Abstract. Surface air temperature (Ta), as an important
climate variable, has been used in a wide range of fields such as ecology,
hydrology, climatology, epidemiology, and environmental science. However,
ground measurements are limited by poor spatial representation and
inconsistency, and reanalysis and meteorological forcing datasets suffer
from coarse spatial resolution and inaccuracy. Previous studies using
satellite data have mainly estimated Ta under clear-sky conditions or
with limited temporal and spatial coverage. In this study, an all-sky daily
mean land Ta product at a 1 km spatial resolution over mainland China for
2003–2019 has been generated mainly from the Moderate Resolution Imaging
Spectroradiometer (MODIS) products and the Global Land Data Assimilation
System (GLDAS) dataset. Three Ta estimation models based on random
forest were trained using ground measurements from 2384 stations for three
different clear-sky and cloudy-sky conditions. The random sample validation
results showed that the R2 and root-mean-square error (RMSE) values of the
three models ranged from 0.984 to 0.986 and from 1.342 to 1.440 K,
respectively. We examined the spatiotemporal patterns and land cover type
dependences of model accuracy. Two cross-validation (CV) strategies of
leave-time-out (LTO) CV and leave-location-out (LLO) CV were also used to
evaluate the models. Finally, we developed the all-sky Ta dataset from
2003 to 2009 and compared it with the China Land Data Assimilation System
(CLDAS) dataset at a 0.0625∘ spatial resolution, the China
Meteorological Forcing Data (CMFD) dataset at a 0.1∘ spatial
resolution, and the GLDAS dataset at a 0.25∘ spatial resolution.
Validation accuracy of our product in 2010 was significantly better than
other datasets, with R2 and RMSE values of 0.992 and 1.010 K,
respectively. In summary, the developed all-sky daily mean land Ta
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 the studies of climate change and the hydrological cycle.
This dataset is currently freely available at https://doi.org/10.5281/zenodo.4399453
(Chen et al., 2021b) and the University of Maryland
(http://glass.umd.edu/Ta_China/, last access: 24 August 2021). A sub-dataset
that covers Beijing generated from this dataset is also publicly available
at https://doi.org/10.5281/zenodo.4405123 (Chen et al., 2021a).