A Practical Single-Channel Algorithm for Land Surface Temperature Retrieval: Application to Landsat Series Data

The single-channel (SC) algorithm has been widely used to retrieve land surface temperature from Landsat series data for its simplicity and requirement of only one thermal infrared channel. The main error sources of the existing SC algorithms are the linearization of the Planck's function and atmospheric correction. This paper proposed a practical SC (PSC) algorithm to retrieve land surface temperature from Landsat series data aiming at avoiding the aforementioned error sources. The sensitivity of the PSC algorithm to the input parameters was analyzed. The performance of the proposed PSC algorithm was compared with the most commonly used SC algorithm (the generalized SC, GSC) using a simulation data set and satellite measurements. Results showed that the PSC algorithm was less sensitive to uncertainties in the input parameters than the GSC algorithm. When validated with the simulation data set, the root-mean-square error (RMSE) of the PSC algorithm was 1.23K, with an improvement by 0.57K compared with the GSC algorithm. For the validation with 71 clear-sky Landsat 8 images, the RMSE of the PSC algorithm was 1.77K when using the measurements from U.S. surface radiation budget network as real values. Compared with the GSC algorithm, the RMSE improvement for the PSC algorithm was 0.47K. We conclude that the PSC algorithm is more accurate than the GSC algorithm and the sensitivity to input parameters in the PSC algorithm is weaker than in the GSC algorithm.