Modeling and analysis of thermal contrast based on LST algorithm for Baghdad city

• Main Authors: Al-Barrak, Alyaa, Alatta, Hassan J., Behadili, Suhad Faisal, Sayyid, Baqer H., Adham, Ali, Mahmood, Mustafa A.
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Brightness temperature; Climate change; Emissivity; Glaciers; Land cover; Land surface temperature; Land use; Landsat satellites; Monsoons; Rainfall; Sensors; Vegetation growth
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0202094

Land Surface Temperature (LST) is an important factor in global climate change, vegetation growth, and glacier. Its impact will be more in monsoon area because of monsoon failure and uncertainty and unpredictable in rainfall. In this article we perform LST estimation using LST algorithm on Landsat 8 Operational Land Imager (OLI) Sensor and Thermal Infrared Sensor (TIRS) dataset of Baghdad city for the period June 2021-2022. TIRS sensor exhibits two thermal Band 10 and 11. LST algorithm require brightness temperature value of both band 10 or 11 as well as land surface emissivity calculated from OLI bands (NIR and RED) for estimation of LST. However, the estimated emissivity values over few land use/land cover of Landsat-8 OLI have been compared with the literature values. The results show that the satellite derived emissivity values are in the acceptable range and the NDVI is effective in deriving surface emissivity. The derived surface temperature values are found to be in good agreement with the field measured values, indicating that the methodology can be adopted for the study over urban areas. As well as, these results shown that there is a thermal contrast in LST of June 2022 is higher than June 2021 by approximately 0.5 C°.