Daily 1-km all-weather land surface temperature dataset for Western China (TRIMS LST-TP; 2000-2021) V2
File Name: The temperature data is stored in the GEORTIF format and approximately 51 MB per GEOTIFF file. The data for different years are stored in "Year"-named folders, which are stored in the day DAY and NIGHT at night, respectively. The specific file is named as a "ddd.tif", where dd represents a specific date.
Data format: integer (2003-2010,2013-2018), floating-point type (2011-2012);
The data reading mode: all the data in the data set can be opened directly by using relevant software in the field of GIS and remote sensing (Arcmap, QGIS, ENVI, Erdas, etc.), or a similar matlab can be used, python and the like provide software for remote sensing image processing support for data reading, pre-processing and the like;
Usage: The surface temperature value at a certain pixel = integer data at the pixel. /100 (2003-2010,2013-2018); surface temperature value at certain pixel = floating point type data at the pixel. /100 (2011-2012);
Data resolution: day by day,1 km.
Data time: near the Aqua MODIS transit time. For each pixel, the transit time is the same as the AQUA MODIS transit time at the image element (at noon/ about a half a. m. in the local solar).
Data set key parameters:
(1) Projection method: Albers equal area
(2) Missing value: Unified by 00000 (the missing data is about 0.1% ~ 0.5% of the total data)
Zhou, J., Zhang, X., Tang, W., Ding, L., Ma, J., Zhang, X. (2019). Daily 1-km all-weather land surface temperature dataset for Western China (TRIMS LST-TP; 2000-2021) V2. A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.270953. CSTR: 18406.11.Meteoro.tpdc.270953. (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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|East: 104.00||West: 72.00|
|South: 20.00||North: 45.00|
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