Land surface temperature (LST) is an essential parameter in investigating environmental, ecological processes and climate change, and thermal infrared remote sensing is a useful tool to acquire information regarding LST. Several accurate LST retrieval methodologies have been developed or refined in recent years and have demonstrated great potential. An assessment of various recent LST inversion single-channel (SC) algorithms is presented. These algorithms include improved mono-window, SC, and improved single channel (ISC). We compared the methods using two Brazilian sites, in which two kinds of validation were performed: field measurements with the satellite overpass and a comparative analysis using the web-based Atmospheric Correction Parameter Calculator tool and the radiative transfer equation (RTE) (assumed as reference). The three methods showed high coefficient of determination with the RTE (between 0.9 and 0.98). SC algorithm produced the furthest results from the reference and was statistically different. ISC algorithm provided the most reliable LST estimates, yielding root mean square errors between 1.53 and 1.91 K. LST can be retrieved through ISC algorithm only using meteorological station data, thus being an alternative for regions where radiosonde points have low density. Our findings contribute to more operational LST products from the Landsat series in humid places.