Modeling Year-to-Year Variations of Clear-Sky Land Surface Temperature Using Aqua/MODIS Data

Land surface temperature (LST) and its annual or inter-annual variations play an important role in understanding global climate change, urban heat island, and the process of land-atmosphere energy exchange. Many annual temperature cycle (ATC) models [i.e., ATC with three or five parameters (ACP3 or ACP5)] have been proposed to analyze the annual variations of LST in the past decades. In this study, two year-to-year continuous and derivable models (YYCD_ACP3 and YYCD_ACP5 models) were proposed to model several years of ATCs. The fitting results of the YYCD_ACP3 model with global Aqua/MODIS daytime LSTs from 2014 to 2018 show that the YYCD_ACP3 model achieved a good performance in fitting the time-series LSTs with an overall normalized root-mean-square error (NRMSE) of 0.21, coefficient of determination (R
²
) of 0.74, and refined index of agreement (
$d$
) of 0.85. In addition, the modeling results of ten representative samples covering different climatic conditions and land cover worldwide show that, except for two sites located in tropical and Antarctic, the YYCD_ACP3 model could show a good performance with R
²
greater than 0.6. Although the ACP3 model shows similar performance to the YYCD_ACP3 model, the fitting curve of the YYCD_ACP3 model is continuous and smooth for describing the interannual variations of LST. When the LSTs of 2014–2018 are fitted as a whole by using both models, the YYCD_ACP3 model shows a slightly better performance than that of the ACP3 model. The application of the YYCD_ACP3 model with the global MODIS LSTs from 2003 to 2018 indicates that the results of the YYCD_ACP3 model have the potential to reveal the interannual variations of LST. Therefore, we conclude that the YYCD models are valuable for modeling the variations of LST over several years and can be widely applied.