Remote Sensing - Based Analysis of LST and NDVI Correlation for the Coolest and Hottest Month of 2023 in Khanaqen City

Abstract

Urban land cover characteristics often lead to variations in surface temperature. Remote Sensing (RS) and Geographic Information Systems (GIS) are valuable tools for obtaining detailed information about surface indices. This study evaluates the temporal variation of Land Surface Temperature (LST) in Khanaqen city, located in South eastern Iraqi Kurdistan and Eastern Iraq. The research investigates the relationship between LST and the Normalized Difference Vegetation Index (NDVI) for the coolest and hottest month of 2023 using Landsat images from the USGS Earth Explorer. The objective is to explore how green spaces influence LST during the cool, humid winter and hot, dry summer. Two satellite images from Landsat 9 were used to retrieve LST and NDVI for the study periods. The results indicate that green spaces significantly reduce LST in July, so that the result of the correlation analysis of variables, significant inverse correlation between LST and NDVI (R = -0.53, R2 = 0.28 and P value = 0.00) highlights the effect of vegetation cooling. Conversely, January shows a modest correlation (R = -0.01, R2 = 0.00, and P value = 0.90), indicating a minimal effect of vegetation cover on LST.