Land surface temperature (LST) is a key parameter for land cover analysis and for many fields of study, for example, in agriculture, due to its relationship with the state of the crop in the evaluation of natural phenomena such as volcanic eruptions and geothermal areas, in desertification studies, or in the estimation of several variables of environmental interest such as evapotranspiration. The computation of LST from satellite imagery is possible due to the advances in thermal infrared technology and its implementation in artificial satellites. For example, Landsat 8 incorporates Operational Land Imager(OLI) and Thermal InfraRed Sensor(TIRS)sensors the images from which, in combination with data from other satellite platforms (such as Terra and Aqua) provide all the information needed for the computation of LST. Different methodologies have been developed for the computation of LST from satellite images, such as single-channel and split-window methodologies. In this paper, two existing single-channel methodologies are evaluated through their application to images from Landsat 8, with the aim at determining the optimal atmospheric conditions for their application, instead of searching for the best methodology for all cases. This evaluation results in the development of a new adaptive strategy for the computation of LST consisting of a conditional process that uses the environmental conditions to determine the most suitable computation method.
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