无人机载多光谱遥感监测冬油菜氮素营养研究
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摘要
为探索无人机搭载多光谱相机对冬油菜冠层氮素营养状况监测的可行性,设置9种施氮水平的油菜试验小区,获取八叶期、十叶期、十二叶期和蕾薹期的多光谱影像,同步采样分析获取地上部生物量、叶片氮浓度和氮素积累量等氮营养指标。以宽波段植被指数和氮营养指标的相关性为基础,通过敏感性分析确定最佳指数,建立预测模型并进行精度验证。结果显示,宽波段植被指数与氮营养指标有极显著的相关性,不同生育期差异明显。其中,红光标准值和蓝光标准值在蕾薹期均与各氮营养指标相关关系最好,且敏感性因子的值小而稳定。进一步研究表明,叶片氮浓度、地上部生物量的氮素积累量三种指标均可用红光标准值和蓝光标准值建立的二次模型进行测算,决定系数R~2均大于0.85,模型精度较高,说明无人机多光谱遥感能有效辅助冬油菜氮素营养监测。
To use the filter-type multi-spectral camera mounted on the unmanned aerial vehicle(UAV) to dynamically monitor the nitrogen(N) nutrition of winter rapeseed, field experiment was conducted under 9 N application rates. The multi-spectral images of winter rapeseed canopy were obtained. Meanwhile, the conventional N monitoring parameters of aboveground biomass, leaf N concentration and N accumulation at 8-leaf period, 10-leaf period, 12-leaf period and bud period were taken. Based on the correlation between wide-band vegetation indices and nitrogen nutrition indices, the optimal vegetation indices were determined by sensitivity analysis. The models of nitrogen nutrition indices were calibrated and tested respectively, using training and test dataset. Results showed that broad-band vegetation indices had a significant correlation with N nutrition indices at each growth stage. Among them, normalized redness intensity(NRI1) and normalized blueness intensity(NBI) had the best correlation with N nutrition indices in bud stage. Small and stable noise equivalent(NE) values indicated the best vegetation indices for dynamic diagnosis of N nutrition in this crop. Further studies showed that the N concentration of leaves, aboveground biomass and nitrogen accumulation at bud stage could be estimated by quadratic model established by NRI1 and NBI with high accuracy. The study was demonstrated multi-spectral remote sensing of UAV, which was flexible and promising in N nutrition monitoring of winter rapeseed.
To use the filter-type multi-spectral camera mounted on the unmanned aerial vehicle(UAV) to dynamically monitor the nitrogen(N) nutrition of winter rapeseed, field experiment was conducted under 9 N application rates. The multi-spectral images of winter rapeseed canopy were obtained. Meanwhile, the conventional N monitoring parameters of aboveground biomass, leaf N concentration and N accumulation at 8-leaf period, 10-leaf period, 12-leaf period and bud period were taken. Based on the correlation between wide-band vegetation indices and nitrogen nutrition indices, the optimal vegetation indices were determined by sensitivity analysis. The models of nitrogen nutrition indices were calibrated and tested respectively, using training and test dataset. Results showed that broad-band vegetation indices had a significant correlation with N nutrition indices at each growth stage. Among them, normalized redness intensity(NRI1) and normalized blueness intensity(NBI) had the best correlation with N nutrition indices in bud stage. Small and stable noise equivalent(NE) values indicated the best vegetation indices for dynamic diagnosis of N nutrition in this crop. Further studies showed that the N concentration of leaves, aboveground biomass and nitrogen accumulation at bud stage could be estimated by quadratic model established by NRI1 and NBI with high accuracy. The study was demonstrated multi-spectral remote sensing of UAV, which was flexible and promising in N nutrition monitoring of winter rapeseed.
引文
[1] 李培武,杨湄,张文,等.我国油菜产品质量安全现状及对策[J].中国油料作物学报,2004,26(1):84-88.
[2] 刘代平,宋海星,刘强,等.不同施氮水平下油菜地上部生理特性研究[J].湖南农业大学学报(自然科学版),2008,34(1):100-104.
