Combining Spatiotemporally Global and Local Interpolations Improves Modeling of Annual Land Surface Temperature Cycles

Annual temperature cycle (ATC) models are widely used to characterize temporally continuous land surface temperature (LST) dynamics within an annual cycle. However, the existing ATC models ignore the spatiotemporally local correlations among adjacent LST pixels and are inadequate for capturing the complex relationships between LSTs and LST-related descriptors. To address these issues, we propose an improved ATC model (termed the ATC_GL), which combines both the spatiotemporally global and local interpolations. Using the random forest (RF) algorithm, the ATC_GL model quantifies the complex relationships between LSTs and LST-related descriptors such as the surface air temperature, normalized difference vegetation index, and digital elevation model. The performances of the ATC_GL and several extensively used LST reconstruction methods were compared under both clear-sky and overcast conditions. In the scenario with randomly missing LSTs, the accuracy of the ATC_GL was 2.3 K and 3.1 K higher than that of the ATCE (the enhanced ATC model) and the ATCO (the original ATC model), respectively. In the scenario with LST gaps of various sizes, the ATC_GL maintained the highest accuracy and was less sensitive to gap size when compared with the ATCH (the hybrid ATC model), Kriging interpolation, RSDAST (Remotely Sensed Daily Land Surface Temperature), and HIT (Hybrid Interpolation Technique). In the scenario of overcast conditions, the accuracy of the ATC_GL was 1.0 K higher than that of other LST reconstruction methods. The ATC_GL enriches the ATC model family and provides enhanced performance for generating spatiotemporally seamless LST products with high accuracy.