Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm.

Area of Science:

• Earth Observation
• Remote Sensing
• Atmospheric Science

Background:

• Land surface temperature (LST) is a critical variable in Earth system science.
• Satellite remote sensing provides essential LST data, with Landsat-8 Thermal Infrared Sensor (TIRS) offering new public domain data.
• Accurate LST retrieval is vital for various environmental applications.

Purpose of the Study:

• To adjust the split window algorithm (SWA) for Landsat-8 TIRS data.
• To investigate the impact of atmospheric transmittance and land surface emissivity (LSE) on SWA performance.
• To assess the accuracy of the developed SWA for LST estimation.

Main Methods:

• Developed an adjusted split window algorithm (SWA) utilizing atmospheric transmittance and land surface emissivity (LSE) as inputs for TIRS.
• Reviewed various methods for estimating SWA input parameters.
• Conducted a sensitivity analysis of the SWA to input parameter misestimation.
• Validated the adjusted SWA using simulated Modtran data.

Main Results:

• The adjusted SWA demonstrated good performance for LST retrieval from Landsat-8 TIRS.
• The root mean square error (RMSE) for simulated LST was calculated to be 0.93 °C.
• Sensitivity analysis identified key parameters affecting SWA accuracy.

Conclusions:

• The adjusted split window algorithm represents a significant advancement for LST determination using Landsat-8 TIRS.
• This work contributes to more accurate land surface temperature mapping from satellite data.
• Further research can build upon this adjusted SWA for improved environmental monitoring.