Abstract. Lake surface temperature (LST) is an important attribute that highlights regional weather and climate variability and trends. The spatial resolution and thermal sensors on Landsat platforms provide the capability of monitoring the temporal and spatial distribution of lake surface temperature on small to medium size lakes. In this study, a retrieval algorithm was applied to the thermal bands of Landsat archives to generate a LST dataset (North Slave LST dataset) for 535 lakes in the

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... th Slave Region (NSR) of the Northwest Territories (NWT), Canada for the period of 1984 to 2021. North Slave LST was retrieved from Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI/TIRS, however majority of the dataset were created from the thermal bands of Landsat-5 (43 %) due to its longevity (1984–2013). Cloud masks were applied to Landsat images to eliminate cloud cover. In addition, a 100-meter inward buffer was applied to lakes to prevent pixel mixing with shorelines. To evaluate the algorithm applied, retrieved LST was compared with in-situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) LST observations. A good agreement was observed between in-situ observations and North Slave LST derived in this study with a mean bias of 0.12 °C and an RMSD of 1.7 °C. The North Slave LST dataset contains more available data from warmer months (May to September), covering 57.3 % in comparison to colder months (October to April). Average number of images per year for each lake across the NSR ranged from 20 to 45. The North Slave LST dataset will provide communities, scientists and stakeholders with spatial and temporal changing trends of temperature on lakes for the past 38 years.