一种组合反演叶面积指数的方法
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摘要
针对传统PRO4SAIL+查找表方法反演叶面积指数存在查找表过于庞大,反演速度较慢等问题,该文提出一种基于PRO4SAIL与局部加权多元回归组合模型反演叶面积指数的方法。通过利用卫星传感器光谱响应函数实现了实测端元高光谱向像元多光谱的转化,解决了测量尺度不同导致的反射率差异问题;选取两种叶面积指数植被指数MTVI1和MCARI1作为反演因子,同时只选用40组PRO4SAIL模型模拟数据建立训练组,解决查找表数据量过大的问题;将局部加权多元回归的权重因子距离公式按照反演因子个数从一维空间扩展至多维空间,更符合实际应用。该组合模型的预测决定系数为0.727 1,平均相对误差为11.09%,传统查找表的预测决定系数为0.693 2,平均相对误差为13.63%。实验结果表明:组合模型具有较好的预测能力,反演得到的叶面积指数含量精度较高,可为更好地监测路域植被生态环境提供技术支撑。
Due to the disadvantages of the traditional PRO4 SAIL+look-up table method inversion of leaf area index,such as the large size of the look-up table and slow retrieval speed,a method based on combined model of PRO4 SAIL and local weighted multi-variable regression to invert the leaf area index was proposed.By using the spectral response function of the satellite sensor,the conversion of measured endmember hyper-spectral to pixel multi-spectral is realized,and the problem of the difference in reflectivity caused by different measurement scales is solved;Two vegetation index of leaf area index of MTVI1 and MCARI1 were selected as inversion factors,and only 40 sets of PRO4 SAIL model simulation data were used to establish a training group,which solve the problem of excessive data in the look-up table;The weighted distance formula of local weighted multi-variable regression expands from one-dimensional space to multidimensional space according to the number of inversion factors,which is more in line with practical application.The predictive coefficient of the combination model is 0.730 3,and the average relative error is 11.95%,the prediction coefficient of the traditional look-up table is 0.683 9,and the average relative error is 14.93%.The experimental results show that:the combined model has good predictive ability,and the accuracy of the leaf area index obtained by inversion is higher,which provides technical support for better monitoring of the road vegetation ecological environment.
Due to the disadvantages of the traditional PRO4 SAIL+look-up table method inversion of leaf area index,such as the large size of the look-up table and slow retrieval speed,a method based on combined model of PRO4 SAIL and local weighted multi-variable regression to invert the leaf area index was proposed.By using the spectral response function of the satellite sensor,the conversion of measured endmember hyper-spectral to pixel multi-spectral is realized,and the problem of the difference in reflectivity caused by different measurement scales is solved;Two vegetation index of leaf area index of MTVI1 and MCARI1 were selected as inversion factors,and only 40 sets of PRO4 SAIL model simulation data were used to establish a training group,which solve the problem of excessive data in the look-up table;The weighted distance formula of local weighted multi-variable regression expands from one-dimensional space to multidimensional space according to the number of inversion factors,which is more in line with practical application.The predictive coefficient of the combination model is 0.730 3,and the average relative error is 11.95%,the prediction coefficient of the traditional look-up table is 0.683 9,and the average relative error is 14.93%.The experimental results show that:the combined model has good predictive ability,and the accuracy of the leaf area index obtained by inversion is higher,which provides technical support for better monitoring of the road vegetation ecological environment.
引文
[1]林文鹏.基于SPOT遥感影像的城市森林叶面积指数反演[J].测绘科学,2008,33(2):57-63.(LINWenpeng.Inversion of urban forest leaf area index based on SPOT remote sensing images[J].Science of Surveying and Mapping,2008,33(2):57-63.)
[2]蔡博峰,绍霞.基于PROSPECT+SAIL模型的遥感叶面积指数反演[J].国土资源遥感,2007(2):39-43.(CAIBofeng,SHAO Xia.Inversion of leaf area index of remote sensing based on PROSPECT+SAIL model[J].Remote Sensing for Land&Resources,2007(2):39-43.)
