The mining activities of ion-type rare earth have caused extremely ecological disturbances on the surface of the mining area and caused local ecological and environmental problems. The variation of surface thermal environment in the mining area can better reflect the ecological disturbance characteristics of the mining area, and is an important parameter to identify surface ecological disturbances. The ion-type rare earth area has the characteristics of scattered ore and small single-site area, thus obtaining the surface temperature data with strong practicability and higher spatial resolution is valuable to the monitoring of the ecological environment of the rare earth mining area.We constructed a temperature downscale model with image fusion and spectral unmixing. The Lingbei ion-type rare earth district in Dingnan County of Ganzhou City is selected as the study area. The Landsat 8 satellite image is used as main data source. Firstly, we select data of two seasons in the same year, and combine the integrated image fusion algorithm and linear spectral mixture model. The surface temperature resolution of the surface is downscaled to 15 m.Then, the land surface temperature results after downscaling are qualitatively and quantitatively analyzed and tested for accuracy.The results show that the spatial distribution of the surface temperature and the overall trend of the mining area before and after the decomposition are consistent. The surface temperature after the downscaling can reflect the surface features and spatial differences of the mining area in more details. The overall root mean square error(RMSE)of the two seasonal phases in the study area are respectively for 1.459 K and 1.196 K, the mean absolute error(MAE) are 1.128 K and 0.952 K respectively with high accuracy.Our proposed method has high applicability for improving the spatial resolution of the surface temperature of the ionic rare earth.