0.02° seamless hourly land surface temperature dataset over East Asia (2016-2021)- A Big Earth Data Platform for Three Poles

Hourly spatially complete land surface temperature (LST) products have a wide range of applications in many fields such as freeze-thaw state monitoring and summer high temperature heat wave monitoring. Although the LST retrieved from thermal infrared (TIR) remote sensing observations has high accuracy, it is spatially incomplete due to the influence of cloud, which heavily limits the application of LST. LST simulated by land surface models (LSM) is with high temporal resolution and spatiotemporal continuity, while the spatial resolution is relatively coarse and the accuracy is poor. Therefore, fusing the remote sensing retrieved LST and the model simulated LST is an effective way to obtain seamless hourly LST. The authors proposed a fusion method to generate 0.02° hourly seamless LST over East Asia and produced the corresponding data set.

This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ verification results show that the root mean square error (RMSE) of the seamless LST is about 3k.

The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively. The time period is 2016-2021 over (0-60°N, 80°E-140°E).

File naming and required software

File naming convention: AHI_ ALLSKY_ LST_ YYYYMMDDHH_ Vn. tif/ AHI_ ALLSKY_ QC_ YYYYMMDDHH_ Vn. TIF (AHI indicates Himawari-8/AHI LST based; allsky indicates all-weather LST; LST indicates LST product / QC represents the quality identification; yyyy indicates the year of the product; mm indicates the month of the product; DD indicates the date of the product; HH indicates the UTC hours of the product; VN indicates the version number of the product
Data format: tif
The data storage types are as follows:
Lst: the data type is uint16, the unit is k, the scaling factor is 0.01, that is, the real LST = read LST * 0.01, and the filling value is 65535
QC: the data type is int8, unused digits are filled with 0 value, the right side is low and the left side is high
Data reading method: it can be read by MATLAB, Python and other programming languages, or it can be visualized in envi or ArcGIS and other software.
Data version No.: V1

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Cite as:

Cheng, J., Dong, S., Shi, J. (2022). 0.02° seamless hourly land surface temperature dataset over East Asia (2016-2021). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Cryos.tpdc.272511. CSTR: 18406.11.Cryos.tpdc.272511. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170( View Details | Bibtex)

2. Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.( View Details | Bibtex)

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

Support Program

Second Tibetan Plateau Scientific Expedition Program

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