[3]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.(ZHAO Yingshi.Principle and method of remote sensing application analysis[M].Beijing:Science Press,2003.)
[4]张婷,丁建丽,王飞.基于实测端元光谱的多光谱图像光谱模拟研究[J].光谱学与光谱分析,2010,30(11):2889-2893.(ZHANG Ting,DING Jianli,WANG fei.Multi-spectral image spectral simulation based on realtime endmember spectra[J]Spectroscopy and Spectral Analysis,2010,30(11):2889-2893.)
[5]吴伶,刘湘南,周博天,等.利用PROSPECT+SAIL模型反演植物生化参数植被指数优化模拟[J].应用生态学报,2012,23(12):3250-3256.(WU Ling,LIU Xiangnan,ZHOUBotian,et al.Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT+SAIL model[J].Chinese Journal of Applied Ecology,2012,23(12):3250-3256.)
[6]孙家柄.遥感原理与应用[M].武汉:武汉大学出版社,2009.(SUN Jiabing.Principles and applications of remote sensing[M].Wuhan:Wuhan University Press,2009.)
[7]JACQUEMOUDS,VERHOEF W.PROSPECT+SAILmodels:A review of use for vegetation characteri-zation[J].Remote Sensing of Environment,2009(113):56-66.
[8]JACQUEMOUD S,BARET F.PROSPECT:a model of leaf optical properties spectra[J].Remote Sensing of Environment,1990,34(2):75-91.
[9]陆成,陈圣波,刘万崧,等.叶片辐射传输模型理论研究[J].世界地质,2013,32(1):177-188.(LU Cheng,CHEN Shengbo,LIU Wansong,et al.Research of PROSPECT leaf optical property model[J].Global Geology2013,32(1):177-188.)
[10]展昕.基于SAIL模型的光谱解混研究[D].武汉:华中科技大学,2009.(ZHAN Xin.Study on spectral unmixing based on SAIL model[D].Wuhan:Huazhong University of Science and Technology,2009.)
[11]郭云开,苟叶培.路域植被叶面积指数变化遥感监测[J].遥感信息,2013,28(6):92-95.(GUO Yunkai,GOU Yepei.Remote sensing monitoring on the change of road vegetation leaf area index[J].Remote Sensing Information,2013,28(6):92-95.)
[12]郭云开,张进会.路域植被叶绿素多光谱遥感定量反演研究[J].测绘通报,2014(12):5-8.(GUO Yunkai,ZHANG Jinhui.Quantitative inversion research of road vegetation chlorophyll by using multispectral remote sensing[J].Bulletin of Surveying and Mapping,2014(12):5-8.)
[13]徐晓丹,刘华文,姚明海,等.一种基于局部加权回归的分类方法[J].计算机工程与科学,2015,37(10):1959-1964.(XU Xiaodan,LIU Huawen,YAO Minghai,et al.A novel classification method based on locally weighted regression[J].Computer Engineering and Science,2015,37(10):1959-1964.)
[14]张金艺,张晶晶,李若涵,等.流水线型局部加权回归RFID室内定位[J].应用科学学报,2014,32(2):125-132.(ZHANG Jinyi,ZHANG Jingjing,LI Ruohan,et al.Pipelined RFID indoor positioning based on locally-weighted regression[J]Journal of Applied Sciences,2014,32(2):125-132.)
[15]谷成燕.基于PROSAIL辐射传输模型的毛竹林冠层参数遥感定量反演[D].杭州:浙江农林大学,2013.(GUChengyan.Satellite-based retrieval of canopy parameters of Moso bamboo forest with PROSAIL radiative transfer model[D].Hangzhou:Zhejiang A&F University,2013.)
[16]HABOUDANE D,MILLER J R,PATTEY E,et al.Hyperspectral vegetation indices and novel algorithms for predicting green lai of crop canopies:modeling and validation in the context of precision agriculture[J].Remote Sensing of Environment,2004,90:337-352.
[2]蔡博峰,绍霞.基于PROSPECT+SAIL模型的遥感叶面积指数反演[J].国土资源遥感,2007(2):39-43.(CAIBofeng,SHAO Xia.Inversion of leaf area index of remote sensing based on PROSPECT+SAIL model[J].Remote Sensing for Land&Resources,2007(2):39-43.)
