A fine-resolution soil moisture dataset for China in 2002–2018

Abstract. Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become means to quickly obtain soil across large areas, but the coarse spatial resolution of data imposes great limitations on application these data. We provide a unique dataset (0.05?, monthly) China from 2002 2018 based reconstruction model-based downscaling techniques using different passive products – including AMSR-E AMSR2 (Advanced Microwave Scanning Radiometer Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 SMOS-IC (Soil Moisture Ocean Salinity designed by Institut National de la Recherche Agronomique, INRA, Centre d’Etudes Spatiales BIOsphère, CESBIO) calibrated with consistent model in combination ground observation This new fine-resolution high overcomes multisource time matching problem between optical sources eliminates difference sensor errors. The validation analysis indicates that accuracy satisfactory (bias: ?0.057, ?0.063 ?0.027 m3 m?3; unbiased root mean square error (ubRMSE): 0.056, 0.036 0.048; correlation coefficient (R): 0.84, 0.85 0.89 monthly, seasonal annual scales, respectively). was used analyze spatiotemporal patterns water content 2018. In past 17 years, China's shown cyclical fluctuations slight downward trend can be summarized as wet south dry north, increases west decreases east. reconstructed widely significantly improve hydrologic serve input ecological other geophysical are published Zenodo at https://doi.org/10.5281/zenodo.4738556 (Meng et al., 2021a).