thermal remote sensing

Temperature is one of the most important physical parameters that control the transfer and exchange of energy between different layers of the earth and the atmosphere. LST estimation methods based on satellite images require surface and atmospheric parameters such as surface emissivity, average air temperature, atmospheric transfer coefficient, and water vapor as input. Uncertainty in these parameters causes errors in the retrieval of land surface temperature. This study aimed to compare the accuracy of different methods for estimating atmospheric water vapor in estimating land surface temperature using Landsat 8 images. In this study, atmospheric water vapor was estimated using FLAASH atmospheric correction methods, MODIS sensor images, and SWCVR method. Then, the impact of atmospheric water vapor on land surface temperature accuracy was investigated using the split window and single-channel methods. Validation of Land surface temperature images was performed using cross-validation and ground measurement methods. Therefore, 20 Landsat 8 images related to 2018 and 2019 were used to estimate atmospheric water vapor by the FLAASH atmospheric correction and SWCVR methods, and land surface temperature estimation. MODIS radiance images were used to estimate atmospheric water vapor and the land surface temperature product of this sensor was used for cross-validation. The surface temperature was measured using a thermometer in places with homogeneous cover, for ground-based validation. Results showed that among water vapor estimation methods, the SWCVR method is more suitable for estimating land surface temperature and the split-window method based on the SWCVR method shows the lowest RMSE and MADE at 3.47 and 3.18. Results of RMSE image classification of split-window algorithm based on the SWCVR showed that 1.67% of the area has an error of more than 4 °C and 98% of the study area has less than 4 °C error.