Review of methods for land surface temperature derived from thermal infrared remotely sensed data

Abstract

Land Surface Temperature (LST) is a key parameter in the physical processes of surface energy and water balance at local and global scales. Knowledge of LST provides information on the temporal and spatial variations of the surface equilibrium state and is of fundamental importance in many applications. This paper systematically surveys the methods for LST derived from thermal infrared remotely sensed data. These methods include single-channel, multi-channel, multi-angle, multi-temporal, and hyperspectral retrieval methods. To provide potential LST users with reliable information regarding the quality of the LST product and to provide feedback to the developers of LST retrieval algorithms for future improvement, assessing the accuracy of the retrieved LST is necessary. We review the methods used to validate LST derived from thermal infrared remotely sensed data, including temperature-based, radiance-based, and inter-comparison methods. The advantages and disadvantages of these methods are discussed. Furthermore, we review the temporal and angular normalization methods of satellite-derived LST. Finally, we present suggestions for future research to improve the accuracy of satellite-derived LST.