Modeling Daily Temperatures Via a Phenology-Based Annual Temperature Cycle Model

High spatiotemporal resolution land surface temperature (LST) plays an important role in various environment applications. However, the limitation of thermal infrared sensors and effect clouds other atmospheric conditions result discontinuous daily observations Moderate Resolution Imaging Spectroradiometer (MODIS). Annual cycle (ATC) models can help to supply continuous LSTs via limited observations, but these ATC seldom consider disturbance weather or cover change. On hand, spatial interpolation techniques also limit implementation when available data one day several days are not able obtain enough information for LST reconstruction. The objective this study is propose a phenology-based model (termed PATC), which takes phenology change local into account, reconstruct unscanned at annual scale. Daily MODIS collected 2015 were utilized analyze performance PATC compared with models. Results show that improved accuracy by 1.6 0.5 K classic daytime nighttime, respectively. Compared enhanced model, shows better higher accuracies, especially during growing season vegetation daytime. Future research may focus on incorporation Landsat diurnal implement reconstruction