基于低空无人机影像光谱和纹理特征的棉花氮素营养诊断研究
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摘要
【目的】基于无人机高空间分辨率影像,探讨剔除土壤背景信息及增加纹理信息对棉花植株氮浓度反演的影响,为棉花氮素营养精准探测提供新技术手段。【方法】开展棉花水、氮耦合试验,分别在棉花的不同生育期获取无人机多光谱影像和植株氮浓度信息。基于以上数据,首先探讨了土壤背景对棉花冠层光谱的影响;其次,分析了影像纹理特征与植株氮浓度间的相关性;最后,将获得的数据分为建模样本和检验样本,设置剔除土壤背景前、剔除土壤背景后、增加纹理特征等不同情景,采用光谱指数与主成分分析耦合建模的方法,来建立各种情景下植株氮浓度的反演模型,并对模型反演效果进行比较。【结果】土壤背景对棉花冠层光谱有影响,且不同生育期趋势不同;影像纹理特征参数与植株氮浓度间有显著相关关系;剔除土壤背景前植株氮浓度反演模型的建模决定系数为0.33,标准误差为0.21%,验证决定系数为0.19,标准误差为0.23%;剔除土壤背景后模型的建模决定系数为0.38,标准误差为0.20%,验证决定系数为0.30,标准误差为0.21%;增加纹理信息后模型的建模决定系数为0.57,标准误差为0.17%,验证决定系数为0.42,标准误差为0.19%。【结论】基于低空无人机高空间分辨率影像,剔除土壤背景和增加纹理特征均可提高棉花植株氮浓度的反演精度;影像纹理可以作为一种重要信息来支撑无人机遥感技术反演作物氮素营养状况。
【Objective】 Based on the high spatial resolution images of unmanned aerial vehicle(UAV), the effects of removing soil background information and increasing image texture information on the inversion of cotton plant nitrogen concentration were investigated, in order to provide new technology for accurate estimation of cotton nitrogen nutrition status.【Method】 Cotton water and nitrogen coupling experiment was conducted, and UAV images and plant nitrogen concentration data were measured during different cotton growth stages. Based on the above data, the effect of soil background on cotton canopy spectrum was firstly investigated. Secondly, the correlations between image texture parameters and plant nitrogen concentration were analyzed. Finally, the obtained data was divided into calibration dataset and validation dataset. Different scenarios, including before and after removing the soil background, and adding texture features, were set. The inversion models of plant nitrogen concentration under various scenarios were designed by using the coupled method of spectral indexes and principal component regression, and the performances of the models were compared. 【Result】 The soil background had an effect on the cotton canopy spectrum, and the trends were not the same at different growth stages. There existed significant correlations between image texture parameters and plant nitrogen concentration. For the scenarios before removal soil background, the plant nitrogen concentration prediction model had determination coefficient(R~2) value of 0.33 and root mean square error(RMSE) value of 0.21%during model calibration, and R~2 value of 0.19 and RMSE value of 0.23% during validation. For the scenarios after removing soil background, the plant nitrogen concentration prediction model had R~2 value of 0.38 and RMSE value of 0.20% during model calibration, and R~2 value of 0.30 and RMSE value of 0.21% during validation. For the scenarios adding image texture information,the plant nitrogen concentration prediction model had R~2 value of 0.57 and RMSE value of 0.17% during model calibration, and R~2 value of 0.42 and RMSE value of 0.19% during validation. 【Conclusion】 Based on high spatial resolution images of low-altitude UAVs, both removing soil background and adding image texture information could improve the inversion accuracy of cotton plant nitrogen concentration. Image texture could be considered as important information to support prediction of crop nitrogen nutrition status using UAV images.
