Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data

TLDR

Land surface temperature derived from high‑resolution remote sensing is crucial for environmental studies, and the Landsat‑8 TIRS sensor enables split‑window algorithms that were not possible with earlier Landsat platforms. The authors propose applying single‑channel and split‑window algorithms to Landsat‑8 TIRS data for land surface temperature retrieval. They tested these algorithms using simulated data from forward atmospheric profile databases and emissivity spectra from spectral libraries. The methods achieve mean errors below 1.5 K, with the split‑window approach slightly outperforming the single‑channel method as atmospheric water vapor increases.

Abstract

The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 μm, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.