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利用多源遥感数据,结合光学遥感数据高空间分辨率及被动微波数据不受云干扰的优势,利用 MODIS 逐日积雪标准产品和 AMSR-E 雪水当量产品,生成了欧亚大陆中高纬度区500 m 分辨率的逐日无云积雪产品,并利用更高分辨率的 Landsat-TM 数据生成的积雪产品作为“真值”影像,对研发的逐日无云积雪覆盖产品的精度进行了验证。结果表明：MOD10A1和 MYD10A1受云影响均较为严重,无法直接用于地表积雪面积的监测。而本研究合成的逐日无云产品具有较好的精度,与 TM 积雪图具有较高的一致性。但不同的土地覆盖类型对积雪分类精度有一定的影响。其中,裸地和草原覆盖区精度最好,Kappa 系数分别为0．655和0．644,均为高度一致性；其次精度较好的是灌丛和耕地覆盖区,Kappa 系数分别为0．584和0．572,均为中等的一致性；而森林覆盖区由于受到高大植被的影响,Kappa 系数仅为0．389,合成产品相对 TM 积雪产品明显高估了森林区积雪面积。整体 Kappa 均值达到0．569,接近高度一致,研究结果对实时监测欧亚大陆积雪面积具有一定的应用价值。“,”With the high spatial resolution of optical data and the lack of weather effects of passive micro-wave data,we developed a daily cloud-free snow cover product with a resolution of 500 m for region in Eur-asia by the use of the Moderate Resolution Imaging Spectroradiometer (MODIS)standard daily snow cover products and the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)snow water equivalent (SWE)product.Selecting Landsat-TM data with high-resolution as“truth value”images, the accuracy of the MODIS daily cloud-free snow products over Eurasia is evaluated by comparing with these“truth value”images.The results demonstrated that MOD10A1 and MYD10A1 cannot be directly used to monitor snow cover because of the serious effect of cloud amount.However,the products developed in this study has good accuracy,which are high concordant with Landsat-TM.Land-use type has influence on snow classification accuracy.The accuracies of barren and grasslands are best and the Kappa coefficient rea-ches to 0.655 and 0.644 respectively.The accuracies of shrublands and croplands are good and the Kappa is 0.584 and 0.572,respectively.But the Kappa of the forested region is only 0.389.The integral accuracy of the synthesized product whose Kappa reaches to 0.569 in this study is high,which can effectively improve the accuracy of snow area monitoring in Eurasia.