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

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 (Delta T-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 are becoming less capable of generating spatiotemporally seamless T-dm across the globe. Based on MODIS and reanalysis data, here we propose 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 the GADTC products). The validations show that the IADTC framework reduces the systematic Delta T-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 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 and 3.1 K for the SURFRAD and FLUXNET datasets, respectively, and the mean biases are -1.6 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 Delta T-sb of greater than 2.0 K in low-latitude and midlatitude 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 yr(-1)), regional discrepancies in LST trend do occur - the pixel-based MAE in LST trend between these two methods reaches 0.012 K yr(-1). 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 (Hong et al., 2022).