基于多时相和多角度的烤烟烟碱密度遥感监测
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摘要
【目的】快速、准确获取烟株烟碱含量,实时把控烟叶品质和精准施肥。【方法】以不同区域、品种、氮肥处理连续两年的田间试验为基础,采集了烤烟整个生育期的多角度光谱反射率,选用观测天顶角和方位角组合形成的光谱数据构建烟碱密度的植被指数,建立最佳观测角度下的烟碱密度监测模型。【结果】(1)30°天顶角同210°方位角的角度组合为最佳观测角度;(2)RVI(1630,1740)和NDVI(1630,1740)为监测烟碱密度的最佳敏感光谱参数;(3)构建的两种预测模型BP神经网络和支持向量机,其R~2分别为0.944和0.996,模型验证的均方根误差P-RMSE分别为0.858和0.011,两者均具有较高的精准度和普适性。【结论】表明多角度、多时相遥感在大田烟碱密度的监测中具有良好的应用前景。
Rapid and accurate measuring of nicotine content in tobacco plants is of great significance for the control of tobacco quality and precise fertilization. As nicotine is in a non-uniform distribution in tobacco plants, current single-angle and single-period remote sensing observation methods may neglect nicotine information of middle and lower leaves or only obtain single period information of vegetation,which is difficult to meet the needs of quantitative remote sensing. In contrast, multi-angle, multi-temporal remote sensing can provide directional information and time information of vegetation, which helps to understand the characteristics of two-way reflection and improve the ability to quantitatively invert vegetation structure and physiological parameters. This research was carried out in different regions, varieties and nitrogen fertilizers in Xuchang Tobacco Science and Education Park of Henan Agricultural University, Songxian County of Luoyang City and Lushi County of Sanmenxia City for two consecutive years from 2016 to 2017. Data were collected from root-grown period of flue-cured tobacco(30 d after transplanting), prolonged period(55 d after transplanting) and mature period(75 d after transplanting); for each treatment,3 strains with uniform growth and typical representativeness were selected. Spectral reflectance of 73 angles per tobacco was collected. The total number of samples was 175,200, and the total number of samples per single angle was 2,400. Half of the random selection was made for modeling and verification. Nicotine density vegetation index was constructed using spectral data formed by different combinations of zenith angle and azimuth angle. Quantitative relationship between nicotine density normalized vegetation index(NDVI) and ratio vegetation index(RVI) was systematically analyzed by decremental fine sampling method. Results showed that the combination angle of 30° zenith angle and 210° azimuth was the best observation angle for nicotine canopy density; band combinations of R~2 >0.8 of RVI were(1,596～1,610,1,757～1,761),(1,609～11,672, 1,733～1,760) and(1,682～1,691, 1,716～1,722); band combinations of R~2 >0.8 of NDVI were(1,598～1,605,1,758～1,761),(1,610～1,668, 1,734～1,755) and(1,683～1,691, 1,717～1,722); and the best sensitive spectral parameters for monitoring nicotine density were RVI(1, 630, 1, 740) and NDVI(1, 630, 1, 740). A random sample of 120 samples from a total sample of single angle were used to build a prediction model, while the remaining 120 samples were used to validate the model. BP neural network and SVM(support vector machine), as nonlinear nicotine density prediction models, were constructed by selecting the top 20 RVI values and the first 20 NDVI with the best angle of 30°-210° values as independent variables, with the R~2 being 0.944 and 0.996, respectively, and the RMSE of the model verification being 0.858 and 0.011, respectively. Both models had high precision and universality. This study proves that multi-angle and multitemporal remote sensing has a good application prospect in the monitoring of nicotine density of tobacco.
