Estimation Land Surface Temperature & NDVI from Landsat-9 Thermal Infrared Data Using Single Window algorithm Method of Al- khoms District, Libya (مجلة البحوث الأكاديمية, 2025)
Estimation Land Surface Temperature (LST) from Landsat satellite imageries has been shown to be a very effective in the estimation of environmental protection, ecology, and climatic models. Land Satellite (LANDSAT) Data has given more direction using techniques of remote sensing and Geographic Information System (GIS) to study the land processes. This research work was intended to evaluate the LST and the associated land cover parameters viz; land surface emissivity (LSE), Brightness Temperature (BT), and normalized difference vegetation index (NDVI). The study focused on the estimation of LST with NDVI over district Al-Khoms, Libya, using the means of remote sensing and GIS techniques (Raster functions and Raster calculation).The used data acquired from LANDSAT 9 satellite data on May 2020, thermal Bands (10 & 11) . Several algorithms have been suggested to extract LSTs from Thermal Infrared (TIR) data using different band configurations. These algorithms can be roughly classified into four types: single- window, split-window, multichannel, and machine learning methods. The results of this study presented that surface temperature was high in the barren areas while it was low in the vegetation cover areas. As the Single Window algorithm uses both OLI and TIRS bands, the results are feasible to calculate NDVI, LSE, TB and LST with appropriate accuracy of the study area.
Keywords: Remote sensing; GIS; Land Surface Temperature; Land Surface Emissivity; Normalized Difference Vegetation Index