基于双波段植被指数（TBVI）的柑橘冠层含氮量预测及可视化研究

摘要

氮素（nitrogen，N）是果树生长发育的必需重要元素，及时准确地无损检测果树的氮素水平对果实增产、合理施肥以及减少环境污染等具有重要意义。研究了基于高光谱成像技术进行柑橘冠层含氮量预测及可视化的可行性。实验采用高光谱成像光谱仪 ImSpector V10E（Spectral imaging Ltd．，Oulu，Finland）分别采集柑橘叶片实验室样本和野外整个植株冠层的高光谱图像。利用 ENVI 软件提取每个叶片样本感兴趣区域（ROI）的平均光谱数据作为整个样本的光谱数据进行分析，同时采用杜马斯燃烧法快速定氮仪（Ele-mentar Analytical，Germany）测定叶片样本的含氮量。通过简单相关分析和双波段植被指数（TBVI）的获取，建立基于光谱数据的含氮量预测模型。计算表明，基于811和856 nm 的双波段植被指数（TBVI）能够建立最佳的柑橘叶片含氮量预测模型（R 2＝0．6071）。在此基础上，计算上述 TBVI 的冠层图像，把基于该 TBVI 的含氮量预测模型导入到 TBVI 图像中计算生成冠层含氮量的预测分布图。图中直观地显示柑橘嫩叶、中叶、老叶的含氮水平从高到低分布，实现了冠层含氮量的可视化。结果表明，利用高光谱成像技术可以实现柑橘冠层氮素水平的检测和诊断，这为实施基于每颗果树信息的变量施肥技术提供了参考信息。%Nitrogen is a necessary and important element for the growth and development of fruit orchards.Timely,accurate and nondestructive monitoring of nitrogen status in fruit orchards would help maintain the fruit quality and efficient production of the orchard,and mitigate the pollution of water resources caused by excessive nitrogen fertilization.This study investigated the ca-pability of hyperspectral imagery for estimating and visualizing the nitrogen content in citrus canopy.Hyperspectral images were obtained for leaf samples in laboratory as well as for the whole canopy in the field with ImSpector V10E (Spectral Imaging Ltd., Oulu,Finland).The spectral datas for each leaf sample were represented by the average spectral data extracted from the selected region of interest (ROI)in the hyperspectral images with the aid of ENVI software.The nitrogen content in each leaf sample was measured by the Dumas combustion method with the rapid N cube (Elementar Analytical,Germany).Simple correlation a-nalysis and the two band vegetation index (TBVI)were then used to develop the spectra data-based nitrogen content prediction models.Results obtained through the formula calculation indicated that the model with the two band vegetation index (TBVI) based on the wavelengths 811 and 856 nm achieved the optimal estimation of nitrogen content in citrus leaves (R 2 =0.607 1 ). Furthermore,the canopy image for the identified TBVI was calculated,and the nitrogen content of the canopy was visualized by incorporating the model into the TBVI image.The tender leaves,middle-aged leaves and elder leaves showed distinct nitrogen status from highto low-levels in the canopy image.The results suggested the potential of hyperspectral imagery for the nonde-structive detection and diagnosis of nitrogen status in citrus canopy in real time.Different from previous studies focused on nitro-gen content prediction at leaf level,this study succeeded in predicting and visualizing the nutrient content of fruit trees at canopy level.This would provide valuable information for the implementation of individual tree-based fertilization schemes in precision orchard management practices.