Rapid and accurate measuring of nicotine content in tobacco plants is of great significance for the control of tobacco quality and precise fertilization. As nicotine is in a non-uniform distribution in tobacco plants, current single-angle and single-period remote sensing observation methods may neglect nicotine information of middle and lower leaves or only obtain single period information of vegetation,which is difficult to meet the needs of quantitative remote sensing. In contrast, multi-angle, multi-temporal remote sensing can provide directional information and time information of vegetation, which helps to understand the characteristics of two-way reflection and improve the ability to quantitatively invert vegetation structure and physiological parameters. This research was carried out in different regions, varieties and nitrogen fertilizers in Xuchang Tobacco Science and Education Park of Henan Agricultural University, Songxian County of Luoyang City and Lushi County of Sanmenxia City for two consecutive years from 2016 to 2017. Data were collected from root-grown period of flue-cured tobacco(30 d after transplanting), prolonged period(55 d after transplanting) and mature period(75 d after transplanting); for each treatment,3 strains with uniform growth and typical representativeness were selected. Spectral reflectance of 73 angles per tobacco was collected. The total number of samples was 175,200, and the total number of samples per single angle was 2,400. Half of the random selection was made for modeling and verification. Nicotine density vegetation index was constructed using spectral data formed by different combinations of zenith angle and azimuth angle. Quantitative relationship between nicotine density normalized vegetation index(NDVI) and ratio vegetation index(RVI) was systematically analyzed by decremental fine sampling method. Results showed that the combination angle of 30° zenith angle and 210° azimuth was the best observation angle for nicotine canopy density; band combinations of R~2 >0.8 of RVI were(1,596～1,610,1,757～1,761),(1,609～11,672, 1,733～1,760) and(1,682～1,691, 1,716～1,722); band combinations of R~2 >0.8 of NDVI were(1,598～1,605,1,758～1,761),(1,610～1,668, 1,734～1,755) and(1,683～1,691, 1,717～1,722); and the best sensitive spectral parameters for monitoring nicotine density were RVI(1, 630, 1, 740) and NDVI(1, 630, 1, 740). A random sample of 120 samples from a total sample of single angle were used to build a prediction model, while the remaining 120 samples were used to validate the model. BP neural network and SVM(support vector machine), as nonlinear nicotine density prediction models, were constructed by selecting the top 20 RVI values and the first 20 NDVI with the best angle of 30°-210° values as independent variables, with the R~2 being 0.944 and 0.996, respectively, and the RMSE of the model verification being 0.858 and 0.011, respectively. Both models had high precision and universality. This study proves that multi-angle and multitemporal remote sensing has a good application prospect in the monitoring of nicotine density of tobacco.
引文
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[4]姚延娟,阎广建,王锦地.多光谱多角度遥感数据综合反演叶面积指数方法研究[J].遥感学报,2005, 9(2):117-122.YAO Yanjuan, YAN Anjian, WANG Jinde. The approach on leaf a rea index inversion using multangular and multispectral Data Sets[J]. Journal of Remote Sensing, 2005, 9(2):117-122.
[5]HUANG Wenjiang, WANG Zhijie, HUANG Linsheng, et al.Estimation of vertical distribution of chlorophyll concentration by bidirectional canopy reflectance spectra in winter wheat[J]. Transactions of the Chinese Society of Agricultura, 2011, 12(2):165-178.
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[7]KUUSK A. The angular distribution of reflectance and vegetation indices in barley and clover canopies[J]. Remote Sensing of Environment, 1991, 37(2):143-151.
[8]黄文江,王纪华,刘良云,等.基于多时相和多角度光谱信息的作物株型遥感识别初探[J].农业工程学报,2005, 21(6):1-5.HUANG Wenjiang, WANG Jihua, LIU Liangyun, et al. Remote sensing identification of plant structural types based on multitemporal and bidirectional canopy spectrum[J]. Transactions of the Chinese Society of Agricultural Engineering, 2005, 21(6):1-5.
[9]JIAO Ziti, SCHAAF C B, DONG Yadong, et al. A method for improving hotspot directional signatures in BRDF models used for MODIS[J]. Remote Sensing of Environment, 2016, 186:135-151.
[10]LI Lantao, JáKLI B, LU Piaopiao, et al. Assessing leaf Nitrogen concentration of winter oilseed rape with canopy hyperspectral techniqueconsideringanon-uniformverticalNitrogen distribution[J]. Industrial Crops and Products, 2018, 116:1-14.
[11]张东彦,梁栋,赵晋陵,等.多角度成像解析大豆冠层的二向反射特征[J].红外与激光工程,2013, 42(3):787-797.ZHANGDongyan,LIANGDong,ZHAOJinling,etal.Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Infrared and Laser Engineering, 2013, 42(3):787-797.
[12]廖钦洪,张东彦,王纪华,等.基于多角度成像数据的新型植被指数构建与叶绿素含量估算[J].光谱学与光谱分析,2014,34(06):1599-1604.LIAO Qinhong, ZHANG Dongyan, WANG Jihua, et al. Assessment of chlorophyll content using s new vegetation index based on multiangular hyperspectral image data[J]. Spectroscopy and Spectral Analysis, 2014, 34(06):1599-1604.
[13]李伟娜,韦玮,张怀清,等.基于多角度高光谱数据的高寒沼泽湿地植被生物量估算[J].遥感技术与应用,2017, 32(5):809-817.LI Weina, WEI Wei, ZHANG Huaiqing, et al. Estimation the biomass of alpine wetland vegetation based on multi-angle and hyperspectral data[J]. Remote Sensing Technology and Application,2017, 32(5):809-817.
