LAUSR

Land surface temperature (LST) is a key surface feature parameter. Temperature and emissivity separation (TES) and split-window (SW) algorithms are two typical LST estimation algorithms that have been applied to a variety of sensors to generate LST products. The TES algorithm can synchronously obtain LST and emissivity, but it requires high accuracy for atmospheric correction of the thermal infrared (TIR) data and does not perform well for surfaces with low spectral emissivity contrast. On the contrary, the SW algorithm can retrieve LST without detailed atmospheric data because the linear or nonlinear combination of brightness temperatures in the two adjacent TIR channels can reduce the atmospheric effect; however, this algorithm requires prior accurate pixel emissivity. Combining the two algorithms can improve the accuracy of LST estimation because the emissivity calculated from the TES algorithm can be used in the SW algorithm, and the LST from the SW algorithm can then be applied to the TES algorithm as an initial value to refine emissivity and LST. This paper investigates the aforementioned hybrid algorithm using Chinese Gaofen-5 satellite data, which will provide four-channel data for TIR at 40 m for synchronously retrieving LST and emissivity. The results showed that the hybrid algorithm was less sensitive to instrument noise and atmospheric data error, and can obtain LST and emissivity with an error less than 1 K and 0.015, respectively, which is better than those obtained with the single TES or SW algorithm. Finally, the hybrid algorithm was tested in simulated image and ground-measured data, and obtained accurate results.