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

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