GEE-based analysis of spatiotemporal patterns of land cover and LST in an arid urban environment a case study of Abu Dhabi

Urban expansion and land cover changes significantly influence land surface temperature (LST), especially in arid environments. This study investigates spatial and temporal patterns of land cover and LST across Abu Dhabi, UAE, for the benchmark years 2017, 2020, and 2024, using Sentinel-2 imagery and MODIS thermal data via Google Earth Engine (GEE). Four dominant land cover classes were mapped: bare desert, urban areas, vegetation, and water bodies. Between 2017 and 2024, bare desert coverage declined from 92.8% to 90.9%, while urban land grew from 3.0% to 4.5%, vegetation increased from 3.3% to 4.1%, and water decreased from 0.9% to 0.5%. Thermal analysis based on MODIS summer composites revealed that 2020 was the hottest year, with an average LST of 53.14 °C , higher than 2017 (52.40 °C) despite COVID-19 mobility restrictions, likely due to extreme heat and atmospheric conditions. These values reflect emirate-wide averages, aggregated across all land cover types. By 2024, average LST declined to 48.76 °C, coinciding with expanded vegetation and milder summer temperatures. The observed 3.6 °C reduction may reflect both climatic moderation and land cover transformations. LST comparisons across land cover types showed a consistent thermal hierarchy: bare desert exhibited the highest surface temperatures, followed by urban areas, vegetation, and water bodies. These results highlight the cooling role of green infrastructure in hyper-arid cities. The findings contribute to Sustainable Development Goals (SDGs) 11, 13, and 15 by providing geospatial insights for sustainable land management and urban climate resilience.