基于无人机多光谱影像的夏玉米叶片氮含量遥感估测
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摘要
利用无人机平台搭载多光谱相机组成的遥感监测系统在农业上已取得了一些成果,但利用无人机多光谱影像开展作物氮素估测研究少有尝试。基于此,该文利用国家精准农业基地2017年夏玉米3个关键生育期无人机多光谱影像和田间实测叶片氮含量数据,开展夏玉米叶片氮素含量的无人机遥感估测研究。对该研究选用的15个光谱变量,通过相关性分析解析光谱变量与LNC的相关关系,筛选出对玉米叶片氮素含量敏感的光谱变量;应用后向逐步回归方法分析不同变量指数下估测精度变化,最终确定不同生育期夏玉米LNC估测的光谱变量,实现对夏玉米叶片氮含量的较高精度监测。研究发现:1)在3个生育时期,GRE和GNDVI与LNC都有很强的相关性,表明绿波段可以很好地进行夏玉米生物理化参数的反演;2)在喇叭口期和灌浆期,OSAVI、SAVI与LNC具有高度相关性,证明在夏玉米生长前期和后期选择控制土壤因素的光谱变量可以提高对氮素估测的能力。在筛选最优光谱变量建模过程中发现,喇叭口期选取5个光谱变量(GNDVI、GRE、OSAVI、REG、SAVI)建模效果最好,估测模型的R~2、RMSE和nRMSE分别为0.63、27.63%、11.62%;抽雄吐丝期选取6个光谱变量(REG、GRE、GNDVI、MNLI、RED、NDVI)建模效果最好,估测模型的R~2、RMSE和n RMSE分别为0.64、20.50%、7.80%;灌浆期选取5个光谱变量(GRE、GNDVI、RED、NDVI、OSAVI)建模效果最好,估测模型的R~2、RMSE和n RMSE分别为0.56、31.12%、12.71%;在不同生育期选取最优光谱变量进行夏玉米LNC估测具有很好的效果。应用无人机多光谱遥感影像数据可以很好地监测田块尺度夏玉米LNC的空间分布,可为玉米田间氮素精准管理提供空间决策服务信息支持。
At present, the remote sensing monitoring system composed of UAV platform and multi-spectral camera has achieved some results in agriculture. However, there are few attempts to use UAV multi-spectral image to estimate crop nitrogen. Based on this, the multi-spectral images were acquired by UAV and leaf nitrogen contents(LNC) were measured in the National Precision Agriculture Base in 2017 to carry out the estimation research on the nitrogen content of summer maize leaves in this paper. Crop LNC estimation model were constructed mostly based on single spectral variable in traditional research methods, but the model constructed by single spectral variable were easy to be saturated, while excessive selection of variables may lead to over-fitting. The stepwise regression method is a multivariate regression analysis method, which is simple and easy to perform. The obtained regression equation has fewer variables and retains the advantages of the most significant important variables. Therefore, the stepwise regression model is used to estimate the nitrogen content of leaves. In this study, 48 sets of sample data were obtained in 3 growth stages. Firstly, the data were preprocessed, and the preprocessing of UAV multispectral data included image mosaic, radiation calibration and geometric correction. Secondly, 15 spectral variables were selected to analyze the correlation with LNC, and then the spectral variables sensitive to LNC at different growth stages were screened out. Finally, the backward stepwise regression method was used to analyze the change of estimation accuracy under different variables, and the spectral variables to estimate LNC in different growth stages were determined to achieve higher precision monitoring of LNC in summer maize. It can be found that: 1) In the 3 growth stages, the green band reflectance and the green normalized difference vegetation index(GNDVI) constructed by the green band had strong correlation with LNC, indicating that the green band can perform the inversion of LNC in summer maize; 2) The optimal soil adjustment vegetation index(OSAVI), soil adjustment vegetation index(SAVI) and LNC were highly correlated in trumpet stage and filling stage, which proved that the selection of spectral variables reflecting soil factors in early and late growth stage of summer maize can improve inversion accuracy of the nitrogen content. Considering the evaluation index and simple practicability of the estimation model, 5 spectral variables were selected according to the adjusted determination coefficient(R~2 adj) in the trumpet stage, 6 spectral variables were selected in the anthesis silking stage, and 5 spectral variables were selected in the filling stage to construct the model. In the trumpet stage, the R~2, root mean square error(RMSE)and normalized RMSE(nRMSE) of the estimation model were 0.63, 27.63% and 11.62%; In the anthesis silking stage, that were 0.64, 20.50% and 7.80%; In the filling stage, that were 0.56, 31.12% and 12.71%; It can be found that the application of UAV multi-spectral remote sensing image data can well monitor the spatial distribution of LNC in field-scale summer maize, and provide spatial decision service information support for corn field precision management.
