中文摘要：

实时、快速、无损监测作物氮素状况对于精确氮肥管理具有重要意义。传统的氮素估测方法在时间或空间上难以满足要求，新兴的高光谱遥感技术为作物氮素监测提供了有效手段和技术途径。本研究的目的是基于三个田间试验的系统观测资料，探索可用于小麦叶片氮素监测的新的高光谱敏感波段及比值指数。利用减量精细采样法，系统构建了350-2500nm范围内所有两两波段形成的比值光谱指数RSI（ratio speetral index），综合分析了小麦叶片氮积累量LNA（ leaf nitrogen accumulation） （gN·m^-2）与RsI的定量关系，发现了监测叶片氮积累量的新高光谱特征波段（990，720）和光谱指数RSI（990，720），建立了相应的监测模型y=5．095x-6．040，模型的决定系数（R^2）为0．814。利用独立试验资料检验模型，决定系数（R^2）为0．847，相对根均方差（RRMSE）为24．70％，表明模型预测值与观察值之间的符合度较高。因此，利用高光谱比值指数RSI（990，720）来估算小麦叶片氮积累量是精确可行的。该结果为便携式小麦氮素监测仪的研制开发及遥感信息的快速提取提供了适用可行的波段选择与技术依据。

英文摘要：

The objectives of the present study were to explore new sensitive spectral bands and ratio spectral indices based on precise analysis of ground-based hyperspectral information, and then develop regression model for estimating leaf N accumulation per unit soil area （LNA） in winter wheat （Triticum aestivum L. ）. Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. By adopting the method of reduced precise sampling, the detailed ratio spectral indices （RSI） within the range of 350-2 500 nm were constructed, and the quantitative relationships between LNA （gN. m^-2 ） and RSI （i, j） were analyzed. It was found that several key spectral bands and spectral indices were suitable for estimating LNA in wheat, and the spectral parameter RSI （990, 720） was the most reliable indicator for LNA in wheat. The regression model based on the best RSI was formulated as y=5. 095x--6. 040, with R^2 of 0. 814. From testing of the derived equations with independent experiment data, the model on RSI （990, 720） had R^2 of 0. 847 and RRMSE of 24. 7%. Thus, it is concluded that the present hyperspectral parameter of RSI （990, 720） and derived regression model can be reliably used for estimating LNA in winter wheat. These results provide the feasible key bands and technical basis for developing the portable instrument of monitoring wheat nitrogen status and for extracting useful spectral information from remote sensing images.