[14]张东彦,COBURN C,赵晋陵,等.基于多角度成像数据的大豆冠层叶绿素密度反演[J].农业机械学报,2013, 44(02):205-213.ZHANG Dongyan, COBURN C, ZHAO Jinling, et al. Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Transactions of the Chinese Society of Agricultural Machinery, 2013, 44(02):205-213.
[15]杨绍源,黄文江,梁栋,等.利用多角度光谱数据探测冬小麦氮素含量垂直分布方法研究[J].光谱学与光谱分析,2015, 35(07):1956-1960.YANG Shaoyuan, HUANG Wenjiang, LIANG Dong, et al.Estimating winter wheat Nitrogen vertical distribution based on bidirectional canopy reflected spectrum[J]. Spectroscopy and Spectral Analysis, 2015, 35(07):1956-1960.
[16]廖钦洪.作物长势参数的垂直分布反演及遥感监测研究[D].杭州:浙江大学,2014.LIAO Qinhong. Monitoring and estimating the vertical distribution of crop growth parameters based on remote sensing data[D].Hangzhou:Zhejing University, 2014.
[17]杨绍源.冬小麦氮素含量垂直分布的多角度光谱反演研究[D].合肥:安徽大学,2015.YANG Shaoyuan. Estimating winter wheat nitrogen vertical distribution with bidirectional canopy reflected spectrum[D]. Hefei:Anhui University, 2015.
[18]王强.基于激光雷达数据与多角度遥感模型的森林参数反演研究[D].哈尔滨:哈尔滨工业大学,2017.WANG Qiang. Research on forest parameters retrieval based on lidar data and multi-angle remote sensing model[D]. Harbin:Harbin Institute of Technology, 2017.
[19]童庆禧,张兵,郑兰芬.高光谱遥感——原理、技术与应用[M].北京:高等教育出版社,2006.TONG Qingxi, ZHANG Bing, ZHENG Lanfen. Hyperspectral Remote Sensing-Principles, Techniques and Applications[M].Beijing:Higher education press, 2006.
[20]柳钦火,辛晓洲,唐娉,等.定量遥感模型、应用及不确定性研究[M].北京:科学出版社,2010.LIU Qinhuo, XIN Xiaozhou, TANG Ping, et al. Quantitative Remote Sensing Model, Application and Uncertainty Research[M].Beijing:Science Press, 2010.
[21]国家烟草质量监督检验中心, YC/T 160-2002.烟草及烟草制品,总植物碱的测定,连续流动法[S].北京:中国标准出版社, 2002.National Tobacco Quality Supervision and Inspection Center, YC/T160-2002. Tobacco and tobacco products—Determination of total alkaloids—Continuous flow method[S]. Beijing:Standards Press of China, 2002.
[22]JORDAN C F. Derivation of leaf-area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4):663-666.
[23]ROUSE J W, HAAS R H, SCHELL J A, et al. Monitoring vegetation systems in the great plain with ERTS[C]//Proceedings of Third Earth Resources Technology Satellite-1 Symposium.Greenbelt:NASA SP-351, 1974:3010-3017.
[24]郭建茂,王迁,童应详,等.太阳辐射强度与观测角度对冬小麦冠层反射高光谱的影响[J].农业工程学报,2016, 32(10):157-163.GUO Jianmao, WANG Qian, TONG Yingxiang, et al. Effect of solar radiation intensity and observation angle on canopy reflectance hyperspectral for winter wheat[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(10):157-163.
[25]WU Chaoyang, NIU Zheng, WANG Jindi, et al. Predicting leaf area index in wheat using angular vegetation indices derived from in situ canopy measurements[J]. Canadian Journal of Remote Sensing,2010, 36(4):301-312.
[26]王卿,刘健,余坤勇.基于多角度植被指数的马尾松林LAI反演方法[J].植物科学学报,2017, 35(1):48-55.WANG Qing, LIU Jian, YU Kunyong. Inversion of Masson pine forest LAI by multiple-perspective vegetation index[J]. Plant Science Journal, 2017, 35(1):48-55.
[27]杨勤英.冬小麦叶面积指数与氮素垂直分布的高光谱反演研究[D].合肥:安徽大学,2014.YANG Qinying. Inversion of winter wheat leaf area index and nitrogen vertical distribution with hyperspectral data. Hefei:Anhui University, 2014.