At present, the remote sensing monitoring system composed of UAV platform and multi-spectral camera has achieved some results in agriculture. However, there are few attempts to use UAV multi-spectral image to estimate crop nitrogen. Based on this, the multi-spectral images were acquired by UAV and leaf nitrogen contents(LNC) were measured in the National Precision Agriculture Base in 2017 to carry out the estimation research on the nitrogen content of summer maize leaves in this paper. Crop LNC estimation model were constructed mostly based on single spectral variable in traditional research methods, but the model constructed by single spectral variable were easy to be saturated, while excessive selection of variables may lead to over-fitting. The stepwise regression method is a multivariate regression analysis method, which is simple and easy to perform. The obtained regression equation has fewer variables and retains the advantages of the most significant important variables. Therefore, the stepwise regression model is used to estimate the nitrogen content of leaves. In this study, 48 sets of sample data were obtained in 3 growth stages. Firstly, the data were preprocessed, and the preprocessing of UAV multispectral data included image mosaic, radiation calibration and geometric correction. Secondly, 15 spectral variables were selected to analyze the correlation with LNC, and then the spectral variables sensitive to LNC at different growth stages were screened out. Finally, the backward stepwise regression method was used to analyze the change of estimation accuracy under different variables, and the spectral variables to estimate LNC in different growth stages were determined to achieve higher precision monitoring of LNC in summer maize. It can be found that: 1) In the 3 growth stages, the green band reflectance and the green normalized difference vegetation index(GNDVI) constructed by the green band had strong correlation with LNC, indicating that the green band can perform the inversion of LNC in summer maize; 2) The optimal soil adjustment vegetation index(OSAVI), soil adjustment vegetation index(SAVI) and LNC were highly correlated in trumpet stage and filling stage, which proved that the selection of spectral variables reflecting soil factors in early and late growth stage of summer maize can improve inversion accuracy of the nitrogen content. Considering the evaluation index and simple practicability of the estimation model, 5 spectral variables were selected according to the adjusted determination coefficient(R~2 adj) in the trumpet stage, 6 spectral variables were selected in the anthesis silking stage, and 5 spectral variables were selected in the filling stage to construct the model. In the trumpet stage, the R~2, root mean square error(RMSE)and normalized RMSE(nRMSE) of the estimation model were 0.63, 27.63% and 11.62%; In the anthesis silking stage, that were 0.64, 20.50% and 7.80%; In the filling stage, that were 0.56, 31.12% and 12.71%; It can be found that the application of UAV multi-spectral remote sensing image data can well monitor the spatial distribution of LNC in field-scale summer maize, and provide spatial decision service information support for corn field precision management.
引文
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[2]王纪华,李存军,刘良云,等.作物品质遥感监测预报研究进展[J].中国农业科学,2008,41(9):2633-2640.Wang Jihua,Li Cunjun,Liu Liangyun,et al.Progress of remote sensing monitoring and forecasting crop quality[J].Scientia Agricultura Sinica,2008,41(9):2633-2640.(in Chinese with English abstract)
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[4]王莉雯,卫亚星.植被氮素浓度高光谱反演研究进展[J].光谱学与光谱分析,2013,33(10):2823-2827.Wang Liwen,Wei Yaxing.Progress in inversion of vegetation nitrogen concentration by hyperspectral remote sensing[J].Spectroscopy and Spectral Analysis,2013,33(10):2823-2827.(in Chinese with English abstract)
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[6]陈仲新,任建强,唐华俊,等.农业遥感应用进展与展望[J].遥感学报,2016,20(5):748-767.Chen Zhongxin,Ren Jianqiang,Tang Huajun,et al.Progress and perspectives on agricultural remote sensing research and applications in China[J].Journal of Remote Sensing,2016,20(5):748-767.(in Chinese with English abstract)
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[8]Aasen H,Burkart A,Bolten A,et al.Generating 3Dhyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring:From camera calibration to quality assurance[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,108:245-259.
[9]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(10):74-82.Pei Haojie,Feng Haikuan,Li Changchun,et al.Remote sensing monitoring of winter wheat growth with UAV based on comprehensive index[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(10):74-82.(in Chinese with English abstract)
[10]张春兰,杨贵军,李贺丽,等.基于随机森林算法的冬小麦叶面积指数遥感反演研究[J].中国农业科学,2018,51(5):855-867.Zhang Chunlan,Yang Guijun,Li Heli,et al.Remote sensing inversion of leaf area index of winter wheat based on random forest algorithm[J].Scientia Agricultura Sinica,2018,51(5):855-867.(in Chinese with English abstract)
[11]陈鹏飞,李刚,石雅娇,等.一款无人机高光谱传感器的验证及其在玉米叶面积指数反演中的应用[J].中国农业科学,2018,51(8):1464-1474.Chen Pengfei,Li Gang,Shi Yajiao,et al.Validation of an unmanned aerial vehicle hyperspectral sensor and its application in maize leaf area index estimation[J].Scientia Agricultura Sinica,2018,51(8):1464-1474.(in Chinese with English abstract)
[12]田明璐,班松涛,常庆瑞,等.基于低空无人机成像光谱仪影像估算棉花叶面积指数[J].农业工程学报,2016,32(21):102-108.Tian Minglu,Ban Songtao,Chang Qingrui,et al.Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(21):102-108.(in Chinese with English abstract)
[13]王利民,刘佳,杨玲波,等.基于无人机影像的农情遥感监测应用[J].农业工程学报,2013,29(9):136-145.Wang Limin,Liu Jia,Yang Lingbo,et al.Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(9):136-145.(in Chinese with English abstract)
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