Disaggregation of Remotely Sensed Land Surface Temperature: A Generalized Paradigm

Abstract

The environmental monitoring of earth surfaces requires land surface temperatures (LSTs) with high temporal and spatial resolutions. The disaggregation of LST (DLST) is an effective technique to obtain high-quality LSTs by incorporating two subbranches, including thermal sharpening (TSP) and temperature unmixing (TUM). Although great progress has been made on DLST, the further practice requires an in-depth theoretical paradigm designed to generalize DLST and then to guide future research before proceeding further. We thus proposed a generalized paradigm for DLST through a conceptual framework (C-Frame) and a theoretical framework (T-Frame). This was accomplished through a Euclidean paradigm starting from three basic laws summarized from previous DLST methods: the Bayesian theorem, Tobler's first law of geography, and surface energy balance. The C-Frame included a physical explanation of DLST, and the T-Frame was created by construing a series of assumptions from the three basic laws. Two concrete examples were provided to show the advantage of this generalization. We further derived the linear instance of this paradigm based on which two classical DLST methods were analyzed. This study finally discussed the implications of this paradigm to closely related topics in remote sensing. This paradigm develops processes to improve an understanding of DLST, and it could be used for guiding the design of future DLST methods.