【Objective】 Based on the high spatial resolution images of unmanned aerial vehicle(UAV), the effects of removing soil background information and increasing image texture information on the inversion of cotton plant nitrogen concentration were investigated, in order to provide new technology for accurate estimation of cotton nitrogen nutrition status.【Method】 Cotton water and nitrogen coupling experiment was conducted, and UAV images and plant nitrogen concentration data were measured during different cotton growth stages. Based on the above data, the effect of soil background on cotton canopy spectrum was firstly investigated. Secondly, the correlations between image texture parameters and plant nitrogen concentration were analyzed. Finally, the obtained data was divided into calibration dataset and validation dataset. Different scenarios, including before and after removing the soil background, and adding texture features, were set. The inversion models of plant nitrogen concentration under various scenarios were designed by using the coupled method of spectral indexes and principal component regression, and the performances of the models were compared. 【Result】 The soil background had an effect on the cotton canopy spectrum, and the trends were not the same at different growth stages. There existed significant correlations between image texture parameters and plant nitrogen concentration. For the scenarios before removal soil background, the plant nitrogen concentration prediction model had determination coefficient(R~2) value of 0.33 and root mean square error(RMSE) value of 0.21%during model calibration, and R~2 value of 0.19 and RMSE value of 0.23% during validation. For the scenarios after removing soil background, the plant nitrogen concentration prediction model had R~2 value of 0.38 and RMSE value of 0.20% during model calibration, and R~2 value of 0.30 and RMSE value of 0.21% during validation. For the scenarios adding image texture information,the plant nitrogen concentration prediction model had R~2 value of 0.57 and RMSE value of 0.17% during model calibration, and R~2 value of 0.42 and RMSE value of 0.19% during validation. 【Conclusion】 Based on high spatial resolution images of low-altitude UAVs, both removing soil background and adding image texture information could improve the inversion accuracy of cotton plant nitrogen concentration. Image texture could be considered as important information to support prediction of crop nitrogen nutrition status using UAV images.
引文
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[4]肖晶晶,霍治国,姚益平,张蕾,李娜,柏秦凤,温泉沛.棉花节水灌溉气象等级指标.生态学报,2013,33(22):7288-7299.XIAO J J,HUO Z G,YAO Y P,ZHANG L,LI N,BAI Q F,WEN Q P.Meteorogical grading indexs of water-saving irrigation for cotton.Acta Ecologica Sinica,2013,33(22):7288-7299.(in Chinese)
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[6]陈鹏飞,孙九林,王纪华,赵春江.基于遥感的作物氮素营养诊断技术:现状与趋势.中国科学(信息科学),2010,40(增刊):21-37.CHEN P F,SUN J L,WANG J H,ZHAO C J.Using remote sensing technology for crop nitrogen diagnosis:Status and trends.Scientia Sinica(Informationis),2010,40(S1):21-37.(in Chinese)
[7]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.Remote Sensing of Environment,2003,86(4):542-553.
[8]EITEL J U H,LONG D S,GESSLER P E,SMITH A M S.Using in-situ measurements to evaluate the new RapidEyeTM satellite series for prediction of wheat nitrogen status.International Journal of Remote Sensing,2007,28(18):4183-4190.
[9]CHEN P F,DRISS H,TREMBLAY N,WANG J H,VIGNEAULT P,LI B G.New index for estimating crop nitrogen concentration using hyperspectral data.Remote Sensing of Environment,2010,114(9):1987-1997.
[10]HUANG S Y,MIAO Y X,YUAN F,GNYP M L,YAO Y K,CAO Q,WANG H Y,LENZ-WIEDEMANN V I S,BARETH G.Potential of rapidEye and worldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages.Remote Sensing,2017,9(3):227.
[11]LIANG L,DI L P,HUANG T,WANG J H,LIN L,WANG L J,YANGM H.Estimation of leaf nitrogen content in wheat using new hyperspectral indices and a random forest regression algorithm.Remote Sensing,2018,10(12):1940.
