中文摘要：

祁连山冰沟流域地形复杂，积雪深度较浅且破碎化严重，针对MODIS标准积雪面积比例产品在该地区监测精度较差的问题，本研究基于冰沟流域浅雪光谱特征分析及结合野外实测经验，探索浅雪的光谱特征对MODIS浅雪面积比例提取精度的影响；然后通过线性回归法、线性混合像元分解法及BP神经网络模型3种方法分别构建了针对研究区的MoDIS积雪制图算法，并利用同时相的Landsat8 OLI二值积雪数据作为真值对上述3种制图方法进行精度验证。结果表明，1）浅雪的光谱反射率对基于NDSI阈值法的MODIS浅雪提取精度几乎没影响，MODIS提取浅雪精度差的主要原因为该地区复杂的地形而导致的积雪分布破碎化，即混合像元的大量存在；2）利用BP神经网络模型反演积雪面积比例的最佳输入参数组合为（ρ1～ρ7）＋NDSI＋DEM；3）线性混合像元分解模型在该研究区的积雪面积比例提取精度较低，BP神经网络模型精度最好；4）在地形复杂区域，多因素模型（BP神经网络模型）相对于单因素模型（一元线性回归模型）具有较好的积雪面积比例提取精度和稳定性，可以为研究区MODIS积雪面积比例的反演提供一种理想的方法。

英文摘要：

The terrain in Qilian Binggou Basin is relatively complex, the snow depth is generally thin and snow distribution is fragmentized. Aiming at the problem of poor monitoring accuracy of MODIS snow products in this area, This study explored the influence of spectral characteristic of thin snow on the retrieval accuracy of MODIS data based on thin snow spectral characteristic analysis and combined with field survey experience; Then three MODIS fractional snow cover retrieval models are constructed through linear regression, linear mixed pixel unmixing and artificial neural network, and the snow map retrieved from Landsat 80LI image is taken as the ground truth to validate the three models＇ accuracy respectively. The results show that： 1） The spectral reflectance of thin snow almost has no effect on MODIS snow retrieval accuracy based on the NDSI threshold method in the area. The poor accuracy of MODIS retrieval from thin snow is mainly due to the fragmentation of snow distribution caused by the complex terrain in the area, that is, the existence of a quantity of mixed pixels. 2） The best input parameters combination of BP artificial neural network model for MODIS fractional snow cover retrieval is（ρ1～ρ7）＋NDSI＋DEM;3） The linear mixed pixel unmixing model has the lowest accuracy and the BP artificial neural network model has the best accuracy in terms of snow cover extraction in the study area. 4） Multi-factor model （BP artificial neural network） has better accuracy and stability of snow coverage extraction compared with the single factor model （unary linear regression model） in complex terrain, it is an ideal method for the retrieval of fractional snow cover in the study area.