Enhancing landsat 8 land surface temperature retrieval in coastal regions using MODIS atmospheric water vapor data

The Landsat series has long served as a critical data source for remote sensing-based land surface temperature (LST) retrieval. Accurate LST estimation requires parameters like emissivity and atmospheric transmittance, whose uncertainties can introduce significant errors. This study presents the MODIS three-channel weighted algorithm, which offers robust atmospheric water vapour retrieval capabilities, particularly in regions with high spatial variability such as coastal areas. By analysing variations in atmospheric water vapour across different land cover types, we incorporate these values into Qin's mono-window algorithm for LST calculation and validate the results through two approaches: UAV-based validation and cross-validation. The UAV-based measurements were used to directly compare observed and modelled LST, achieving a high accuracy with an RMSE of 1.01. Cross-validation against existing satellite-derived LST products further confirmed the model's reliability, demonstrating robust consistency with an RMSE of 1.259 K. This integration provides a reliable solution for addressing atmospheric variability in heterogeneous landscapes, with potential applications in climate monitoring and land management.