Snow cover is one of the important parameters in the study of hydrometeorology.At present,the most widely used Snow Cover Area (SCA) can be obtained by Moderate-resolution Imaging Spectroradiometer (MODIS),which is often used in the study of temporal and spatial changes of snow cover.However,large area snow data missing existed in MODIS snow cover products due to the cloud occlusion.To address this,we take the Kaidu River basin as the research region,and combine the snow product data retrieved from MODIS carried on the Terra and Aqua satellites with the topographic feature data,then use a denoising autoencoder artificial neural network and the extreme snow line method to quantitatively complement the snow data loss caused by cloud occlusion in complex alpine terrain.The denoising autoencoder artificial neural network combines multi-feature data to establish a nonlinear mapping relationship between topographic features and snow grain size,which is then used to complement the missing snow grain size data.The extreme snow line method is used to remove the false report value in low altitude area and obtain the snow cover image with high precision.In contrast verification,the accuracy of the proposed cloud removal method is over 86％,which effectively improves the snow cover detection.Therefore,the approach proposed in this paper can effectively remove cloud occlusion from snow products in complex terrain areas.