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Land surface temperature (LST) is a key parameter in the study of surface energy balance. It is widely used in the fields of meteorology, climate, hydrology, agriculture and ecology. As an important means to obtain global and regional scale LST information, satellite (thermal infrared) remote sensing is vulnerable to the influence of cloud cover and other atmospheric conditions, resulting in temporal and spatial discontinuity of LST remote sensing products, which greatly limits the application of LST remote sensing products in related research fields.

The preparation of this data set is based on the empirical orthogonal function interpolation method, using Terra / Aqua MODIS surface temperature products to reconstruct the lst under ideal clear sky conditions, and then using the cumulative distribution function matching method to fuse era5 land reanalysis data to obtain the lst under all-weather conditions. This method makes full use of the spatio-temporal information of the original MODIS remote sensing products and the cloud impact information in the reanalysis data, alleviates the impact of cloud cover on LST estimation, and finally reconstructs the high-quality global 0.05 ° spatio-temporal continuous ideal clear sky and all-weather LST data set.

This data set not only realizes the seamless coverage of space-time, but also has good verification accuracy. The reconstructed ideal clear sky LST data in the experimental areas of 17 land cover types in the world, the average correlation coefficient (R) is 0.971, the bias (bias) is -0.001 K to 0.049 K, and the root mean square error (RMSE) is 1.436 K to 2.688 K. The verification results of the reconstructed all-weather LST data and the measured data of ground stations: the average R is 0.895, the bias is 0.025 K to 2.599 K, and the RMSE is 4.503 K to 7.299 K.

The time resolution of this data set is 4 times a day, the spatial resolution is 0.05 °, the time span is 2002-2020, and the spatial range covers the world.

File naming and required software

File naming:
(1) Global 0.05° spatiotemporal continuous ideal clear-sky land surface temperature dataset, file naming:
<MOD11C1(MYD11C1)_YYYYDDD_Clear-sky>.h5, MOD11C1(MYD11C1) represents the MODIS LST product of Terra (Aqua) sun-synchronous satellite, <YYYY> is the year, <DDD> represents the day of year, and <Clear-sky> represents it is ideal clear-sky MODIS LST product.
(2) Global 0.05° spatiotemporal continuous all-weather land surface temperature dataset, file naming:
<MOD11C1(MYD11C1)_YYYYDDD_All-weather>.h5, MOD11C1(MYD11C1) represents the MODIS LST product of Terra (Aqua) sun-synchronous satellite, <YYYY> is the year, <DDD> represents the day of year, and < All-weather > represents it is all-weather MODIS LST product.
How to use:
The data can be read in various programming languages such as Matlab, Python, IDL, etc., and can also be visualized in Panoply. When reading data from a scientific data set, it needs to be multiplied by its corresponding scale factor. For details, please refer to the description documentation.

Data Citations Data citation guideline What's data citation?

Zhao, T., Yu, P. (2021). Global daily 0.05 ° spatiotemporal continuous land surface temperature dataset (2002-2020). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.271663. CSTR: 18406.11.Meteoro.tpdc.271663. (Download the reference： RIS | Bibtex )

1. Yu, P., Zhao, T.J, Shi, J.C., Ran, Y.H., Jia, L., Ji, D.B., & Xue, H.Z. (2022). Global spatiotemporally continuous MODIS land surface temperature dataset. Scientific Data, 9, 143.( View Details | Download | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

CASEarth:Big Earth Data for Three Poles（grant No. XDA19070000） (No:XDA19000000)

Strategic leading science and technology project of Chinese Academy of Sciences (category A)

o Strategic Priority ResearchProgram of the Chinese Academy of Sciences: Grant No. XDA19070204 (No:XDA19070204) [XDA19070204]

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)

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