Estimation and Validation of Land Surface Temperature (LST) from Landsat 8 OLI and TIRS sensor’s Data

Abstract

In thermal remote sensing, the emitted radiations from the ground objects are measured for estimating the surface temperature. This measurement is providing the radiant temperature of a body which depends on two factors such as kinetic temperature and emissivity. The Split Window (SW) is a suitable algorithm to estimate the land surface temperature from Landsat 8, TIRS data. In this study NDVI is the chief parameter, which is computed from NIR and RED band of Landsat 8, OLI Sensor’s data. The ratio is done after the DOS correction and converting the DN to top of the atmospheric spectral radiance and then reflectance. Then the Land Surface Emissivity (LSE) is estimated through the Fractional Vegetation Coverage (FVC) which is obtained from NDVI. The Split Window (SW) algorithm calculates the Brightness Temperature of band 10 with combining some coefficient and Land Surface Emissivity (LSE) is considered to estimate the Land Surface Temperature (LST) for each ground pixel. After that, some field survey record has been collected to validate this model. The comparative transect study is finalized to validate this model.