Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-651-2022
https://doi.org/10.5194/essd-14-651-2022
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

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Cited articles

Aguilar-Lome, J., Espinoza-Villar, R., Espinoza, J. C., Rojas-Acuña, J., Willems, B. L., and Leyva-Molina, W. M.: Elevation-dependent warming of land surface temperatures in the Andes assessed using MODIS LST time series (2000–2017), Int. J. Appl. Earth Obs. Geoinf., 77, 119–128, https://doi.org/10.1016/j.jag.2018.12.013, 2019. 
Anderson, M. C., Kustas, W. P., Norman, J. M., Hain, C. R., Mecikalski, J. R., Schultz, L., González-Dugo, M. P., Cammalleri, C., d'Urso, G., Pimstein, A., and Gao, F.: Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery, Hydrol. Earth Syst. Sci., 15, 223–239, https://doi.org/10.5194/hess-15-223-2011, 2011. 
Bai, L., Long, D., and Yan, L.: Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land, Water Resour. Res., 55, 1105–1128, https://doi.org/10.1029/2018WR024162, 2019. 
Cheng, J., Dong, S., and Shi, J.: 1 km seamless land surface temperature dataset of China (2002-2020), edited by: Natl. Tibet. Plateau Data Center, https://doi.org/10.11888/Meteoro.tpdc.271657, National Tibetan Plateau Data Center, 2021. 
Choi, Y. Y. and Suh, M. S.: Development of Himawari-8/Advanced Himawari Imager (AHI) land surface temperature retrieval algorithm, Remote Sens.-Basel, 10, 1–20, https://doi.org/10.3390/rs10122013, 2013. 
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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.
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