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Discussion papers
https://doi.org/10.5194/essd-2019-155
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-155
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 11 Oct 2019

Submitted as: data description paper | 11 Oct 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003–2017

Bing Zhao1, Kebiao Mao2,3, Yulin Cai1, Jiancheng Shi4, Zhaoliang Li3, Zhihao Qin3, and xiangjin Meng5 Bing Zhao et al.
  • 1Geomatics College, Shandong University of Science and Technology, Qingdao, 266590, China
  • 2School of Geography, South China Normal University, Guangzhou 510631, China
  • 3Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
  • 4State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China
  • 5School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250100, China

Abstract. Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperatures over large areas. However, there are many missing and low-quality values in satellite-based LST data caused by cloud coverage exceeding 60 % of the global surface every day. This article presents a unique LST dataset in China for 2003–2017, which filters and removes missing values and poor-quality LST pixel values contaminated by clouds from raw LST images and retrieves real surface temperatures under cloud coverage by a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the true LST under cloud coverage, and then the data performance is further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with the in situ observations. For the six natural subregions with different climatic conditions in China, the RMSE ranges from 1.24 °C to 1.58 °C, the MAE varies from 1.23 °C to 1.37 °C, and the R2 ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003–2017, the overall annual mean LST in China shows a weak increase. Moreover, the warming trend was remarkably unevenly distributed over China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region (slope > 0.10, R > 0.71, P  <0.05), and a strong cooling trend was also observed in some parts of the Northeast Region. Seasonally, there was significant warming in the western part in winter, which was most pronounced in December. The reconstructed dataset exhibited significant improvements and can be used for the spatiotemporal evaluation of LST and high temperature and drought monitoring studies. The data are published in the Zenodo at https://doi.org/10.5281/zenodo.3378912 (Zhao et al., 2019).

Bing Zhao et al.
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A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003–2017 B. Zhao, K. Mao, Y. Cai, J. Shi, Z. Li, Z. Qin, and X. Meng https://doi.org/10.5281/zenodo.3378912

Bing Zhao et al.
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Short summary
Land surface temperature is a key variable for climate and ecological environment research. We reconstructed land surface temperature dataset (2003–2017) to take advantage of the ground observation site through building reconstruction model which overcomes the effects of the cloud. The reconstructed dataset exhibited significant improvements and can be used for the spatiotemporal evaluation of land surface temperature and high temperature and drought monitoring studies.
Land surface temperature is a key variable for climate and ecological environment research. We...
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