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

Abstract

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 1.23 K, with an improvement by 0.57 K compared with the GSC algorithm. For the validation with 71 clear‐sky Landsat 8 images, the RMSE of the PSC algorithm was 1.77 K 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 0.47 K. 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.

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

“…In addition to the previous study, Zhang et al [53] also investigated the accuracy of SCA using Landsat 8 imagery and SURFRAD measurements using 40 Landsat 8 scenes acquired in different seasons and different years, and they obtained 1.96 K RMSE. Wang et al [54] reported that Practical Single-Channel Algorithm (PSCA) and generalized SCA provided 1.77 K and 2.24 K RMSE, respectively, in line with our SCA results based on Landsat 8 and Sobrino et al's LSE model. Sekertekin [59] The limitations of this study and future investigations can be reported as follows: (1) Thermal bands have a native spatial resolution of 120 m, 60 m and 100 m for Landsat 5 TM, 7 ETM+, and 8 TIRS, respectively, but they are delivered by USGS at 30-m after cubic convolution resampling.…”

“…The cross-validation method considers a well-validated LST product as a reference and compares the satellite-derived LST with the referenced (well-validated) LST derived from other satellites [7]. The T-based method, used by many researchers and also considered in this study, directly compares the satellite-based LST with ground-based LST measurements at the satellite overpass [46,[48][49][50][51][52][53][54][55]. The main advantage of the T-based method is that it enables evaluating the radiometric quality of the satellite sensor and the performance of LST retrieval methods depending on atmospheric and emissivity parameters.…”

“…Zhang et al [53] also investigated the accuracy of SCA using Landsat 8 imagery and Surface Radiation Budget Network (SURFRAD) measurements. Wang et al [54] proposed a Practical Single-Channel Algorithm (PSCA) using Sobrino et al's LSE model. Sekertekin [59] used Skoković et al's LSE model [60] and compared RTE-based LST from Landsat 8 with SURFRAD measurements.…”

An Atmospheric Correction Using High Resolution Numerical Weather Prediction Models for Satellite-Borne Single-Channel Mid-Wavelength and Thermal Infrared Imaging Sensors

“…As seen in Figures 4 and 5, the model bias of atmospheric downwelling radiances was significant for both VIIRS I4 and I5 bands, while that of the atmospheric transmittance values was relatively insignificant. The atmospheric transmittance could be directly calculated from optical thickness values with the proposed regression model in Equation (11). On the other hand, only path radiance and extinction were considered in the calculation of atmospheric radiances.…”

“…Conventional single-channel atmospheric correction methods are based on radiative transfer codes or regression models; those require ancillary atmospheric data such as atmospheric profiles, total column precipitable water vapor contents (TPW), and near-surface air temperature [2,4,[11][12][13]. Some recent studies used numerical weather prediction (NWP) models as input atmospheric information for single-channel atmospheric corrections [14][15][16][17].…”