基于高光谱的夏玉米冠层SPAD值监测研究
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摘要
开展夏玉米冠层SPAD值监测技术研究,建立叶绿素含量与敏感波段、光谱指数间的定量关系模型,以促进高光谱技术在玉米快速、无损长势监测及水肥精准管理的应用。以小型蒸渗仪夏玉米光谱反射率与植株冠层SPAD值的监测为基础,研究了夏玉米植株冠层光谱信息与SPAD值的响应关系,并优选出监测夏玉米冠层SPAD值的敏感波段与最优光谱指数。结果表明:夏玉米冠层光谱反射率在可见光波段随玉米冠层SPAD值增加而下降,在近红外波段却与之相反;采用原始光谱反射率、一阶微分光谱监测夏玉米冠层SPAD值的最敏感波段分别为700,690nm,与SPAD值的相关性分别为-0.498(p<0.05)和-0.538(p<0.01);而根据多元逐步回归分析获得的最优波段组合由405,408,700nm波段构成;从已报道的73个光谱指数中筛选出与夏玉米冠层SPAD值相关性较高的(SDr-SDb)/(SDr+SDb)、MCARI∥OSAVI、TCARI/OSAVI、SDr/SDb和MTCI等5个光谱指数,光谱指数(SDr-SDb)/(SDr+SDb)与SPAD值的相关性在各生育期均达极显著正相关,且在全生育期相关系数高达0.697(p<0.01),进一步优选出监测夏玉米冠层SPAD值最适宜的光谱指数为(SDr-SDb)/(SDr+SDb);在基于敏感波段、光谱指数和最优波段组合建立的夏玉米SPAD值的回归模型中,按照模拟效果由高到低排序依次为最优波段组合、光谱指数、原始光谱反射率、一阶微分光谱,其决定系数分别为0.777,0.539,0.351,0.282;推荐以(SDr-SDb)/(SDr+SDb)指数构建的二次多项式模型与基于405,408,700nm波段组合建立的线性回归监测模型作为夏玉米植株冠层SPAD值光谱监测适宜模型,且R2大于0.539,RMSE及MAE分别小于6.194和4.702。
This study was an exploration into monitoring technology of summer corn canopy SPAD values.A small lysimeter was used in the experiment to monitor spectral reflectivity of summer corn and SPAD values of plant canopy,which provided the basis for investigation of the responsive relationship between canopy spectral information and SPAD values.From this relationship,the sensitive wave band and the optimum spectral index were determined for the SPAD values of the summer corn under investigation.The findings suggested that the canopy spectral reflectivity decreased with the increase of SPAD values in the visible light band,but increased with the increase of SPAD in the near infrared band.The band to which SPAD was most sensitive was 700 nm and 690 nm when monitoring the original spectrum and the first differential spectrum respectively and the correlations with SPAD values were-0.498(p <0.05)and-0.538(p <0.01).The multivariate stepwise regression analysis found an optimum band combination of 405 nm,408nm,and 700 nm.From 73 published spectral indices,five indices,i.e.,(SDr-SDb)/(SDr+SDb),MCARI//OSAVI,TCARI/OSAVI,SDr/SDb,and MTCI,were chosen because they produced a higher correlation with SPAD values of summer corn canopy.Among the five indices,the most suitable index was found to be(SDrSDb)/(SDr+SDb),and the correlation coefficient was as high as 0.697(p <0.01)in the whole growth period.A combinational regression model of SPAD,built from sensitive band,spectral index,and optimum band,produced the following simulation performances in descending order:optimum band combination,spectral index,original spectral reflectivity,and first differential spectrum.A quadratic polynomial model built from(SDrSDb)/(SDr+SDb)index,and a combinational linear regression model built from 405 nm,408nm,and 700 nm bands,were recommended as suitable models for spectral monitoring of summer corn canopy SPAD values.
