A global seamless 1&thinsp;km resolution daily land surface temperature dataset (2003–2020)

Zhang, Tao; Zhou, Yuyu; Zhu, Zhengyuan; Li, Xiaoma; Asrar, Ghassem R.

doi:https://doi.org/10.5194/essd-14-651-2022

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.

https://doi.org/10.5194/essd-14-651-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 2

Data description paper

|

15 Feb 2022

Data description paper |

| 15 Feb 2022

A global seamless 1 km resolution daily land surface temperature dataset (2003–2020)

Tao Zhang, Yuyu Zhou, Zhengyuan Zhu, Xiaoma Li, and Ghassem R. Asrar

Supplement

https://doi.org/10.5194/essd-14-651-2022-supplement

Data sets

A global seamless 1 km resolution daily land surface temperature dataset (2003–2020) Tao Zhang, Yuyu Zhou, Zhengyuan Zhu, Xiaoma Li, and Ghassem Asrar https://doi.org/10.25380/iastate.c.5078492

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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.