Lake Sevan being Armenia’s largest freshwater reservoir has a vital economic, recreational and cultural importance to both the catchment area and the nation as a whole. At present the Sevan which has seen the dramatic - some 20m drop - in water level entailing grave ecological consequences to the whole of its ecosystem, is at the stage of recovery. Hence, it is very important to study basic parameters describing the ecological status of the lake, and their annual and seasonal dynamics. The Sevan water surface temperature (WST) is a key parameter which influences all ecological processes that occur in the Lake. Declining lake level has brought to reduction of water volume and consequently to earlier warming of lake water in spring and its earlier cooling in the fall. Besides, more frequent becomes the complete surface freezing of Lake Sevan. Remotely sensed imagery makes it possible to get immediate information on a regular basis about WST across the entire surface of lakes. The purpose of this particular research was to study the space and time dynamics of Lake Sevan WST using Landsat 8 satellite imagery. The advantage of Landsat8 images is a regular frequency of capturing and availability of another thermal band that helps reduce the atmospheric refraction-induced errors/deviations. This research involved Landsat imagery for 2000-2018. The images underwent preprocessing steps (radiometric calibration, atmospheric correction, normalization etc) and then Lake Sevan WSTs and their monthly and annual changes over the mentioned periods were derived using both thermal bands (b10, b11). The research confirmed the fact, that Lake Sevan surface completely or partly freezing with periodicity of 2-3 years, whereas before the water drop the periodicity was 15-20 years. The study of spatial distribution of WST data derived from remote sensing shows that the temperature data corresponds to the overall general picture of temperature for Lake Sevan. This research has indicated that remotely sensed images and Landsat 8 imagery in particular allow derive both WST data on a regular basis and retrospective data (since 2013).