Land Surface Temperature and Soil Moisture Index Assessments Using Satellite Imagery at Karbala City

Abstract

The use of remote sensing technology and modeling methodologies to monitor changes in land surface temperature (LST) and soil moisture index (SMI) has become an essential reference for making decisions on sustainable land use. This study aims to estimate LST and SMI in Karbala Province to contribute to land management, urban planning, or climate resilience in the region; as a result of environmental changes in recent years, LANDSAT Satellite Imagery from 2013- 2023 was implemented to estimate the LST and SMI indexes. ArcGIS 10.7 package was used to calculate the indices, and the normalized mean vegetation index (NDVI) was calculated as it is closely related to extracting the LST and SMI indices. The results showed that extracting the vegetation index, atmospheric radiation, satellite brightness temperature, and land surface emissivity using Landsat-8 bands and processing them on ArcGIS facilitates the estimation of each LST and SMI index. The results showed a complete inverse correlation between LST and SMI; the correlation coefficient during 2013 and 2023 was ^-1 and -0.999, respectively.