ABSTRACT
The land surface temperature (LST) is a highly dynamic parameter that drives many terrestrial physical processes and is observed with satellite remote sensing of thermal infrared radiation. However, the analysis of satellite LST is complicated due to cloud gaps and the pronounced spatiotemporal variability of LST. A prominent way to overcome these limitations is by monitoring the annual LST cycle (ATC) through time series analysis. In particular, the ATC of mid-latitude regions can be approximated by a sine function with three free parameters. These spatially distributed parameters are called the annual cycle parameters (ACP) and provide a robust estimate of the LST seasonal characteristics and a gap-free representation of the surface's thermal characteristics. In addition, these data can also be used to derive virtual LST patterns for every day of year. This work presents the ACP derived from MODIS collection-6 LST data for Europe and North Africa and introduces a new parameter, the coefficient of determination (R2). Because the sine function performs badly over the tropics, an alternative ACP model with a second harmonic function is proposed, which results in a much better fit. Among the large number of potential applications, examples of ACP-based SUHI analysis and LST downscaling are presented.
