中文摘要：

以新疆天山北坡中段典型流域季节性积雪为研究对象,基于高光谱遥感监测技术,分析了融雪期积雪孔隙率与光谱反射率的相关性。采用偏最小二乘法（PLS）对相关性较高的波段进行压缩,并提取贡献率最高的前四个主成分,以此用来确定神经网络的隐含节点数、输入层、输出层的初始权值,建立PLS-BP模型进行积雪孔隙率反演研究。结果表明：当隐含节点数为3,模型的线性确定相关系数（R^2）较高为0.915 9,RMSE为0.04,相对误差为0.23。与传统偏最小二乘回归（PLSR）、主成分回归（PCA）建模方法相比,精度较高,所建定量模型可用于高光谱遥感反演积雪孔隙率。

英文摘要：

As one of the most important surface water resources in arid areas,snowcover plays an irreplaceable role in water resources management,global climate change,snowmelt runoff simulation and forecasting. Snow porosity is one of the important indexes reflecting physical characterization of snowmelt and snowpack. Using the hyper spectral technology,this paper analyzed the impacts of snow porosity on spectral reflectance in a typical snowmelt watershed in Northern Slope of the Tianshan Mountain. Combined the BP neural network method and partial least-squares regression,we further proposed a new quantitative modeling approach of hyper spectral to specifically invert the snow porosity by remote sensing data. Results show as follows：（1）the snow porosity was obviously responded to spectral reflectance,and showed significant correlation in the near-infrared bands including 1 165-1 478 nm,1 615-1 921 nm,and 1 930-2 100 nm.（2）When the implied nodes was set 3,the inversion ability of snow porosity with hyper spectral data can be greatly improved by PLS-BP model,coefficient of determination R2 and the root mean square error（RMSE）of linear regression between the snow porosity measured and simulated of PLS-BP in the same period were 0.915 9 and 0.04,respectively.（3）Compared with the traditional partial least-squares regression（PLSR）and principal components regression（PCR）,PLS-BP model obtained higher inversion,for example,the coefficient of determination R2 and the root mean square error（RMSE）of PCA-BP were 0.604 2 and 0.42,respectively,but for the traditional PLSR,they were 0.3536 and0.63,respectively. The innovation of this study is as follows：（1）using principal component to obtain main information bands as input of PLS-BP model,which is conducive to reflect reflectivity of snow porosity and help improving the model precision;（2）the PLS-BP combined with hyperspectral remote sensing data could estimate the snow porosity better,and this method can be referenced for analysis of near infrared