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Temperature vegetation dryness index (TVDI) in a triangular or trapezoidal feature space can be calculated from the land surface temperature (LST) and normalized difference vegetation index (NDVI), and has been widely applied to regional drought monitoring. However, thermal infrared sensors cannot penetrate clouds to detect surface informa-tion of sub-cloud pixels. In cloudy areas, LST data include a large number of cloudy pixels, seriously degrading the spatial and temporal continuity of drought monitoring. In this paper, the Remotely Sensed Daily Land Surface Tem-perature Reconstruction model (RSDAST) is combined with the LST reconstructed (RLST) by the RSDAST and ap-plied to drought monitoring in a cloudy area. The drought monitoring capability of the reconstructed temperature ve-getation drought index (RTVDI) under cloudy conditions is evaluated by comparing the correlation between land sur-face observations for soil moisture and the TVDI before and after surface temperature reconstruction. Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI (OTVDI) in 2011. In addition, RLST/NDVI scatter plots cover a wide range of values, with the fitted dry–wet bound-aries more representative of real soil moisture conditions. Under continuously cloudy conditions, the OTVDI inver-ted from the original LST (OLST) loses its drought monitoring capability, whereas RTVDI can completely and ac-curately reconstruct surface moisture conditions across the entire study area. The correlation between TVDI and soil moisture is stronger for RTVDI (R =?0.45) than that for OTVDI (R =?0.33). In terms of the spatial and temporal distributions, the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI. Hence, in continuously cloudy areas, RTVDI not only expands drought monitoring capability in time and space, but also im-proves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal in-frared data under extreme conditions.