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The Qinghai Tibet Plateau is a sensitive region of global climate change. Land surface temperature (LST), as the main parameter of land surface energy balance, characterizes the degree of energy and water exchange between land and atmosphere, and is widely used in the research of meteorology, climate, hydrology, ecology and other fields. In order to study the land atmosphere interaction over the Qinghai Tibet Plateau, it is urgent to develop an all-weather land surface temperature data set with long time series and high spatial-temporal resolution. However, due to the frequent cloud coverage in this region, the use of existing satellite thermal infrared remote sensing land surface temperature data sets is greatly limited.

Compared with the previous version released in 2019, Western China Daily 1km spatial resolution all-weather land surface temperature data set (2003-2018) V1, this data set (V2) adopts a new preparation method, namely satellite thermal infrared remote sensing reanalysis data integration method based on new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. This method makes full use of the high frequency and low frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data.

The evaluation results show that this data set has good image quality and accuracy, which is not only seamless in space, but also highly consistent with the amplitude and spatial distribution of 1 km daily Aqua MODIS LST products widely used in current academic circles. When MODIS LST was used as the reference value, the mean deviation (MBE) of the data set in daytime and nighttime was -0.28 K and -0.29 K respectively, and the standard deviation (STD) of the deviation was 1.25 K and 1.36 K respectively. The test results based on the measured data of six stations in the Qinghai Tibet Plateau and Heihe River Basin show that under clear sky conditions, the data set is highly consistent with the measured LST in daytime / night, and its MBE is -0.42-0.25 K / - 0.35-0.19 K; The root mean square error (RMSE) was 1.03 ~ 2.28 K / 1.05 ~ 2.05 K; Under the condition of non clear sky, the MBE of this data set in daytime / night is -0.55 ~ 1.42 K / - 0.46 ~ 1.27 K; The RMSE was 2.24-3.87 K / 2.03-3.62 K. Compared with the V1 version of the data, the two kinds of all-weather land surface temperature show the characteristics of seamless (i.e. no missing value) in the spatial dimension, and in most areas, the spatial distribution and amplitude of the two kinds of all-weather land surface temperature are highly consistent with MODIS land surface temperature. However, in the region where the brightness temperature of AMSR-E orbital gap is missing, the V1 version of land surface temperature has a significant systematic underestimation. The mass of trims land surface temperature is close to that of V1 version outside AMSR-E orbital gap, while the mass of trims is more reliable inside the orbital gap. Therefore, it is recommended that users use V2 version.

The time span of this data set is from 2000 to 2021 and will be updated continuously; The time resolution is twice a day (corresponding to the two transit times of aqua MODIS in the daytime and at night); The spatial resolution is 1 km. In order to facilitate the majority of colleagues to carry out targeted research around the Qinghai Tibet Plateau and its adjacent areas, and reduce the workload of data download and processing, the coverage of this data set is limited to Western China and its surrounding areas (72 ° E-104 ° E，20 ° N-45 ° N）。 Therefore, this dataset is abbreviated as trims lst-tp (thermal and reality integrating modem resolution spatial seamless LST – Tibetan Plateau) for user's convenience.

File naming and required software

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)

Data Citations Data citation guideline What's data citation?

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 )

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.

Support Program

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