Seasonal Analysis of the Correlation Between Land Surface Temperature and Surface Moisture Dynamics on Various Land Covers; Garmian Administration as a Case Study

Abstract

This study aimed to examine the correlation between Land Surface Temperature (LST) and Modified Normalized Differences Vegetation Index (MNDWI) on different wetness-based land surfaces, Garmian Administration is selected as a case study, it is located in southeast of Iraqi Kurdistan region in semi-arid region. LST retrieved using Mono-Window Algorithm for a set of Landsat images acquired for the four seasons of 2025. Landsat True color and GIS base map along with MNDWI index were used to determine various wetness-based land cover types, the Pearson correlation coefficient was used to explain the impact of water body and wetness areas on LST. Results show that the nature of MNDWI impact on LST is different in each seasons and surfaces, the greatest and wettest association was noticed in water bodies. Autumn water bodies had the strongest negative correlation (r=-0.82), while Winter water bodies have the smallest (r=-0.17), where other factors may play a greater influence than wetness. MNDWI is a reliable approach for monitoring temperature dynamics in the Garmian area, since most results were statistically significant (p < 0.01).