Assessment of spatial variability of land surface temperature using multi-satellite data in the Lake Tana catchment, Upper Blue Nile Basin, Ethiopia

This study investigates the spatial and temporal variability of Land Surface Temperature (LST) in the Lake Tana catchment, Upper Blue Nile River Basin, Ethiopia, utilizing data from Landsat 8, MODIS and Sentinel-3. The analysis covers two distinct seasons: winter (January–April) and summer (June–August) of 2021. LST was derived using the Split Window Algorithm (SWA) applied to the thermal bands of these satellites. The results reveal notable Spatial and seasonal variations, with the highest LST recorded in April at 51 °C, as measured by Sentinel-3, and the lowest LST in February at 5 °C, as measured by Landsat 8. In the summer, the highest LST was observed in June at 42 °C from Landsat 8, while the lowest was recorded in July at 8 °C from Sentinel-3. The spatial distribution of LST showed higher temperatures in the peripheral regions of the study area, with cooler temperatures around Lake Tana, largely due to its cooling effect. Correlation analysis between LST and mean temperature data from NASA POWER revealed strong correlations (r > 0.5) for most months, particularly for Landsat 8, which demonstrated the strongest association with mean temperature across both winter and summer seasons. These findings underscore the significance of satellite-derived LST in monitoring regional climate dynamics, supporting sustainable agricultural practices, and informing water resource management strategies. The study highlights the potential of remote sensing technologies for evaluating the impact of climate variability on ecosystems and land use in the context of climate resilience and environmental sustainability. LST monitoring becomes an indispensable tool for achieving sustainable development objectives, particularly in improving resilience to climate risks (SDG 13), enabling sustainable agricultural and water practices (SDGs 2 and 6), and supporting environmental conservation (SDG 15). Therefore, this study fills a region-specific gap in understanding LST dynamics and provides valuable insights for sustainability policy and planning in data-scarce areas.