[3] 贾良良,范明生,张福锁,等.应用数码相机进行水稻氮营养诊断[J].光谱学与光谱分析,2009,29(8):2 176-2 179.
[4] 朱娟娟.玉米氮素营养无损诊断及水氮效应[D].杨凌:西北农林科技大学,2012.
[5] Blackmer T M,Schepers J S.Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn[J].J Produc Agr,1995,8(1):56-60.
[6] 孙俊,金夏明,毛罕平,等.基于高光谱图像的生菜叶片氮素含量预测模型研究[J].分析化学,2014,42(5):672-677.
[7] 张玲,陈新平,贾良良.基于无人机可见光遥感的夏玉米氮素营养动态诊断参数研究[J].植物营养与肥料学报,2018,24(1):261-269.
[8] 刘昌华,马文玉,陈志超,等.基于无人机遥感的冬小麦氮素营养诊断[J].河南理工大学学报(自然科学版),2018,37(3):45-53.
[9] 李红军,李佳珍,雷玉平,等.无人机搭载数码相机航拍进行小麦、玉米氮素营养诊断研究[J].中国生态农业学报,2017,25(12):1 832-1 841.
[10] 马明洋,许童羽,周云成,等.东北粳稻叶绿素相对含量的无人机高清影像检测方法[J].沈阳农业大学学报,2017(6):757-762.
[11] 李红军,张立周,陈曦鸣,等.应用数字图像进行小麦氮素营养诊断中图像分析方法的研究[J].中国生态农业学报,2011,19(1):155-159.
[12] 高林,杨桂军,李红军,等.基于无人机数码影像的冬小麦叶面积指数探测研究[J].中国生态农业学报,2016,24(9):1 254-1 264.
[13] Datt B.Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves[J].Int J Remote Sens,1999,20(14):19.
[14] Jordan C F.Derivation of leaf area from quality measurements of light on the forest floor[J].Ecology,1969,50:663-666.
[15] Gitelson A A,Kaufman Y J,Merzlyak M N.Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J].Remote Sens Environ,1996,58(3):289-298.
[16] Gitelson A A,Viňa A,Ciganda V,et al.Remote estimation of canopy chlorophyll content in crops[J].Geophys Res Lett,2005,32(8):93-114.
[17] Gillespie T W,Ostermann-Kelm S,Dong C,et al.Monitoring changes of NDVI in protected areas of southern California[J].Ecol Indic,2018,88:485-494.
[18] Yao X,Wang N,Liu Y,et al.Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery[J].Remote Sens,2017,9(12):1304.
[19] 李岚涛,马驿,魏全全,等.基于高光谱的冬油菜植株氮素积累量监测模型[J].农业工程学报,2015,31(20):147-156.
[20] Wang S Q,Li J,et al.Prediction of soil texture using FT-NIR spectroscopy and PXRF spectrometry with data fusion[J].Soil Science,2013,178(11):626-638.
[21] 魏全全,李岚涛,任涛,等.基于数字图像技术的冬油菜氮素营养诊断[J].中国农业科学,2015,48(19):3 877-3 886.
[22] Li X,Li Q,Yang T,et al.Responses of plant development,biomass and seed production of direct sown oilseed rape (Brassica napus) to nitrogen application at different stages in Yangtze River Basin[J].Field Crops Research,2016,194:12-20.
[23] Liu S S,Li L T,Gao W H,et al.Diagnosis of nitrogen status in winter oilseed rape (Brassica napus L.) using in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images[J].Comput Electron Agr,2018,151:185-195.
[24] 李岚涛,张萌,任涛,等.应用数字图像技术进行水稻氮素营养诊断[J].植物营养与肥料学报,2015,21(1):259-268.
[25] 魏全全.应用数字图像技术进行冬油菜氮素营养诊断的初步研究[D].武汉:华中农业大学,2016.
[26] Jay S,Baretb F,Dutartre D,et al.Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops[J].Remote Sens Environ,https://doi.org/10.1016/j.rse.2018.09.011.
[2] 刘代平,宋海星,刘强,等.不同施氮水平下油菜地上部生理特性研究[J].湖南农业大学学报(自然科学版),2008,34(1):100-104.
