不同宽窄波段组合的光谱参量对夏玉米穗位叶氮素含量估测
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摘要
【目的】利用敏感波段构建适宜的植被指数,对于提高光谱技术诊断作物营养状况的精确度具有十分重要的意义。【方法】采用单因素随机区组设计的方法,通过设置5种不同施氮处理,研究夏玉米在吐丝期穗位叶光谱反射率与氮素含量的关系,进而比较了前人的植被指数与构建的8种不同形式宽窄波段组合植被指数的优越性,并对构建的氮素含量估测模型进行精度验证。【结果】穗位叶原始光谱反射率在近红外波段(700～1000 nm)随施氮量增加而升高,与氮素含量的变化表现一致;一阶导数光谱的红边位置随施氮量增加依次为698、703、709、714和714 nm,出现"红移"现象;利用氮素敏感波段构建宽窄波段组合的植被指数与叶片氮含量进行相关性分析,优于前人所采用的植被指数;植被指数[R(800-900)-R(692-729)]/R711和植被指数[R(800-900)+R(650-670)]/R711构建的乘幂函数估测模型检验精度较高,R~2和RMSE分别为0.92和0.09。【结论】利用氮素敏感波段构建的宽窄波段组合植被指数,提高了光谱参量与氮素含量的相关性,可以实现对夏玉米吐丝期氮素营养的诊断。
【Objective】Constructing a suitable vegetation index using sensitive bands is of great significance for improving the accuracy of spectroscopy in the diagnosis of crop nutritional status.【Method】The single factor randomized block design method was used to determine the relationship between spectral reflectance and nitrogen content in the ear leaf of the summer maize during the silking period by setting five different nitrogen treatments. The vegetation index and the advantages of the eight different forms of the combination vegetation index constructed were used to verify the accuracy of the con-structed nitrogen content estimation model in this study.【Result】The original spectral reflectance of the panicle leaf in the near-infrared band(700-1 000 nm)increased with the increase of nitrogen application,which was consistent with the change of nitrogen content. The position of the red edge of the first derivative spectrum increases with the amount of nitrogen applied to 698 nm,703 nm,709 nm,714 nm and 714 nm,showing a ′red shift′ phenomenon. Correlation analysis between the vegetation index and the nitrogen content of the leaves using the nitrogen-sensitive band to construct a wide and narrow band combination is better than the vegetation index used by the predecessors. The vegetation index [R(800-900)-R(692-729)]/R711 and [R(800-900)+R(650-670)]/R711 by the power model estimation model constructed has higher accuracy,R~2 and RMSE are 0.92 and 0.09,respectively.【Conclusion】The use of wide and narrow band combination vegetation index can improve the correlation between the spectral parameters and nitrogen content,and can be used to diagnose the nitrogen nutrition of summer corn in the silking period.
【Objective】Constructing a suitable vegetation index using sensitive bands is of great significance for improving the accuracy of spectroscopy in the diagnosis of crop nutritional status.【Method】The single factor randomized block design method was used to determine the relationship between spectral reflectance and nitrogen content in the ear leaf of the summer maize during the silking period by setting five different nitrogen treatments. The vegetation index and the advantages of the eight different forms of the combination vegetation index constructed were used to verify the accuracy of the con-structed nitrogen content estimation model in this study.【Result】The original spectral reflectance of the panicle leaf in the near-infrared band(700-1 000 nm)increased with the increase of nitrogen application,which was consistent with the change of nitrogen content. The position of the red edge of the first derivative spectrum increases with the amount of nitrogen applied to 698 nm,703 nm,709 nm,714 nm and 714 nm,showing a ′red shift′ phenomenon. Correlation analysis between the vegetation index and the nitrogen content of the leaves using the nitrogen-sensitive band to construct a wide and narrow band combination is better than the vegetation index used by the predecessors. The vegetation index [R(800-900)-R(692-729)]/R711 and [R(800-900)+R(650-670)]/R711 by the power model estimation model constructed has higher accuracy,R~2 and RMSE are 0.92 and 0.09,respectively.【Conclusion】The use of wide and narrow band combination vegetation index can improve the correlation between the spectral parameters and nitrogen content,and can be used to diagnose the nitrogen nutrition of summer corn in the silking period.
引文
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[10]林毅,李倩,王宏博,等.干旱条件下春玉米高光谱特征及土壤含水量反演[J].生态学杂志,2016,35(5):1323-1329.
[11]王磊,白由路,卢艳丽,等.不同形式的光谱参量对春玉米氮素营养诊断的比较[J].农业工程学报,2010,26(2):218-223.
