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摘要：  积雪是冰冻圈中分布最广泛的要素，在气候变化以及水文循环中扮演着重要角色。微波遥感因其全天时全天候工作、具有一定穿透性等优势，成为积雪监测的重要手段。利用FY-3C卫星同步观测获取的微波成像仪(MWRI)被动微波亮度温度数据、融合可见光红外扫描仪(VIRR)与中等分辨率成像光谱仪(MERSI)数据得到的积雪产品，结合MODIS地表分类数据、地表温度数据，发展了基于国产卫星数据的被动微波积雪判识算法。首先提取无云覆盖的不同地表类型被动微波数据像元样本，然后对各地表类型的微波特征进行分析，利用空间聚类的方法，得到TB19V-TB19H、TB19V-TB37V、TB22V、TB22V-TB89V、(TB22V-TB89V)—(TB19V-TB37V)这五类可以较好地区分积雪和其他类似积雪地表的指标。最后应用MODIS积雪产品为参考对该积雪判识算法进行精度评价，该算法在中国西部积雪判识总体精度为87.1%，漏判率为4.6%，误判率为23.3%；Grody算法判识总体精度为78.6%，漏判率为9.8%，误判率为30.7%，该算法判识精度高于Grody算法；通过Kappa系数分析比较，该算法积雪判识结果的Kappa系数值为47.3%，高于Grody算法判识结果的Kappa系数值39.9%，表明该算法积雪判识结果与MODIS积雪产品判识结果一致性更好。 关键词： 积雪范围;  地表分类;  FY-3C卫星;  ')" href="#">被动微波亮度温度     Abstract: Snow cover，as the most widely distributed element in the cryosphere，plays a critical role in the climate change and hydrological cycle.Microwave remote sensing is an important technique to monitor snow cover，because of its all-weather，all-time capability and ability to penetrate.In this study，FY-3C satellite’ s passive microwave brightness temperature data acquired by FY-3C MWRI，snow cover products obtained by MERSI and VIRR，MOD10C1 and MOD11C1，are used to develop a new Snow identification algorithm in western China.In this algorithm，the passive microwave brightness temperature of different land types are firstly extracted，and then they are analyzed using cluster analysis.The analysis results exhibit that TB19V-TB19H，TB19V-TB37V，TB22V，TB22V-TB89V，(TB22V-TB89V)-(TB19V-TB37V) can be used as the criterion for identifying snow cover from other scatters.Finally，MODIS snow cover products are used to validate the identification accuracy as a reference，and the results show that the overall accuracy of this algorithm in western China is 87.1%，the omission rate is 4.6%，the commission rate is 23.3%.The overall accuracy of Grody algorithm is 78.6%，the omission rate is 9.8%，and the commission rate is 30.7%.The accuracy of this algorithm is higher than the Grody algorithm.The Kappa coefficient of this algorithm is 47.3%，which is higher than the Grody algorithm’s Kappa coefficient of 39.9%，indicates that the algorithm's snow identification results are more consistent with the MODIS snow product identification results.

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