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Land surface soil moisture (SM) plays a critical role in hydrological processes and terrestrial ecosystems in desertification areas. Passive microwave remote-sensing products such as the Soil Moisture Active Passive (SMAP) satellite have been shown to monitor surface soil water well. However, the coarse spatial resolution and lack of full coverage of these products greatly limit their application in areas undergoing desertification. In order to overcome these limitations, a combination of multiple machine learning methods, including multiple linear regression (MLR), support vector regression (SVR), artificial neural networks (ANNs), random forest (RF) and extreme gradient boosting (XGB), have been applied to downscale the 36 km SMAP SM products and produce higher-spatial-resolution SM data based on related surface variables, such as vegetation index and surface temperature. Desertification areas in northern China, which are sensitive to SM, were selected as the study area, and the downscaled SM with a resolution of 1 km on a daily scale from 2015 to 2020 was produced. The results showed a good performance compared with in situ observed SM data, with an average unbiased root mean square error value of 0.057 m3 m−3. In addition, their time series were consistent with precipitation and performed better than common gridded SM products. The data can be used to assess soil drought and provide a reference for reversing desertification in the study area. This dataset is freely available at https://doi.org/10.6084/m9.figshare.16430478.v6 (Rao et al., 2022).