TRIMS LST: A daily 1-km all-weather land surface temperature dataset for the Chinese landmass and surrounding areas (2000–2021)
Abstract. Land surface temperature (LST) is a key variable within the Earth’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 – 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 https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).
Wenbin Tang et al.
Status: final response (author comments only)
RC1: 'Comment on essd-2023-27', Anonymous Referee #1, 24 Apr 2023
- AC1: 'Reply on RC1', Wenbin Tang, 24 Apr 2023
- RC2: 'Comment on essd-2023-27', Anonymous Referee #2, 28 Apr 2023
- RC3: 'Comment on essd-2023-27', Anonymous Referee #3, 10 May 2023
Wenbin Tang et al.
Daily 1-km all-weather land surface temperature dataset for the Chinese landmass and its surrounding areas (TRIMS LST; 2000-2021) https://doi.org/10.11888/Meteoro.tpdc.271252
Wenbin Tang et al.
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The manuscript reported a daily 1-km all-weather LST dataset for the Chinese landmass and surrounding areas from 2000 to 2021 with the special features of the longest time coverage, high image quality, and good accuracy. The study is interesting and fits within the scope of “Earth System Science Data”.
The main concept of the paper (the necessity to obtain long time-series all-weather LST from 2000 to 2021, especially during the temporal gaps of MODIS between 2000 and 2002) is well presented and introduced. The objective for work is laid out clearly in the introduction section.
The adopted approaches and methodologies are detailed, and sound and the supporting visual material is useful to better understand the exposed concepts. The experimental setup for the data acquisition and data processing is well structured and presented.
The results comprehensively demonstrates the data quality of TRIMS LST by comparison with reanalysis data, satellite TIR LST products, validation against in-situ LST, and quantification of the similarity between the TRIMS-Aqua LST and TRIMS-Terra LST time series during the temporal gaps.
Therefore, for final publication, I recommend the manuscript could be accepted after reviewed by editor.