基于高光谱数据的滴灌甜菜叶片全氮含量估算

Estimation of total nitrogen content in sugarbeet leaves under drip irrigation based on hyperspectral characteristic parameters and vegetation index

本文旨在明确甜菜叶片全氮含量与高光谱地面植被遥感的定量关系,建立干旱区甜菜叶片全氮含量精确估测模型,及时监测甜菜生长状况。本研究选取新疆滴灌甜菜(Beta356)为材料,利用ASD野外高光谱仪在甜菜叶丛快速生长期、块根膨大期与糖分积累期采集各处理反射光谱,并同时测定全氮含量,分析原始光谱反射率及一阶微分光谱反射率与全氮含量的相关性,并进一步建立光谱特征参数与敏感波段植被指数全氮含量估算模型。结果表明,光谱特征参数Dr762幂函数下估算模型具有较好估算甜菜叶片全氮含量的能力,其决定系数R2=0.747,验证相对误差RE(%)为21.635,验证均方根误差RMSE为4.914;通过植被指数与叶片全氮含量建立多种函数估测模型,其中差值植被指数Dr762–Dr496下一元线性函数具有较好估算甜菜叶片全氮含量的能力,其决定系数R2=0.794,验证相对误差RE(%)为23.008,验证均方根误差为5.372。

The purpose of this paper is to clarify the quantitative relationship between total nitrogen content of sugar beet and high-resolution vegetation remote sensing, to explore the establishment of an optimal estimation model for total nitrogen content of sugar beet, and to monitor the growth of sugar beet. Xinjiang drip-irrigated sugar beet(Beta356) was selected to collect the reflectance spectra of leaf cluster during the leaves rapid growth period, root expansion period and sugar accumulation period by the ASD field hyperspectral apparatus. The total nitrogen content was also measured and the relationship between original spectral reflectance and total nitrogen content was analyzed. According to the correlation between the first-order differential spectral reflectance and total nitrogen content, a total nitrogen content estimation model was established. The model with spectral characteristic parameter Dr762 power function had a good ability to estimate total nitrogen content in leaves of beet, with the determination coefficient, relative error, and root mean square error of 0.747, 21.635, and 4.914, respectively. Various function estimation models were established based on vegetation index and leaf total nitrogen content. The linear function under vegetation index Dr762–Dr496 had better ability to estimate leaf total nitrogen content. Its determinant coefficient, relative error, and root mean square error were 0.794, 23.008, and 5.372, respectively.