An Integrated Method for the Generation of Spatio-Temporally Continuous LST Product With MODIS/Terra Observations

Xiao, Yao; Zhao, Wei; Ma, Mingguo; Yu, Wei; Fan, Lei; Huang, Yajun; Sun, Xupeng; Lang, Qing

doi:10.1109/tgrs.2023.3254598

Cited by 5 publications

(6 citation statements)

References 59 publications

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“…The bias correction ensures that the modeled state is continually adjusted towards the observed values, thereby improving the accuracy of the skin temperature calculations on an incremental, semi-daily, or daily basis (Radakovich et al, 2001). The accuracy of GLDAS LST has been demonstrated by various studies with mean bias error (MBE) ranging from −4.27 to 8.65 K and root mean square error (RMSE) ranging from 3.0 to 6.02 K (Zhang et al, 2021;Xiao et al, 2023). Specifically, the 0.25 • 3 h LST from the GLDAS NOAH model between January 2000 and December 2022 was used as another input of the RTM method.…”

Section: Satellite Data and Reanalysis Datamentioning

confidence: 99%

“…Fortunately, machine learning has been reported as effective in enhancing the spatial resolution of remote sensing images. Specifically, the random forest (RF) algorithm has shown good performance in mapping the correlation between LST with finer resolution and its descriptors with coarser resolution (Xiao et al, 2023;B. Li et al, 2021;Xu et al, 2021;Zhao and Duan, 2020;Yoo et al, 2020).…”

Section: Module Ii: the Rfstm Approachmentioning

confidence: 99%

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

Tang,

Zhou,

et al. 2024

Earth Syst. Sci. Data

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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).

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Section: Satellite Data and Reanalysis Datamentioning

confidence: 99%

Section: Module Ii: the Rfstm Approachmentioning

confidence: 99%

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

Tang,

Zhou,

et al. 2024

Earth Syst. Sci. Data

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“…Note that FY-4A data was not used for producing the CLDAS data, and CLDAS and FY-4A are independent of each other [56]. In addition, some previous studies have used CLDAS data to estimate all-weather LST [34], [57], [58].…”

Section: ) Cldas Datamentioning

confidence: 99%

“…This result illustrates that if the proportion of clear-sky pixels in the study area is high, it is feasible to estimate the NRT-AW LST only using the spatial fusion algorithm. In addition, the accuracy of the NRT-AW LST estimation increases with the proportion of clear sky LST pixels [58]. Therefore, we mainly used the spatial fusion algorithm to estimate the NRT-AW LSTs when the percentages of the clear-sky pixels of FY-4A LST are more than 70% (the minimum coverage rates of the NRT-AW LST estimated by the temporal fusion above 70% in Fig.…”

Section: ) Evaluation Of the Spatial Fusion Algorithmmentioning

confidence: 99%

“…In this process, the temporal fusion results can provide more training samples for RF model building. In many previous studies, RF was directly used to fuse TIR remote sensing LST and reanalysis data [32], [58], [72]. However, when the percentage of clear sky in the study area at the target moment is too low, it is difficult to directly use RF model to complete the data fusion for obtaining all-weather LSTs with a good accuracy.…”

Section: ) Outlook Of Stefmentioning

confidence: 99%

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Near-Real-Time Estimation of Hourly All-Weather Land Surface Temperature by Fusing Reanalysis Data and Geostationary Satellite Thermal Infrared Data

Ding,

Zhou,

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER

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A Two-Step Framework for Generating 0.01°, Hourly, and Gapless Land Surface Temperature

Ma,

Guo,

et al. 2025

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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An Integrated Method for the Generation of Spatio-Temporally Continuous LST Product With MODIS/Terra Observations

Cited by 5 publications

References 59 publications

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

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

Near-Real-Time Estimation of Hourly All-Weather Land Surface Temperature by Fusing Reanalysis Data and Geostationary Satellite Thermal Infrared Data

A Two-Step Framework for Generating 0.01°, Hourly, and Gapless Land Surface Temperature

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