[28]SCHNEIDER T, MANAKOS I. BRDF approximation of maize and canopy parameter retrieval by ProSail inversion[C]//Proceedings of the 3rd EARSeL Workshop on Imaging Spectroscopy.Herrsching,Germany, 2003:13-16.
[29]张东彦,梁栋,赵晋陵,等.多角度成像解析大豆冠层的二向反射特征[J].红外与激光工程,2013, 42(3):787-797.ZHANGDongyan,LIANGDong,ZHAOJinling,etal.Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Infrared and Laser Engineering, 2013, 42(3):787-797.
[2]DUVEILLER G, BARET F, DEFOURNY P. Remotely sensed green area index for winter wheat crop monitoring:10-Year assessment at regional scale over a fragmented landscape[J].Agricultural and Forest Meteorology, 2012, 166(167):156-168.
[3]JIA Fangfang, LIU Guoshun, DING Songshuang, et al. Using leaf spectral reflectance to monitor the effects of shading on nicotine content in tobacco leaves[J]. Industrial Crops and Products, 2013,51(6):444-452.
[4]姚延娟,阎广建,王锦地.多光谱多角度遥感数据综合反演叶面积指数方法研究[J].遥感学报,2005, 9(2):117-122.YAO Yanjuan, YAN Anjian, WANG Jinde. The approach on leaf a rea index inversion using multangular and multispectral Data Sets[J]. Journal of Remote Sensing, 2005, 9(2):117-122.
[5]HUANG Wenjiang, WANG Zhijie, HUANG Linsheng, et al.Estimation of vertical distribution of chlorophyll concentration by bidirectional canopy reflectance spectra in winter wheat[J]. Transactions of the Chinese Society of Agricultura, 2011, 12(2):165-178.
[6]高峰,朱启疆.植被冠层多角度遥感研究进展[J].地理科学,1997, 17(4):346-355.GAO Feng, ZHU Qijiang. Inversion of vegetation canopies using multangular remote sensing[J]. Journal of Geography Sciences,1997, 17(4):346-355.
[7]KUUSK A. The angular distribution of reflectance and vegetation indices in barley and clover canopies[J]. Remote Sensing of Environment, 1991, 37(2):143-151.
[8]黄文江,王纪华,刘良云,等.基于多时相和多角度光谱信息的作物株型遥感识别初探[J].农业工程学报,2005, 21(6):1-5.HUANG Wenjiang, WANG Jihua, LIU Liangyun, et al. Remote sensing identification of plant structural types based on multitemporal and bidirectional canopy spectrum[J]. Transactions of the Chinese Society of Agricultural Engineering, 2005, 21(6):1-5.
[9]JIAO Ziti, SCHAAF C B, DONG Yadong, et al. A method for improving hotspot directional signatures in BRDF models used for MODIS[J]. Remote Sensing of Environment, 2016, 186:135-151.
[10]LI Lantao, JáKLI B, LU Piaopiao, et al. Assessing leaf Nitrogen concentration of winter oilseed rape with canopy hyperspectral techniqueconsideringanon-uniformverticalNitrogen distribution[J]. Industrial Crops and Products, 2018, 116:1-14.
[11]张东彦,梁栋,赵晋陵,等.多角度成像解析大豆冠层的二向反射特征[J].红外与激光工程,2013, 42(3):787-797.ZHANGDongyan,LIANGDong,ZHAOJinling,etal.Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Infrared and Laser Engineering, 2013, 42(3):787-797.
[12]廖钦洪,张东彦,王纪华,等.基于多角度成像数据的新型植被指数构建与叶绿素含量估算[J].光谱学与光谱分析,2014,34(06):1599-1604.LIAO Qinhong, ZHANG Dongyan, WANG Jihua, et al. Assessment of chlorophyll content using s new vegetation index based on multiangular hyperspectral image data[J]. Spectroscopy and Spectral Analysis, 2014, 34(06):1599-1604.
[13]李伟娜,韦玮,张怀清,等.基于多角度高光谱数据的高寒沼泽湿地植被生物量估算[J].遥感技术与应用,2017, 32(5):809-817.LI Weina, WEI Wei, ZHANG Huaiqing, et al. Estimation the biomass of alpine wetland vegetation based on multi-angle and hyperspectral data[J]. Remote Sensing Technology and Application,2017, 32(5):809-817.
[14]张东彦,COBURN C,赵晋陵,等.基于多角度成像数据的大豆冠层叶绿素密度反演[J].农业机械学报,2013, 44(02):205-213.ZHANG Dongyan, COBURN C, ZHAO Jinling, et al. Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Transactions of the Chinese Society of Agricultural Machinery, 2013, 44(02):205-213.
