中文摘要：

基于6个小麦品种、5个施氮水平、4年田间试验条件下不同生育时期的小麦叶片高光谱反射率和相应的氮含量及生物量,采用减量精细采样法,系统构建了350～2500nm范围内所有两两波段组成的归一化光谱指数[NDSI（i,j）],综合分析了小麦叶片氮积累量（LNA,gN·m-2）与NDSI（i,j）的定量关系,确定了估算叶片氮积累量的新高光谱特征波段和光谱指数,进而建立了小麦叶片氮积累量监测模型.结果表明：估算小麦叶片氮积累量的敏感波段主要存在于可见光区和近红外区,最佳特征波段组合为720nm和860nm;基于NDSI（860,720）的叶片氮积累量监测模型为LNA=26.34×[NDSI（860,720）]1.887（R2=0.900,SE=1.327）.利用独立试验资料的检验结果表明,基于NDSI（860,720）建立的回归模型对小麦叶片氮积累量的估测精度为0.823,RMSE为0.991gN·m-2,模型预测值与观察值之间的符合度较高.可利用新的归一化高光谱参数NDSI（860,720）来估算小麦叶片氮积累量.

英文摘要：

Four independent field experiments with 6 wheat varieties and 5 nitrogen application levels were conducted,and time-course measurements were taken on the canopy hyperspectral reflectance and leaf N accumulation per unit soil area （ LNA,g N·m -2） . By adopting reduced precise sampling method,all possible normalized difference spectral indices [NDSI（ i,j） ]within the spectral range of 350-2500 nm were constructed,and the relationships of LNA to the NDSI（ i,j） were quantified,aimed to explore the new sensitive spectral bands and key index from precise analysis of ground-based hyperspectral information,and to develop prediction models for wheat LNA. The results showed that the sensitive spectral bands for LNA were located in visible light and near infrared regions,especially at 860 nm and 720 nm for wheat LNA. The monitoring model based on the NDSI （ 860,720） was formulated as LNA =26. 34 ×[NDSI（ 860,720） ]1.887,with R2 =0. 900 and SE = 1. 327. The fitness test of the derived equations with independent datasets showed that for wheat LNA,the model gave the estimation accuracy of 0. 823 and the RMSE of 0. 991 g N·m -2,indicating a good fitness between the measured and estimated LNA. The present normalized hyperspectral parameter of NDSI（ 860,720） and its derived regression model could be reliably used for the estimation of winter wheat LNA.