Li Chang Chun, Chen Peng, Lu Guo Zheng, Ma Chun Yan, Ma Xiao Xiao, Wang Shuang Ting
Henan Polytechnic University, Jiaozuo 454000, Henan, China.
Collaborative Innovation Center of Beidou Navigation Satellite System Research Application, Zhengzhou 450001, China.
Ying Yong Sheng Tai Xue Bao. 2018 Apr;29(4):1225-1232. doi: 10.13287/j.1001-9332.201804.020.
Nitrogen balance index (NBI) is one of the important indicators for crop growth. The high and low status of nitrogen can be quickly monitored by measuring NBI, which can provide accurate information of agricultural production and management. The relationship between NBI and original spectrum and derivative spectrum of infrared and near infrared wavelength from flowering to maturity stage was analyzed based on high definition digital image and hyperspectral data on unmanned aerial vehicles. Then, the sensitive bands were selected and the vegetation indexes were calculated. The inversion models of NBI were constructed by empirical model method. The optimal inversion model was obtained by analysing the determination coefficient (R) and the root mean square error (RMSE) of validating model. The results showed that the correlation between NBI and derivative spectral reflectance was more stronger than that between it and original spectral reflectance. All the 14 vegetation indices selected in this study, except the derivative spectral photochemical reflectance index, had significant correlation with NBI. The NBI inversion models were constructed based on those 13 vegetation indices and the accuracy was analyzed. The inversion model constructed by derivative spectral difference vegetation index had the highest accuracy, with the R and RMSE being 0.771 and 3.077 respectively. The soybean NBI distribution maps of the whole growing stages generated by this model could reflect the soybean growth state. Estimation of NBI using the high definition digital image and hyperspectral data obtained by unmanned aerial vehicle, as shown by our results, could be a real-time, dynamic, non-destructive and effective way to monitor the nitrogen status of soybean. It's a simple and practical method for precise management of nitrogen in soybean.
氮平衡指数（NBI）是作物生长的重要指标之一。通过测量NBI可以快速监测氮素的高低状况，为农业生产管理提供准确信息。基于无人机的高清数字图像和高光谱数据，分析了从开花期到成熟期NBI与红外和近红外波段原始光谱及导数光谱之间的关系。然后，选择敏感波段并计算植被指数。采用经验模型法构建NBI反演模型。通过分析验证模型的决定系数（R）和均方根误差（RMSE）获得最优反演模型。结果表明，NBI与导数光谱反射率的相关性比其与原始光谱反射率的相关性更强。本研究选取的14种植被指数中，除导数光谱光化学反射率指数外，其余均与NBI具有显著相关性。基于这13种植被指数构建NBI反演模型并分析其精度。由导数光谱差值植被指数构建的反演模型精度最高，R和RMSE分别为0.771和3.077。该模型生成的大豆全生育期NBI分布图能够反映大豆生长状况。结果表明，利用无人机获取的高清数字图像和高光谱数据估算NBI，可为大豆氮素状况监测提供一种实时、动态、无损且有效的方法。这是一种简单实用的大豆精准施氮管理方法。
