New hybrid algorithm for land surface temperature retrieval from multiple-band thermal infrared image without atmospheric and emissivity data inputs

Abstract

ABSTRACT Land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing image requires atmospheric and land surface emissivity (LSE) data that are sometimes unattainable. To overcome this problem, a hybrid algorithm is developed to retrieve LST without atmospheric correction and LSE data input, by combining the split-window (SW) and temperature–emissivity separation (TES) algorithms. The SW algorithm is used to estimate surface-emitting radiance in adjacent TIR bands, and such radiance is applied to the TES algorithm to retrieve LST and LSE. The hybrid algorithm is implemented on five TIR bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Analysis shows that the hybrid algorithm can estimate LST and LSE with an error of 0.5–1.5 K and 0.007–0.020, respectively. Moreover, the LST error of the hybrid algorithm is equivalent to that of the original ASTER TES algorithm, involving 1%–2% uncertainty in atmospheric correction. The hybrid algorithm is validated using ground-measured LST at six sites and ASTER LST products, indicating that the temperature difference between the ASTER TES algorithm and the hybrid algorithm is 1.4 K and about 2.5–3.5 K compared to the ground measurement. Finally, the hybrid algorithm is applied to at two places.