A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis

Abstract. Daily mean land surface temperatures (LSTs) acquired from polar-orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors from polar-orbiters can only sample the surface effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (ΔT_sb) on the daily mean LST (T_dm) estimated with the traditional method, which uses the averages of clear-sky LST observations directly as the T_dm. Several methods have been proposed for the estimation of the T_dm, yet they become less capable of generating spatiotemporally seamless T_dm across the globe. Based on MODIS and reanalysis data, here we proposed an improved annual and diurnal temperature cycle-based framework (termed the IADTC framework) to generate global spatiotemporally seamless T_dm products ranging from 2003 to 2019 (named as the GADTC products). The validations show that the IADTC framework reduces the systematic ΔT_sb significantly. When validated only with in situ data, the assessments show that the mean absolute errors (MAEs) of the IADTC framework are 1.4 K and 1.1 K for SURFRAD and FLUXNET data, respectively; and the mean biases are both close to zero. Direct comparisons between the GADTC products and in situ measurements indicate that the MAEs are 2.2 K and 3.1 K for the SURFRAD and FLUXNET datasets, respectively; and the mean biases are −1.6 K and −1.5 K for these two datasets, respectively. By taking the GADTC products as references, further analysis reveals that the T_dm estimated with the traditional averaging method yields a positive systematic ΔT_sb of greater than 2.0 K in low- and mid-latitude regions while of a relatively small value in high-latitude regions. Although the global mean LST trend (2003 to 2019) calculated with the traditional method and the IADTC framework is relatively close (both between 0.025 to 0.029 K/year), regional discrepancies in LST trend does occur – the pixel-based MAE in LST trend between these two methods reaches 0.012 K/year. We consider the IADTC framework can guide the further optimization of T_dm estimation across the globe; and the generated GADTC products should be valuable in various applications such as global and regional warming analysis. The GADTC products are freely available at https://doi.org/10.5281/zenodo.6287052 (Hong et al., 2022).