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[13]梁惠平,刘湘南.玉米氮营养指数的高光谱计算模型[J].农业工程学报,2010,26(1):250-255.
[14]MISTELE B,GUTSER R,SCHMIDHALTER U,et al.Validation of field-scaled spectral measurements of the nitrogen status in winter wheat[C]//Proceedings of the 7th International Conference on Precision Agriculture and Other Precision Resources Management,July 25-28,2004,Hyatt Regency,Minneapolis.MN,USA:2004.
[15]王克如,潘文超,李少昆,等.不同施氮量棉花冠层高光谱特征研究[J].光谱学与光谱分析,2011,31(7):1868-1872.
[16]DANIELA S,MIRCO B,PIETROALESSANDRO B,et al.Plant nitrogen concentration in paddy rice from field canopy hyperspec tral radiometry[J].Field Crops Research,2009,111(1/2):119-129.
[17]靳华安,刘殿伟,宋开山,等.宽波段与窄波段植被指数估算大豆LAI对比研究[J].土壤与作物,2007,23(4):503-508.
[18]刘玉琴,沙晋明,余涛,等.基于宽波段和窄波段植被指数的草地LAI反演对比研究[J].遥感技术与应用,2014,29(4):587-593.
[19]李粉玲,常庆瑞,申健,等.模拟多光谱卫星宽波段反射率的冬小麦叶片氮含量估算[J].农业机械学报,2016,47(2):302-308.
[20]李岚涛,李静,明金,等.冬油菜叶面积指数高光谱监测最佳波宽与有效波段研究[J].农业机械学报,2018,49(2):156-165.
[21]SIMS D A,GAMON J A.Relationships between leaf pigment content and spectral reflectance across a wide range of species,leaf structures and developmental stages[J].Remote Sensing of Environment,2002,81(2):337-354.
[22]PENUELAS J,BARET F,FILELLA I.Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance[J].Photosynthetica,1995,31(2):221-230.
[23]JIN X,LI Z,FENG H,et al.Newly combined spectral indices to improve estimation of total leaf chlorophyll content in cotton[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2017,7(11):4589-4600.
[24]DASH J,CURRAN P J.Evaluation of the MERIS terrestrial chlorophyll index(MTCI)[J].Advances in Space Research,2007,39(1):100-104.
[25]HABOUDANE D,MILLER J R,TREMBLAY N,et al.Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agricultur e[J].Remote Sensing of Environment,2002,81(2):416-426.
[26]赵靓,侯振安,黄婷,等.氮肥用量对玉米产量和养分吸收的影响[J].新疆农业科学,2014,51(2):275-283.
[27]吴永成,尹传春,董云章,等.施氮方式对川中丘陵区玉米产量和氮肥利用的影响[J].四川农业大学学报,2012,30(1):1-6.
[28]胡珍珠,潘存德,肖冰,等.基于光谱特征参量的核桃叶片氮素含量估测模型[J].农业工程学报,2015,31(9):180-186.
[29]冯伟,朱艳,姚霞,等.利用红边特征参数监测小麦叶片氮素积累状况[J].农业工程学报,2009,25(11):194-201.
[30]尚艳,常庆瑞,刘秀英,等.关中地区小麦冠层光谱与氮素的定量关系[J].西北农林科技大学学报(自然科学版),2016,44(5):38-44.
[31]刘冰峰,李军,赵刚峰,等.夏玉米叶片全氮含量高光谱遥感估算模型研究[J].植物营养与肥料学报,2012,18(4):813-824.
[2]王磊,白由路.不同氮处理春玉米叶片光谱反射率与叶片全氮和叶绿素含量的相关研究[J].中国农业科学,2005,38(11):2268-2276.
[3]裴浩杰,冯海宽,李长春,等.基于综合指标的冬小麦长势无人机遥感监测[J].农业工程学报,2017,33(20):74-82.
[4]王利民,刘佳,邵杰,等.基于高光谱的春玉米大斑病害遥感监测指数选择[J].农业工程学报,2017,33(5):170-177.
[5]陈香,李民赞,孙红,等.基于透射光谱的玉米叶片水分含量快速检测[J].农业工程学报,2017,33(1):137-142.
[6]张杰,臧贺藏,李国强,等.实时氮肥调控对夏玉米产量和氮素利用效率的影响[J].玉米科学,2016,24(1):123-129.
[7]张良培,郑兰芬,童庆禧.利用高光谱对生物变量进行估计[J].遥感学报,1997,1(2):111-114.
