Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-651-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-651-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Feb 2022
Data description paper |
| 15 Feb 2022
| 15 Feb 2022
A global seamless 1 km resolution daily land surface temperature dataset (2003–2020)
Tao Zhang
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, 50011, USA
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, 50011, USA
Zhengyuan Zhu
Department of Statistics, Iowa State University, Ames, IA, 50011, USA
Xiaoma Li
College of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha, Hunan, 410128, China
Ghassem R. Asrar
Universities Space Research Association, Columbia, MD, 21046, USA
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Short summary
We generated a global seamless 1 km daily (mid-daytime and mid-nighttime) land surface temperature (LST) dataset (2003–2020) using MODIS LST products by proposing a spatiotemporal gap-filling framework. The average root mean squared errors of the gap-filled LST are 1.88°C and 1.33°C, respectively, in mid-daytime and mid-nighttime. The global seamless LST dataset is unique and of great use in studies on urban systems, climate research and modeling, and terrestrial ecosystem studies.
We generated a global seamless 1 km daily (mid-daytime and mid-nighttime) land surface...
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