An enhanced single-channel algorithm for retrieving land surface temperature from Landsat series data

Land surface temperature (LST) is a critical parameter in the physics of Earth surface processes and is required for many applications related to ecology and environment. Landsat series satellites have provided more than 30years of thermal information at medium spatial resolution. This paper proposes an enhanced single-channel algorithm (SCen) for retrieving LST from Landsat series data (Landsat 4 to Landsat 8). The SCen algorithm includes three atmospheric functions (AFs), and the latitude and acquisition month of Landsat image were added to the AF models to improve LST retrieval. Performance of the SCen algorithm was assessed with both simulated and in situ data, and accuracy of three single-channel algorithms (including the monowindow algorithm developed by Qin et al., SCQin, and the generalized single-channel algorithm developed by Jimenez-Munoz and Sobrino, SCJ&S) were compared. The accuracy assessments with simulated data had root-mean-square deviations (RMSDs) for the SCen, SCJ&S, and SCQin algorithms of 1.363K, 1.858K, and 2.509K, respectively. Validation with in situ data showed RMSDs for the SCen and SCJ&S algorithms of 1.04K and 1.49K, respectively. It was concluded that the SCen algorithm is very operational, has good precision, and can be used to develop an LST product for Landsat series data.