中文摘要：

利用非成像高光谱仪,获取棉花不同品种、不同密度冠层关键生育时期的反射光谱数据,应用光谱多元统计分析技术,研究表明,棉花冠层叶绿素密度（CH.D）和叶片氮积累量（LNA）分别在反射光谱762 nm和763 nm处的相关系数达最大值（rCH.D=0.8845＊＊和rLNA=0.7870＊＊,n=47）;而一阶微分光谱数据对CH.D、LNA最敏感的波段均发生在750nm处（rCH.D=0.9098＊＊和rLNA=0.9164＊＊,n=47）;采用47个建模样本的一阶微分光谱750 nm处的数值与棉花冠层CH.D建立线性相关模型方程,估算47个检验样本的棉花冠层CH.D,再根据CH.D与LNA建立的线性相关方程估算检验样本的LNA,47个检验样本的实测LNA与估测LNA极显著线性相关（r=0.8982＊＊,n=94）,模型方程的估算精度达86.3%,实测值与估算值的RMSE=1.0155,相对误差为0.1380。说明基于高光谱数据的棉花冠层叶绿素密度的遥感估测,可以间接用于棉花冠层叶片氮积累量的监测研究。

英文摘要：

Chlorophyll and nitrogen contents are important parameters as the indicators of crop photosynthesis productivity, state of growing and nutrition, and optimal diagnosis for crop nitrogenous fertilizer demands. In practical application process, testing nitrogen is more complicated than testing chlorophyll, and using chemicals are liable to pollute environment. Many researches show that chlorophyll content has a positive correlation with nitrogen content, so the status of nitrogen can be indicated by chlorophyll content or its elements. In the meantime, between chlorophyll content and hyperspectral characteristics, a positive correlation still exists, the status of nitrogen can be monitored by chlorophyll remote sensing. Traditionally, the status of chlorophyll density （CH. D） and leaf nitrogen accumulation （LNA） are studied by utilizing hyperspectral data, mainly focused on crops of wheat, rice and corn, mostly establishing a correlation model between hyperspectral data and one variable among CH. D and LNA. But it is scarce for combining CH. D and LNA together in the research, based on hyperspectral data monitoring state of crop canopy nutrition. This paper by utilizing non-imaging hyperspectral spectrometer, 8 cotton cultivars and two of them with 4 level densities planting in north XinJiang, and multivariate regression analysis method recorded multi-temporal hyperspectral data of canopy at cotton key growing stages and analyzed the correlation between reflectance and cotton canopy CH. D, LNA. The result showed that the maximum correlation coefficients between hyperspectral data and CH. D, LNA occurred at 762 and 763 nm （rCH.D = 0. 8845^＊＊ , rigA = 0.7870^＊＊, n = 47） respectively; the highest correlation coefficients between the first derivative spectral data and CH.D, LNA beth occurred at 750 nm （rCH.D = 0.9098^＊＊ , rigA = 0.9164^＊＊, n = 47）. Based on the first derivative data at 750 nm of modeling samples, we established the CH. D linear regression equation and estimated the CH. D