Daily 1-km all-weather land surface temperature dataset for the Chinese landmass and its surrounding areas (TRIMS LST; 2000-2021)
File name: the temperature data is stored in geotif format, and each geotif file is about 130mb. The data of different years are stored in the folder named "year", and the data of this year are stored in two folders, day and night. The specific file name is "DDD. TIF", where DDD represents the specific date.
The data format is: floating point
Usage: GIS software or MATLAB software can be directly used for reading and subsequent processing;
Data resolution: daily, 1km.
Data time: consistent with the transit time of aqua MODIS.
Key parameters of data set:
(1) Projection mode: Albers equal area
(2) Missing values: uniformly marked with 0
Zhou, J., Zhang, X., Tang, W., Ding, L., Ma, J., Zhang, X. (2021). Daily 1-km all-weather land surface temperature dataset for the Chinese landmass and its surrounding areas (TRIMS LST; 2000-2021). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.271252. CSTR: 18406.11.Meteoro.tpdc.271252. (Download the reference： RIS | Bibtex )Related Literatures:
1. Zhang, X., Zhou, J., Göttsche, F., Zhan, W., Liu, S., & Cao, R. (2019). A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations. IEEE Transactions on Geoscience and Remote Sensing, 57, 4670–4691. https://doi.org/10.1109/TGRS.2019.2892417( View Details | Download | Bibtex)
2. Zhou, J., Zhang, X., Zhan, W., Göttsche, F.-M., Liu, S., Olesen, F.-S., Hu, W., & Dai, F. (2017). A thermal sampling depth correction method for land surface temperature estimation from satellite passive microwave observation over barren land. IEEE Transactions on Geoscience and Remote Sensing, 55, 4743–4756. https://doi.org/10.1109/TGRS.2017.2698828( View Details | Download | Bibtex)
Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.
A method to estimate all-weather LST based on the integration of multi-source remote sensing observations (No:41871241)
Integration and Demonstration of Monitoring and Early Warning Technology and Equipment for Debris Flow in Complex Mountainous Areas (No:2018YFC1505205)
All-Weather Land Surface Temperature at High Spatial Resolution: Validation and Applications (No:59318)
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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)
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