This study was an exploration into monitoring technology of summer corn canopy SPAD values.A small lysimeter was used in the experiment to monitor spectral reflectivity of summer corn and SPAD values of plant canopy,which provided the basis for investigation of the responsive relationship between canopy spectral information and SPAD values.From this relationship,the sensitive wave band and the optimum spectral index were determined for the SPAD values of the summer corn under investigation.The findings suggested that the canopy spectral reflectivity decreased with the increase of SPAD values in the visible light band,but increased with the increase of SPAD in the near infrared band.The band to which SPAD was most sensitive was 700 nm and 690 nm when monitoring the original spectrum and the first differential spectrum respectively and the correlations with SPAD values were-0.498(p <0.05)and-0.538(p <0.01).The multivariate stepwise regression analysis found an optimum band combination of 405 nm,408nm,and 700 nm.From 73 published spectral indices,five indices,i.e.,(SDr-SDb)/(SDr+SDb),MCARI//OSAVI,TCARI/OSAVI,SDr/SDb,and MTCI,were chosen because they produced a higher correlation with SPAD values of summer corn canopy.Among the five indices,the most suitable index was found to be(SDrSDb)/(SDr+SDb),and the correlation coefficient was as high as 0.697(p <0.01)in the whole growth period.A combinational regression model of SPAD,built from sensitive band,spectral index,and optimum band,produced the following simulation performances in descending order:optimum band combination,spectral index,original spectral reflectivity,and first differential spectrum.A quadratic polynomial model built from(SDrSDb)/(SDr+SDb)index,and a combinational linear regression model built from 405 nm,408nm,and 700 nm bands,were recommended as suitable models for spectral monitoring of summer corn canopy SPAD values.
引文
[1]房贤一,朱西存,王凌,等.基于高光谱的苹果盛果期冠层叶绿素含量监测研究[J].中国农业科学,2013,46(16):3504-3513.
[2]李云梅,倪绍祥,王秀珍.线性回归模型估算水稻叶片叶绿素含量的适宜性分析[J].遥感学报,2003,7(5):364-371.
[3]高鑫,高聚林,于晓芳,等.基于不同玉米品种叶片高光谱的SPAD值估测模型研究[J].玉米科学2016,24(2):108-114.
[4] Daughtry C S T,Walthall C L,Kim M S,et al.Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J].Remote Sensing of Environment,2000,74(2):229-239.
[5]张俊华,张佳宝,贾科利.不同施肥条件下夏玉米光谱特征与叶绿素含量和LAI的相关性[J].西北植物学报,2008,28(7):1461-1467.
[6]陈志强,王磊,白由路,等.整个生育期玉米叶片SPAD高光谱预测模型研究[J].光谱学与光谱分析2013,33(10):2838-2842.
[7]陈志强.不同氮素水平下玉米叶片的高光谱响应及其诊断[D].北京:中国农业科学院,2013.
[8] Hunt E R,Doraiswamy P C,Mc Murtrey J E,et al.A visible band index for remote sensing leaf chlorophyll content at the canopy scale[J].International Journal of Applied Earth Observations&Geoinformation,2013,21(1):103-112.
[9] Gitelson A A,Vi1a A,Ciganda V,et al.Remote estimation of canopy chlorophyll content in crops[J].Geophysical Research Letters,2005,32(8):93-114.
[10]张晓华,常庆瑞,章曼,等.基于高光谱植被指数的西北玉米不同时期叶绿素含量估测[J].中国农业大学学报,2015,20(4):75-81.
[11]章曼,常庆瑞,张晓华,等.不同施肥条件下水稻冠层光谱特征与叶绿素含量的相关性[J].西北农业学报,2015,24(11):49-56.
[12]王瑞,陈永忠,陈隆升,等.油茶叶片SPAD值与叶绿素含量的相关分析[J].中南林业科技大学学报2013,33(2):77-80.
[13]贺佳.冬小麦不同生育时期生态生理参数的高光谱遥感监测模型[D].陕西杨凌:西北农林科技大学,2015.
[14]姚付启.冬小麦高光谱特征及其生理生态参数估算模型研究[D].陕西杨凌:西北农林科技大学,2012.
