The influence of the changes in surface albedo on the thermal environment of the mining area due to the destruction of the original vegetation caused by the high-intensity mining of mineral resources was analyzed. Taking Malanzhuang Iron Mine in Qian'an city of Hebei province as an example, the statistical model method was adopted to retrieve the surface albedo based on the visible light band of Landsat remote sensing image, and the pixel trisection model was used to retrieve the vegetation fractional coverage based on NDVI-DFI. By means of radiative transfer equation method, the land surface temperature (LST) was retrieved based on the thermal infrared band of Landsat remote sensing image. With the aid of superposition analysis method, correlation analysis method, and regression analysis method, the response of spatiotemporal heterogeneity of surface thermal environment to the changes in photosynthetic vegetation coverage (f_PV) and surface albedo was quantitatively and visually explored. Results showed that the spatial distribution of surface thermal environment in the study area showed obvious heterogeneity and regularity. The high temperature zone was mainly distributed in the northern Shaheshan stope, central Liuheyu dump, and the unreclaimed Baimashan dump in the south. The moderate temperature zone was mainly located at the edge of the high temperature zone and other bare surfaces. The low temperature zone was mainly located in the northern reclaimed tailings pond and reclaimed waste dump, as well as the green land and water bodies in the area. The spatiotemporal variation of photosynthetic vegetation coverage and surface albedo resulted in a large heterogeneity of the surface thermal environment during the summer midday in the study area. The mean surface temperature of underlying surface in the third phase of the image was ranked as follows: mining area bare rock > dump slag > reclamation vegetation > water body. The regression analysis results showed that f_PV at 0.01 level (double sides) had a negative linear correlation with LST (p < 0.01), indicating that the increase in f_PV has a cooling effect on surface thermal environment, and the determination coefficients (R²)were 0.63, 0.55, and 0.65, respectively. According to the regression coefficient, in 2000, 2008, and 2018, for every 10% increase in f_PV, LST decreased by 0.66, 0.74, and 1.09 ℃. The regression analysis results showed that albedo at 0.01 level (double sides) had a positive linear correlation with LST (p < 0.01), indicating that the increase in albedo has a warming effect on surface thermal environment, and the determination coefficients (R²)were 0.35, 0.40, and 0.48, respectively. According to the regression coefficient, in 2000, 2008, and 2018, every 10% increase in albedo led to the increase in LST by 1.09, 1.36, and 1.76 ℃. The research results will provide a quantitative reference for the evaluation and optimization of the surface thermal environment heterogeneity in mining area.