Articles | Volume 13, issue 8
https://doi.org/10.5194/essd-13-4241-2021
https://doi.org/10.5194/essd-13-4241-2021
Data description paper
 | 
30 Aug 2021
Data description paper |  | 30 Aug 2021

An all-sky 1 km daily land surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data

Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang

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Cited articles

Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N., and Santos, A.: Estimating air surface temperature in Portugal using MODIS LST data, Remote Sens. Environ., 124, 108–121, https://doi.org/10.1016/j.rse.2012.04.024, 2012. 
Benavides, R., Montes, F., Rubio, A., and Osoro, K.: Geostatistical modelling of air temperature in a mountainous region of Northern Spain, Agr. Forest Meteorol., 146, 173–188, https://doi.org/10.1016/j.agrformet.2007.05.014, 2007. 
Bisht, G. and Bras, R. L.: Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study, Remote Sens. Environ., 114, 1522–1534, https://doi.org/10.1016/j.rse.2010.02.007, 2010. 
Borbas, E. and Menzel, P.: MODIS Atmosphere L2 Atmosphere Profile Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], USA, https://doi.org/10.5067/MODIS/MOD07_L2.006, 2017. 
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, 1996. 
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Short summary
This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
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