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

Surface air temperature (T-a), 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 T-a under clear-sky conditions or with limited temporal and spatial coverage. In this study, an all-sky daily mean land T-a 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 T-a 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 R-2 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 T-a dataset from 2003 to 2009 and compared it with the China Land Data Assimilation System (CLDAS) dataset at a 0.0625 degrees spatial resolution, the China Meteorological Forcing Data (CMFD) dataset at a 0.1 degrees spatial resolution, and the GLDAS dataset at a 0.25 degrees spatial resolution. Validation accuracy of our product in 2010 was significantly better than other datasets, with R-2 and RMSE values of 0.992 and 1.010 K, respectively. In summary, the developed all-sky daily mean land T-a 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).