Comment on essd-2023-27

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Land surface temperature (LST) is a key variable within the Earth&rsquo;s climate system and a necessary input parameter required by numerous land-atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, but cloud contamination results in many spatial missing. To investigate the temporal and spatial variations of LST in China, long-term, high-quality, and spatio-temporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LST. Among satellite TIR LSTs, the MODIS LST is the most commonly used and a few all-weather LST products generated in this way have been reported recently. However, the publicly reported all-weather LSTs are not available during the temporal gaps of MODIS between 2000 and 2002. In this context, we report a daily 1-km all-weather LST dataset for the Chinese landmass and surrounding areas &ndash; TRIMS LST. Different from other products, the TRIMS LST begins on the first day of the new millennium (i.e., January 1, 2000). The TRIMS LST was generated based on the Enhanced Reanalysis and Thermal infrared remote sensing Merging (E-RTM) method. Specifically, the original RTM method was used to generate the TRIMS LST outside the temporal gaps. Two newly developed approaches, including the Random-Forest based Spatio-Temporal Merging (RFSTM) approach and Time-Sequential LST based Reconstruction (TSETR) approach, were used to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps, respectively. Thorough evaluation of the TRIMS LST was conducted. A comparison with the GLDAS and ERA5-Land LSTs demonstrates that TRIMS LST has similar spatial patterns but higher image quality, more spatial details, and no evident spatial discontinuities. Further comparison with MODIS and AATSR LSTs shows that TRIMS LSTs agree well with them, with mean bias deviation (MBD) between -0.40 K and 0.30 K and standard deviation of bias (STD) between 1.17 K and 1.50 K. Validation based on ground measured LST at 19 ground sites showed that the mean bias error (MBE) of the TRIMS LST ranged from -2.26 K to 1.73 K and the root mean square error (RMSE) was 0.80 K to 3.68 K, with no significant difference between the clear-sky and cloudy conditions. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island (UHI) modelling. The TRIMS LST is freely and conveniently available at <a href="https://doi.org/10.11888/Meteoro.tpdc.271252" target="_blank" rel="noopener">https://doi.org/10.11888/Meteoro.tpdc.271252</a> (Zhou et al., 2021).