[15]蒋桂英.新疆棉花主要栽培生理指标的高光谱定量提取与应用研究[D].长沙:湖南农业大学,2004.
[16]李媛媛.基于地物光谱仪与成像光谱仪耦合的玉米生长信息监测研究[D].陕西杨凌:西北农林科技大学,2017.
[17] 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):416-426.
[18] Dash J,Curran P J.Evaluation of the MERIS terrestrial chlorophyll index(MTCI)[J].Advances in Space Research,2007,39(1):100-104.
[19]孙林,程丽娟.植被叶片生化组分的光谱响应特征分析[J].光谱学与光谱分析,2010,30(11):3031-3035.
[20]彭致功,张宝忠,刘钰,等.华北典型区冬小麦区域耗水模拟与灌溉制度优化[J].农业机械学报2017,48(11):238-246.
[2]李云梅,倪绍祥,王秀珍.线性回归模型估算水稻叶片叶绿素含量的适宜性分析[J].遥感学报,2003,7(5):364-371.
[3]高鑫,高聚林,于晓芳,等.基于不同玉米品种叶片高光谱的SPAD值估测模型研究[J].玉米科学2016,24(2):108-114.
[4] Daughtry C S T,Walthall C L,Kim M S,et al.Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J].Remote Sensing of Environment,2000,74(2):229-239.
[5]张俊华,张佳宝,贾科利.不同施肥条件下夏玉米光谱特征与叶绿素含量和LAI的相关性[J].西北植物学报,2008,28(7):1461-1467.
[6]陈志强,王磊,白由路,等.整个生育期玉米叶片SPAD高光谱预测模型研究[J].光谱学与光谱分析2013,33(10):2838-2842.
[7]陈志强.不同氮素水平下玉米叶片的高光谱响应及其诊断[D].北京:中国农业科学院,2013.
[8] Hunt E R,Doraiswamy P C,Mc Murtrey J E,et al.A visible band index for remote sensing leaf chlorophyll content at the canopy scale[J].International Journal of Applied Earth Observations&Geoinformation,2013,21(1):103-112.
[9] Gitelson A A,Vi1a A,Ciganda V,et al.Remote estimation of canopy chlorophyll content in crops[J].Geophysical Research Letters,2005,32(8):93-114.
[10]张晓华,常庆瑞,章曼,等.基于高光谱植被指数的西北玉米不同时期叶绿素含量估测[J].中国农业大学学报,2015,20(4):75-81.
[11]章曼,常庆瑞,张晓华,等.不同施肥条件下水稻冠层光谱特征与叶绿素含量的相关性[J].西北农业学报,2015,24(11):49-56.
[12]王瑞,陈永忠,陈隆升,等.油茶叶片SPAD值与叶绿素含量的相关分析[J].中南林业科技大学学报2013,33(2):77-80.
[13]贺佳.冬小麦不同生育时期生态生理参数的高光谱遥感监测模型[D].陕西杨凌:西北农林科技大学,2015.
[14]姚付启.冬小麦高光谱特征及其生理生态参数估算模型研究[D].陕西杨凌:西北农林科技大学,2012.
[15]蒋桂英.新疆棉花主要栽培生理指标的高光谱定量提取与应用研究[D].长沙:湖南农业大学,2004.
[16]李媛媛.基于地物光谱仪与成像光谱仪耦合的玉米生长信息监测研究[D].陕西杨凌:西北农林科技大学,2017.
[17] 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):416-426.
[18] Dash J,Curran P J.Evaluation of the MERIS terrestrial chlorophyll index(MTCI)[J].Advances in Space Research,2007,39(1):100-104.
[19]孙林,程丽娟.植被叶片生化组分的光谱响应特征分析[J].光谱学与光谱分析,2010,30(11):3031-3035.
[20]彭致功,张宝忠,刘钰,等.华北典型区冬小麦区域耗水模拟与灌溉制度优化[J].农业机械学报2017,48(11):238-246.
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