[15]杨绍源,黄文江,梁栋,等.利用多角度光谱数据探测冬小麦氮素含量垂直分布方法研究[J].光谱学与光谱分析,2015, 35(07):1956-1960.YANG Shaoyuan, HUANG Wenjiang, LIANG Dong, et al.Estimating winter wheat Nitrogen vertical distribution based on bidirectional canopy reflected spectrum[J]. Spectroscopy and Spectral Analysis, 2015, 35(07):1956-1960.
[16]廖钦洪.作物长势参数的垂直分布反演及遥感监测研究[D].杭州:浙江大学,2014.LIAO Qinhong. Monitoring and estimating the vertical distribution of crop growth parameters based on remote sensing data[D].Hangzhou:Zhejing University, 2014.
[17]杨绍源.冬小麦氮素含量垂直分布的多角度光谱反演研究[D].合肥:安徽大学,2015.YANG Shaoyuan. Estimating winter wheat nitrogen vertical distribution with bidirectional canopy reflected spectrum[D]. Hefei:Anhui University, 2015.
[18]王强.基于激光雷达数据与多角度遥感模型的森林参数反演研究[D].哈尔滨:哈尔滨工业大学,2017.WANG Qiang. Research on forest parameters retrieval based on lidar data and multi-angle remote sensing model[D]. Harbin:Harbin Institute of Technology, 2017.
[19]童庆禧,张兵,郑兰芬.高光谱遥感——原理、技术与应用[M].北京:高等教育出版社,2006.TONG Qingxi, ZHANG Bing, ZHENG Lanfen. Hyperspectral Remote Sensing-Principles, Techniques and Applications[M].Beijing:Higher education press, 2006.
[20]柳钦火,辛晓洲,唐娉,等.定量遥感模型、应用及不确定性研究[M].北京:科学出版社,2010.LIU Qinhuo, XIN Xiaozhou, TANG Ping, et al. Quantitative Remote Sensing Model, Application and Uncertainty Research[M].Beijing:Science Press, 2010.
[21]国家烟草质量监督检验中心, YC/T 160-2002.烟草及烟草制品,总植物碱的测定,连续流动法[S].北京:中国标准出版社, 2002.National Tobacco Quality Supervision and Inspection Center, YC/T160-2002. Tobacco and tobacco products—Determination of total alkaloids—Continuous flow method[S]. Beijing:Standards Press of China, 2002.
[22]JORDAN C F. Derivation of leaf-area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4):663-666.
[23]ROUSE J W, HAAS R H, SCHELL J A, et al. Monitoring vegetation systems in the great plain with ERTS[C]//Proceedings of Third Earth Resources Technology Satellite-1 Symposium.Greenbelt:NASA SP-351, 1974:3010-3017.
[24]郭建茂,王迁,童应详,等.太阳辐射强度与观测角度对冬小麦冠层反射高光谱的影响[J].农业工程学报,2016, 32(10):157-163.GUO Jianmao, WANG Qian, TONG Yingxiang, et al. Effect of solar radiation intensity and observation angle on canopy reflectance hyperspectral for winter wheat[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(10):157-163.
[25]WU Chaoyang, NIU Zheng, WANG Jindi, et al. Predicting leaf area index in wheat using angular vegetation indices derived from in situ canopy measurements[J]. Canadian Journal of Remote Sensing,2010, 36(4):301-312.
[26]王卿,刘健,余坤勇.基于多角度植被指数的马尾松林LAI反演方法[J].植物科学学报,2017, 35(1):48-55.WANG Qing, LIU Jian, YU Kunyong. Inversion of Masson pine forest LAI by multiple-perspective vegetation index[J]. Plant Science Journal, 2017, 35(1):48-55.
[27]杨勤英.冬小麦叶面积指数与氮素垂直分布的高光谱反演研究[D].合肥:安徽大学,2014.YANG Qinying. Inversion of winter wheat leaf area index and nitrogen vertical distribution with hyperspectral data. Hefei:Anhui University, 2014.
[28]SCHNEIDER T, MANAKOS I. BRDF approximation of maize and canopy parameter retrieval by ProSail inversion[C]//Proceedings of the 3rd EARSeL Workshop on Imaging Spectroscopy.Herrsching,Germany, 2003:13-16.
[29]张东彦,梁栋,赵晋陵,等.多角度成像解析大豆冠层的二向反射特征[J].红外与激光工程,2013, 42(3):787-797.ZHANGDongyan,LIANGDong,ZHAOJinling,etal.Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Infrared and Laser Engineering, 2013, 42(3):787-797.
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