[3] 贾良良,范明生,张福锁,等.应用数码相机进行水稻氮营养诊断[J].光谱学与光谱分析,2009,29(8):2 176-2 179.
[4] 朱娟娟.玉米氮素营养无损诊断及水氮效应[D].杨凌:西北农林科技大学,2012.
[5] Blackmer T M,Schepers J S.Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn[J].J Produc Agr,1995,8(1):56-60.
[6] 孙俊,金夏明,毛罕平,等.基于高光谱图像的生菜叶片氮素含量预测模型研究[J].分析化学,2014,42(5):672-677.
[7] 张玲,陈新平,贾良良.基于无人机可见光遥感的夏玉米氮素营养动态诊断参数研究[J].植物营养与肥料学报,2018,24(1):261-269.
[8] 刘昌华,马文玉,陈志超,等.基于无人机遥感的冬小麦氮素营养诊断[J].河南理工大学学报(自然科学版),2018,37(3):45-53.
[9] 李红军,李佳珍,雷玉平,等.无人机搭载数码相机航拍进行小麦、玉米氮素营养诊断研究[J].中国生态农业学报,2017,25(12):1 832-1 841.
[10] 马明洋,许童羽,周云成,等.东北粳稻叶绿素相对含量的无人机高清影像检测方法[J].沈阳农业大学学报,2017(6):757-762.
[11] 李红军,张立周,陈曦鸣,等.应用数字图像进行小麦氮素营养诊断中图像分析方法的研究[J].中国生态农业学报,2011,19(1):155-159.
[12] 高林,杨桂军,李红军,等.基于无人机数码影像的冬小麦叶面积指数探测研究[J].中国生态农业学报,2016,24(9):1 254-1 264.
[13] Datt B.Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves[J].Int J Remote Sens,1999,20(14):19.
[14] Jordan C F.Derivation of leaf area from quality measurements of light on the forest floor[J].Ecology,1969,50:663-666.
[15] Gitelson A A,Kaufman Y J,Merzlyak M N.Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J].Remote Sens Environ,1996,58(3):289-298.
[16] Gitelson A A,Viňa A,Ciganda V,et al.Remote estimation of canopy chlorophyll content in crops[J].Geophys Res Lett,2005,32(8):93-114.
[17] Gillespie T W,Ostermann-Kelm S,Dong C,et al.Monitoring changes of NDVI in protected areas of southern California[J].Ecol Indic,2018,88:485-494.
[18] Yao X,Wang N,Liu Y,et al.Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery[J].Remote Sens,2017,9(12):1304.
[19] 李岚涛,马驿,魏全全,等.基于高光谱的冬油菜植株氮素积累量监测模型[J].农业工程学报,2015,31(20):147-156.
[20] Wang S Q,Li J,et al.Prediction of soil texture using FT-NIR spectroscopy and PXRF spectrometry with data fusion[J].Soil Science,2013,178(11):626-638.
[21] 魏全全,李岚涛,任涛,等.基于数字图像技术的冬油菜氮素营养诊断[J].中国农业科学,2015,48(19):3 877-3 886.
[22] Li X,Li Q,Yang T,et al.Responses of plant development,biomass and seed production of direct sown oilseed rape (Brassica napus) to nitrogen application at different stages in Yangtze River Basin[J].Field Crops Research,2016,194:12-20.
[23] Liu S S,Li L T,Gao W H,et al.Diagnosis of nitrogen status in winter oilseed rape (Brassica napus L.) using in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images[J].Comput Electron Agr,2018,151:185-195.
[24] 李岚涛,张萌,任涛,等.应用数字图像技术进行水稻氮素营养诊断[J].植物营养与肥料学报,2015,21(1):259-268.
[25] 魏全全.应用数字图像技术进行冬油菜氮素营养诊断的初步研究[D].武汉:华中农业大学,2016.
[26] Jay S,Baretb F,Dutartre D,et al.Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops[J].Remote Sens Environ,https://doi.org/10.1016/j.rse.2018.09.011.
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