyperspectral remote sensing estimation models for foliar photosynthetic pigment contents at canopy level in an invasive species[J]. Acta Ecologica Sinica, 2015,35(4):1175-1186.]
[10]肖汉,陈秀万,杨振宇,等.基于光谱分析的草地叶绿素含量估测植被指数[J].光谱学与光谱分析,2014,34(11):3075-3078.[Xi H, Chen X W, Yang Z N, et al. Vegetation index estimation by chlorophyll content of grassland based on spectral analysis[J]. Spectroscopy and Spectral Analysis, 2014,34(11):3075-3078.]
[11]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.[Li S, Zhao G X, Zhu X C. Hyperspectral estimation models of chlorophyll content in apple leaves[J]. Spectroscopy and Spectral Analysis, 2012,32(5):1367-1370.]
[12]张永贺,郭啸川,褚武道,等.基于红边位置的木荷叶片叶绿素含量估测模型研究[J].红外与激光工程,2013,42(3):798-804.[Zhang Y H, Guo X C, Chu W D, et al. Estimation model of schima superba leaf chlorophyll content based on red edge position[J]. Infrared and Laser Engineering, 2013,42(3):798-804.]
[13]杜华强,葛宏立,范文义,等.马尾松针叶光谱特征与其叶绿素含量间关系研究[J].光谱学与光谱分析,2009,29(11):3033-3037.[Du H Q, Ge H L, Fan W Y, et al. Study on relationships between total chlorophyll with hyperspectral features for leaves of pinus massoniana forest[J]. Spectroscopy and Spectral Analysis, 2009,29(11):3033-3037.]
[14]孙小涛,周忠发,陈全,等.重点生态功能区水土流失敏感性评价与分布研究——以贵州省雷山县为例[J].水土保持学报,2016,30(6):73-78.[Sun X T, Zhou Z F, Chen Q,et al. Sensitivity evaluation and the spatial distribution of soil erosion in keyecological function areas:A case of Leishan city of Guizhou[J]. Journal of Soil and Water Conservation, 2016,30(6):73-78.]
[15] Chen Z, Chen Z, Bai L, et al. A catastrophe model to assess soil restoration under ecological restoration in the red soil hilly region of china[J]. Pedosphere, 2017,27(4):778-787.
[16] Chen Z, Chen Z, Yan X, et al. Stoichiometric mechanisms of dicranopteris dichotoma, growth and resistance to nutrient limitation in the Zhuxi watershed in the red soil hilly region of China[J]. Plant and Soil, 2016,398(1-2):367-379.
[17]陈志强,陈志彪.南方红壤侵蚀区土壤肥力质量的突变——以福建省长汀县为例[J].生态学报,2013,33(10):3002-3010.[Chen Z Q, Chen Z B. Mutation of soil fertility quality in the red eroded area of southern China:A case study in Changting County, Fujian Province[J]. Acta Ecologica Sinica, 2013,33(10):3002-3010.]
[18]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):65-70,90.[Chu W D, Chen W H, Ai J Q, et al. Hyperspectral estimation of camphor tree leaf chlorophyll content based on partial least squares regression[J]. Journal of Fujian Normal University(Natural Science Edition), 2014,30(1):65-70,90.]
[19]宫兆宁,赵雅莉,赵文吉,等.基于光谱指数的植物叶片叶绿素含量的估算模型[J].生态学报,2014,34(20):5736-5745.[Gong Z N, Zhao Y L, Zhao W J, et al. Estimation model for plant leaf chlorophyll content based on the spectral index content[J]. Acta Ecologica Sinica, 2014,34(20):5736-5745.]
[20]李春昉,郭际,赵绍丰.多元统计分析之因子分析浅析[J].价值工程,2010,29(15):128-129.[Li C F, Guo J, Zhao S F.[The analysis of factor analysis in multivariate statistical analysis[J]. Value Engineering, 2010,29(15):128-129.]
[21] 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
[22]姚付启,张振华,杨润亚,等.基于红边参数的植被叶绿素含量高光谱估算模型[J].农业工程学报,2009,25(S2):123-129.[Yao F Q, Zhang Z H, Yang R Y, et al. Hyperspectral models for estimating vegetation chlorophyll content based on red edge parameter[J]. Transactions of the CSAE, 2009,25(s2):123-129.]
[23] Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing and Environment, 1979,8(2):127-150.
[24] Gitelson A, Merzlyak M N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. spectral features and relation to chlorophyll estimation[J]. Journal of Plant Physiology, 1994,143(3):286-292.
[25] Datt B. A new reflectance index for remote sensing of chlorophyll content in higher plants:Tests using Eucalyptus leaves[J]. Journal of Plant Physiology, 1999,154(1):30-36.
[26] Sims D A, Gamon J A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance:A comparison of indices based on liquid water and chlorophyll absorption features[J]. Remote Sensing of Environment, 2003,84(4):526-537.
[27] Pe?uelas J, Gamon J A, Fredeen A L, et al. Reflectance indices associated with physiological changes in nitrogen and water-limited sunflower leaves[J]. Remote Sensing of Environment, 1994,48(2):135-146.
[28] Merzlyak M N, Chivkunova O B, Gitelson A A, et al.Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening[J]. Physiologia Plantarum, 2010,106(1):135-141.
[29] Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales:An evaluation of some hyperspectral approaches[J]. Remote Sensing of Environment,1998,66(3):273-285.
[30] Vogelmann J E, Rock B N, Moss D M. Red edge spectral measurements from sugar maple leaves[J]. International Journal of Remote Sensing, 1993,14(8):1563-1575.
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