[3]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.(ZHAO Yingshi.Principle and method of remote sensing application analysis[M].Beijing:Science Press,2003.)
[4]张婷,丁建丽,王飞.基于实测端元光谱的多光谱图像光谱模拟研究[J].光谱学与光谱分析,2010,30(11):2889-2893.(ZHANG Ting,DING Jianli,WANG fei.Multi-spectral image spectral simulation based on realtime endmember spectra[J]Spectroscopy and Spectral Analysis,2010,30(11):2889-2893.)
[5]吴伶,刘湘南,周博天,等.利用PROSPECT+SAIL模型反演植物生化参数植被指数优化模拟[J].应用生态学报,2012,23(12):3250-3256.(WU Ling,LIU Xiangnan,ZHOUBotian,et al.Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT+SAIL model[J].Chinese Journal of Applied Ecology,2012,23(12):3250-3256.)
[6]孙家柄.遥感原理与应用[M].武汉:武汉大学出版社,2009.(SUN Jiabing.Principles and applications of remote sensing[M].Wuhan:Wuhan University Press,2009.)
[7]JACQUEMOUDS,VERHOEF W.PROSPECT+SAILmodels:A review of use for vegetation characteri-zation[J].Remote Sensing of Environment,2009(113):56-66.
[8]JACQUEMOUD S,BARET F.PROSPECT:a model of leaf optical properties spectra[J].Remote Sensing of Environment,1990,34(2):75-91.
[9]陆成,陈圣波,刘万崧,等.叶片辐射传输模型理论研究[J].世界地质,2013,32(1):177-188.(LU Cheng,CHEN Shengbo,LIU Wansong,et al.Research of PROSPECT leaf optical property model[J].Global Geology2013,32(1):177-188.)
[10]展昕.基于SAIL模型的光谱解混研究[D].武汉:华中科技大学,2009.(ZHAN Xin.Study on spectral unmixing based on SAIL model[D].Wuhan:Huazhong University of Science and Technology,2009.)
[11]郭云开,苟叶培.路域植被叶面积指数变化遥感监测[J].遥感信息,2013,28(6):92-95.(GUO Yunkai,GOU Yepei.Remote sensing monitoring on the change of road vegetation leaf area index[J].Remote Sensing Information,2013,28(6):92-95.)
[12]郭云开,张进会.路域植被叶绿素多光谱遥感定量反演研究[J].测绘通报,2014(12):5-8.(GUO Yunkai,ZHANG Jinhui.Quantitative inversion research of road vegetation chlorophyll by using multispectral remote sensing[J].Bulletin of Surveying and Mapping,2014(12):5-8.)
[13]徐晓丹,刘华文,姚明海,等.一种基于局部加权回归的分类方法[J].计算机工程与科学,2015,37(10):1959-1964.(XU Xiaodan,LIU Huawen,YAO Minghai,et al.A novel classification method based on locally weighted regression[J].Computer Engineering and Science,2015,37(10):1959-1964.)
[14]张金艺,张晶晶,李若涵,等.流水线型局部加权回归RFID室内定位[J].应用科学学报,2014,32(2):125-132.(ZHANG Jinyi,ZHANG Jingjing,LI Ruohan,et al.Pipelined RFID indoor positioning based on locally-weighted regression[J]Journal of Applied Sciences,2014,32(2):125-132.)
[15]谷成燕.基于PROSAIL辐射传输模型的毛竹林冠层参数遥感定量反演[D].杭州:浙江农林大学,2013.(GUChengyan.Satellite-based retrieval of canopy parameters of Moso bamboo forest with PROSAIL radiative transfer model[D].Hangzhou:Zhejiang A&F University,2013.)
[16]HABOUDANE D,MILLER J R,PATTEY E,et al.Hyperspectral vegetation indices and novel algorithms for predicting green lai of crop canopies:modeling and validation in the context of precision agriculture[J].Remote Sensing of Environment,2004,90:337-352.