Land surface temperature (LST) is estimated using thermal infrared remote sensing data, which record the apparent temperature of the earth’s surface by measuring the radiant energy of its surface. However, it is also possible to estimate LST through satellite images and image processing software. The Landsat 8 satellite was successfully launched in 2013, with two thermal infrared bands for continuous earth observations to provide for the estimation of LST. However, the calibration notifications issued by the United States Geological Survey (USGS) indicate that the data from the Landsat 8 thermal infrared sensor (TIRS) Band 11 show large uncertainty and thus, it was suggested to use TIRS Band 10 data as a single spectral band for LST estimation. In this study, we present a mono-window (MW) algorithm for LST estimation from the Landsat 8 (Path-147 and Row-38) using TIRS Band 10 data with a 100-m resolution. Emissivity was derived with the help of the normalised difference vegetation index (NDVI) proportion of vegetation technique for which operational land imager (OLI) Bands 4 and 5 (30-m resolution) were used. The results show that the LST was higher in the regions of barren land but lower in snow-covered areas. Further, the LST results were also compared with the air temperature data and they were found to be in good agreement. The MW algorithm presented in the study could be used as an efficient method for LST estimation from the Landsat 8 TIRS Band 10 data.