Introduction
Land surface temperature (LST) is a key environmental parameter that provides insights into the thermal behavior of the Earth's surface. LST has diverse applications in fields such as agriculture, hydrology, and climate studies. Report on the calculation of LST for Akwa Ibom State in Nigeria using Landsat data collected from Google Earth Engine between November 2022 and February 2023. Series of computations were utilized to calculate LST from Landsat data, including radiance calculation, conversion to top-of-atmosphere (TOA) reflectance, NDVI calculation, land surface emissivity estimation, and proportion of vegetation analysis.

Methodology
To calculate LST, we began by calculating the radiance from Landsat data using the radiance multiplication and addition factors. We then converted the radiance to TOA reflectance. Next, we estimated the NDVI using the near-infrared and red reflectance bands. We utilized the NDVI values to calculate the land surface emissivity and subsequently derived the LST values using the Landsat thermal band data and the K1 and K2 constants. Finally, we analyzed the proportion of vegetation in the region based on the NDVI values.

Results
Based on the LST computation, we found that the study area had a mean LST of 23.4°C during the study period. We also analyzed the distribution of LST across different temperature ranges and found that 50.38% of the study area had LST between 19-21°C, while 57.22% of the study area had LST between 21-23°C. The other temperature ranges had smaller coverage with 12.25% of the study area having LST between 11-19°C, 31.88% having LST between 23-26°C, and 17.27% having LST between 26-36°C.

Conclusion
Our results show that the temperature distribution across Akwa Ibom State during the study period varied considerably, with the 19-21°C temperature range covering the largest area. The LST calculations can provide valuable insights into the thermal behavior of the Earth's surface and support decision-making in various fields. The proportion of vegetation analysis provides additional information on the region's vegetation coverage and can be useful for applications such as land-use planning and agricultural productivity assessment.