[12]田明璐,班松涛,常庆瑞,由明明,罗丹,王力,王烁.基于低空无人机成像光谱仪影像估算棉花叶面积指数.农业工程学报,2016,32(21):102-108.TIAN M L,BAN S T,CHANG Q R,YOU M M,LUO D,WANG L,WANG S.Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index.Transactions of the Chinese Society of Agricultural Engineering,2016,32(21):102-108.(in Chinese)
[13]秦占飞,常庆瑞,谢宝妮,申健.基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测.农业工程学报,2016,32(23):77-85.QIN Z F,CHANG Q R,XIE B N,SHEN J.Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district.Transactions of the Chinese Society of Agricultural Engineering,2016,32(23):77-85.(in Chinese)
[14]LIU H Y,ZHU H C,WANG P.Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyper-spectral data.International Journal of Remote Sensing,2017,38(8/10):2117-2134.
[15]N?SI R,VILJANEN N,KAIVOSOJA J,ALHONOJA K,HAKALAT,MARKELIN L,HONKAVAARA E.Estimating biomass and nitrogen amount of barley and grass using UAV and aircraft based spectral and photogrammetric 3D features.Remote Sensing,2018,10(7):1082.
[16]张智韬,边江,韩文霆,付秋萍,陈硕博,崔婷.剔除土壤背景的棉花水分胁迫无人机热红外遥感诊断.农业机械学报,2018,49(10):250-260.ZHANG Z T,BIAN J,HAN W T,FU Q P,CHEN S B,CUI T.Diagnosis of cotton water stress using unmanned aerial vehicle thermal infrared remote sensing after removing soil background.Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):250-260.(in Chinese)
[17]ZHU Y,YAO X,TIAN Y C,LIU X J,CAO W X.Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice.International Journal of Applied Earth Observation and Geoinformation,2008,10(1):1-10.
[18]CHEN P F.A comparison of two approaches for estimating the wheat nitrogen nutrition index using remote sensing.Remote Sensing,2015,7(4):4527-4548.
[19]ROUSE J W,HAAS R W,SCHELL J A,DEERING D W,HARLAN JC.Monitoring the vernal advancement and retrogradation(green wave effect)of natural vegetation.NASA/GSFCT Type III Final Report,USA:NASA,1974.
[20]PEARSON R L,MILLER L D.Remote mapping of standing crop biomass for estimation of the productivity of the Shortgrass Prairie,Pawnee National Grasslands,Colorado//Proceedings of the Eighth International Symposium on Remote Sensing of Environment.Ann Arbor,Michigan,USA,1972:1357-1381.
[21]HUETE A,JUSTICE C,LIU H.Development of vegetation and soil indices for MODIS-EOS.Remote Sensing of Environment,1994,49(3):224-234.
[22]BROGE N H,LEBLANC E.Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density.Remote Sensing of Environment,2001,76(2):156-172.
[23]QI J,CHEHBOUNI A,HUETE A R,KERR Y H,SOROOSHIAN S.A modified soil adjusted vegetation index.Remote Sensing of Environment,1994,48(2):119-126.
[24]RONDEAUX G,STEVEN M,BARET F.Optimization of soiladjusted vegetation indices.Remote Sensing of Environment,1996,55(2):95-107.
[25]HABOUDANE D,MILLER J R,PATTEY E,ZARCO-TEJADA P J,STRACHAN I B.Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies:Modeling and validation in the context of precision agriculture.Remote Sensing of Environment,2004,90(3):337-352.
[26]GITELSON A A,VI?A A,CIGANDA V,RUNDQUIST D C,ARKEBAUER T J.Remote estimation of canopy chlorophyll content in crops.Geophysical Research Letters,2005,32(8):93-114.
[27]XUE L H,CAO W X,LUO W H,DAI T B,ZHU Y.Monitoring leaf nitrogen status in rice with canopy spectral reflectance.Agronomy Journal,2004,96(1):135-142.
[28]YANG F,SUN J L,FANG H L,YAO Z F,ZHANG J H,ZHU Y Q,SONG K S,WANG Z M,HU M G.Comparison of different methods for corn LAI estimation over northeastern China.International Journal of Applied Earth Observation and Geoinformation,2012,18:462-471.
[29]LI J T,SHI Y Y,VEERANAMPALAYAM-SIVAKUMAR A N,SCHACHTMAN D P.Elucidating sorghum biomass,nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system.Frontiers in Plant Science,2018,9:1406.
