The surface reflectance of the Tibetan Plateau is exploited in numerous applications， such as natural resource monitoring， ecological environmental protection， and geoscience research. Typically， the reflectance data of MOD09A1 are affected by detector noise and clouds， producing numerous abnormal pixels and diminishing the integrity and accuracy of remote sensing data. To address these issues， considering the universal geoscience law indicating that neighboring time-series remote sensing images are correlative， and the spectra of adjacent ground objects belonging to the same classification are similar， this paper proposes a deep learning method of surface reflectance reconstruction in the Tibetan Plateau based on incomplete multi-temporal data and land cover classification information. First， based on the multi-temporal reflectance data of MOD09A1 and land cover classification data of MCD12Q1， the basic reflectance image and auxiliary data of the target area are obtained through abnormal pixel removal， effective layer extraction， projection conversion， and mosaic. Subsequently， a deep learning network model is constructed based on the fusion of multi-temporal data and land cover classification information， according to basic principles of the residual network. Third， the deep learning model is trained using cloud mask samples cropped from an area with complete data and augmented training samples generated based on land cover classification and the K-means clustering algorithm. Finally， the trained model is utilized for surface reflectance reconstruction in the area with missing data. Two groups of comparative experiments demonstrate that the proposed method reduces the requirements for the amount and integrity of multi-temporal auxiliary image data and achieves accurate restoration and reconstruction of large-scale surface reflectance in the Tibetan Plateau by combining incomplete multi-temporal data and land cover classification information.