[8]陈志强,王磊,白由路,等.玉米叶片的光谱响应及其氮素含量预测研究[J].光谱学与光谱分析,2013,33(4):1066-1070.
[9]王磊,白由路,卢艳丽,等.基于光谱分析的玉米氮素营养诊断[J].植物营养与肥料学报,2011,17(2):333-340.
[10]林毅,李倩,王宏博,等.干旱条件下春玉米高光谱特征及土壤含水量反演[J].生态学杂志,2016,35(5):1323-1329.
[11]王磊,白由路,卢艳丽,等.不同形式的光谱参量对春玉米氮素营养诊断的比较[J].农业工程学报,2010,26(2):218-223.
[12]ATZBERGER C,JARMER T,SCHLERF M,et al.Spectroradio-metric determination of wheat bio-physical variables.Comparison of differentemprical-statistical approaches[C]//Remote Sensing in Transitions,January,2004,Proc.23rdEARSeL symposium,IEEE,Belgium.Rotterdam:Millpress,2004:463-469.
[13]梁惠平,刘湘南.玉米氮营养指数的高光谱计算模型[J].农业工程学报,2010,26(1):250-255.
[14]MISTELE B,GUTSER R,SCHMIDHALTER U,et al.Validation of field-scaled spectral measurements of the nitrogen status in winter wheat[C]//Proceedings of the 7th International Conference on Precision Agriculture and Other Precision Resources Management,July 25-28,2004,Hyatt Regency,Minneapolis.MN,USA:2004.
[15]王克如,潘文超,李少昆,等.不同施氮量棉花冠层高光谱特征研究[J].光谱学与光谱分析,2011,31(7):1868-1872.
[16]DANIELA S,MIRCO B,PIETROALESSANDRO B,et al.Plant nitrogen concentration in paddy rice from field canopy hyperspec tral radiometry[J].Field Crops Research,2009,111(1/2):119-129.
[17]靳华安,刘殿伟,宋开山,等.宽波段与窄波段植被指数估算大豆LAI对比研究[J].土壤与作物,2007,23(4):503-508.
[18]刘玉琴,沙晋明,余涛,等.基于宽波段和窄波段植被指数的草地LAI反演对比研究[J].遥感技术与应用,2014,29(4):587-593.
[19]李粉玲,常庆瑞,申健,等.模拟多光谱卫星宽波段反射率的冬小麦叶片氮含量估算[J].农业机械学报,2016,47(2):302-308.
[20]李岚涛,李静,明金,等.冬油菜叶面积指数高光谱监测最佳波宽与有效波段研究[J].农业机械学报,2018,49(2):156-165.
[21]SIMS D A,GAMON J A.Relationships between leaf pigment content and spectral reflectance across a wide range of species,leaf structures and developmental stages[J].Remote Sensing of Environment,2002,81(2):337-354.
[22]PENUELAS J,BARET F,FILELLA I.Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance[J].Photosynthetica,1995,31(2):221-230.
[23]JIN X,LI Z,FENG H,et al.Newly combined spectral indices to improve estimation of total leaf chlorophyll content in cotton[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2017,7(11):4589-4600.
[24]DASH J,CURRAN P J.Evaluation of the MERIS terrestrial chlorophyll index(MTCI)[J].Advances in Space Research,2007,39(1):100-104.
[25]HABOUDANE D,MILLER J R,TREMBLAY N,et al.Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agricultur e[J].Remote Sensing of Environment,2002,81(2):416-426.
[26]赵靓,侯振安,黄婷,等.氮肥用量对玉米产量和养分吸收的影响[J].新疆农业科学,2014,51(2):275-283.
[27]吴永成,尹传春,董云章,等.施氮方式对川中丘陵区玉米产量和氮肥利用的影响[J].四川农业大学学报,2012,30(1):1-6.
[28]胡珍珠,潘存德,肖冰,等.基于光谱特征参量的核桃叶片氮素含量估测模型[J].农业工程学报,2015,31(9):180-186.
[29]冯伟,朱艳,姚霞,等.利用红边特征参数监测小麦叶片氮素积累状况[J].农业工程学报,2009,25(11):194-201.
[30]尚艳,常庆瑞,刘秀英,等.关中地区小麦冠层光谱与氮素的定量关系[J].西北农林科技大学学报(自然科学版),2016,44(5):38-44.
[31]刘冰峰,李军,赵刚峰,等.夏玉米叶片全氮含量高光谱遥感估算模型研究[J].植物营养与肥料学报,2012,18(4):813-824.