TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Abstract

Abstract. Land surface temperature (LST) is a key variable within 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, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally 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 LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size

“…A daily 1 km all-weather LST dataset for 2000-2021 retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on Terra and Aqua satellites was used to quantify the SUHIs in the YRDUA region [50]. A 500 m-resolution land cover dataset for 2020 from Terra and Aqua satellites (MCD12Q1) was used to extract the land cover types of croplands, forests, and large cities [51].…”

“…When defining SUHI as the additional anomaly on background condition [16], ∆T i , T i , and TB i are the SUHI intensity and actual and background LSTs at grid point i in the areas that have an SUHI phenomenon. Actual LSTs in the areas that have no SUHIs are well-associated with climate, geographical, topographical, and biophysical conditions, all of which have been well-retrieved from high-quality satellite datasets without gaps and missing values [50,56]. When the LSTs in the areas that have no SUHIs are selected as the background condition and fitted by machine-learning algorithms with geographical and biophysical parameters, ∆T i is the background simulation error at grid point i.…”

“…The nonurban LST is well-associated with climate, geographical, topographical, and biophysical conditions, all of which have been well-retrieved from high-quality satellite datasets without gaps or missing values [50,56]. RF regression models were with geographical, biophysical, and urbanization parameters to fit the nonurban LST in the background, cropland, and forest zones and estimate the urban LST background and SUHI intensity at each grid point in the SUHI zone.…”

Remote Sensing Monitoring of Grassland Locust Density Based on Machine Learning

“…The main input data of the method were Terra/Aqua MODIS LST products and GLDAS data, and the auxiliary data included the vegetation index and surface albedo provided by satellite remote sensing. The method fully utilized the high-frequency components, low-frequency components, and spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data and, finally, it reconstructed a high-quality all-weather land surface temperature dataset [8]. The dataset can be downloaded from the following website: https://data.tpdc.ac.cn/en/data/05d6e569-6d4b-43c0-96aa-5584484259f0/ (accessed on 18 February 2024).…”