[30]张东彦.基于高光谱成像技术的作物叶绿素信息诊断机理及方法研究[D].杭州:浙江大学,2012.ZHANG D Y.Diagnosis mechanism and methods of crop chlorophyll information based on hyperspectral imaging technology[D].Hangzhou:Zhejiang University,2012.(in Chinese)
[2]薛利红,罗卫红,曹卫星,田永超.作物水分和氮素光谱诊断研究进展.遥感学报,2003,7(1):73-80.XUE L H,LUO W H,CAO W X,TIAN Y C.Research progress on the water and nitrogen detection using spectral reflectance.Journal of Remote Sensing,2003,7(1):73-80.(in Chinese)
[3]JUNG J H,MAEDA M,CHANG A J,LANDIVAR J,YEOM J,MCGINTY J.Unmanned aerial system assisted framework for the selection of high yielding cotton genotypes.Computers and Electronics in Agriculture,2018,152:74-81.
[4]肖晶晶,霍治国,姚益平,张蕾,李娜,柏秦凤,温泉沛.棉花节水灌溉气象等级指标.生态学报,2013,33(22):7288-7299.XIAO J J,HUO Z G,YAO Y P,ZHANG L,LI N,BAI Q F,WEN Q P.Meteorogical grading indexs of water-saving irrigation for cotton.Acta Ecologica Sinica,2013,33(22):7288-7299.(in Chinese)
[5]TREMBLAY N.Determining nitrogen requirements from crops characteristics.Benefits and challenges//PANDALAI S G.Recent Research Developments in Agronomy and Horticulture:vol 1.Kerala:India Research Signpost Press,2004:157-182.
[6]陈鹏飞,孙九林,王纪华,赵春江.基于遥感的作物氮素营养诊断技术:现状与趋势.中国科学(信息科学),2010,40(增刊):21-37.CHEN P F,SUN J L,WANG J H,ZHAO C J.Using remote sensing technology for crop nitrogen diagnosis:Status and trends.Scientia Sinica(Informationis),2010,40(S1):21-37.(in Chinese)
[7]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.Remote Sensing of Environment,2003,86(4):542-553.
[8]EITEL J U H,LONG D S,GESSLER P E,SMITH A M S.Using in-situ measurements to evaluate the new RapidEyeTM satellite series for prediction of wheat nitrogen status.International Journal of Remote Sensing,2007,28(18):4183-4190.
[9]CHEN P F,DRISS H,TREMBLAY N,WANG J H,VIGNEAULT P,LI B G.New index for estimating crop nitrogen concentration using hyperspectral data.Remote Sensing of Environment,2010,114(9):1987-1997.
[10]HUANG S Y,MIAO Y X,YUAN F,GNYP M L,YAO Y K,CAO Q,WANG H Y,LENZ-WIEDEMANN V I S,BARETH G.Potential of rapidEye and worldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages.Remote Sensing,2017,9(3):227.
[11]LIANG L,DI L P,HUANG T,WANG J H,LIN L,WANG L J,YANGM H.Estimation of leaf nitrogen content in wheat using new hyperspectral indices and a random forest regression algorithm.Remote Sensing,2018,10(12):1940.
[12]田明璐,班松涛,常庆瑞,由明明,罗丹,王力,王烁.基于低空无人机成像光谱仪影像估算棉花叶面积指数.农业工程学报,2016,32(21):102-108.TIAN M L,BAN S T,CHANG Q R,YOU M M,LUO D,WANG L,WANG S.Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index.Transactions of the Chinese Society of Agricultural Engineering,2016,32(21):102-108.(in Chinese)
[13]秦占飞,常庆瑞,谢宝妮,申健.基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测.农业工程学报,2016,32(23):77-85.QIN Z F,CHANG Q R,XIE B N,SHEN J.Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district.Transactions of the Chinese Society of Agricultural Engineering,2016,32(23):77-85.(in Chinese)
[14]LIU H Y,ZHU H C,WANG P.Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyper-spectral data.International Journal of Remote Sensing,2017,38(8/10):2117-2134.
[15]N?SI R,VILJANEN N,KAIVOSOJA J,ALHONOJA K,HAKALAT,MARKELIN L,HONKAVAARA E.Estimating biomass and nitrogen amount of barley and grass using UAV and aircraft based spectral and photogrammetric 3D features.Remote Sensing,2018,10(7):1082.
[16]张智韬,边江,韩文霆,付秋萍,陈硕博,崔婷.剔除土壤背景的棉花水分胁迫无人机热红外遥感诊断.农业机械学报,2018,49(10):250-260.ZHANG Z T,BIAN J,HAN W T,FU Q P,CHEN S B,CUI T.Diagnosis of cotton water stress using unmanned aerial vehicle thermal infrared remote sensing after removing soil background.Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):250-260.(in Chinese)
[17]ZHU Y,YAO X,TIAN Y C,LIU X J,CAO W X.Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice.International Journal of Applied Earth Observation and Geoinformation,2008,10(1):1-10.
[18]CHEN P F.A comparison of two approaches for estimating the wheat nitrogen nutrition index using remote sensing.Remote Sensing,2015,7(4):4527-4548.
[19]ROUSE J W,HAAS R W,SCHELL J A,DEERING D W,HARLAN JC.Monitoring the vernal advancement and retrogradation(green wave effect)of natural vegetation.NASA/GSFCT Type III Final Report,USA:NASA,1974.
[20]PEARSON R L,MILLER L D.Remote mapping of standing crop biomass for estimation of the productivity of the Shortgrass Prairie,Pawnee National Grasslands,Colorado//Proceedings of the Eighth International Symposium on Remote Sensing of Environment.Ann Arbor,Michigan,USA,1972:1357-1381.
[21]HUETE A,JUSTICE C,LIU H.Development of vegetation and soil indices for MODIS-EOS.Remote Sensing of Environment,1994,49(3):224-234.
[22]BROGE N H,LEBLANC E.Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density.Remote Sensing of Environment,2001,76(2):156-172.
[23]QI J,CHEHBOUNI A,HUETE A R,KERR Y H,SOROOSHIAN S.A modified soil adjusted vegetation index.Remote Sensing of Environment,1994,48(2):119-126.
[24]RONDEAUX G,STEVEN M,BARET F.Optimization of soiladjusted vegetation indices.Remote Sensing of Environment,1996,55(2):95-107.
[25]HABOUDANE D,MILLER J R,PATTEY E,ZARCO-TEJADA P J,STRACHAN I B.Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies:Modeling and validation in the context of precision agriculture.Remote Sensing of Environment,2004,90(3):337-352.
[26]GITELSON A A,VI?A A,CIGANDA V,RUNDQUIST D C,ARKEBAUER T J.Remote estimation of canopy chlorophyll content in crops.Geophysical Research Letters,2005,32(8):93-114.
[27]XUE L H,CAO W X,LUO W H,DAI T B,ZHU Y.Monitoring leaf nitrogen status in rice with canopy spectral reflectance.Agronomy Journal,2004,96(1):135-142.
[28]YANG F,SUN J L,FANG H L,YAO Z F,ZHANG J H,ZHU Y Q,SONG K S,WANG Z M,HU M G.Comparison of different methods for corn LAI estimation over northeastern China.International Journal of Applied Earth Observation and Geoinformation,2012,18:462-471.
[29]LI J T,SHI Y Y,VEERANAMPALAYAM-SIVAKUMAR A N,SCHACHTMAN D P.Elucidating sorghum biomass,nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system.Frontiers in Plant Science,2018,9:1406.
[30]张东彦.基于高光谱成像技术的作物叶绿素信息诊断机理及方法研究[D].杭州:浙江大学,2012.ZHANG D Y.Diagnosis mechanism and methods of crop chlorophyll information based on hyperspectral imaging technology[D].Hangzhou:Zhejiang University,2012.(in Chinese)
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