A global soil moisture dataset with high spatio-temporal resolution from 2000 to 2019

Soil moisture plays a key role in the water and energy exchange between the land surface and the atmosphere, and widely used in agriculture, hydrology, and ecology, especially under climate change. Long-term series of soil moisture with high spatio-temporal resolution can provide sufficient information to understand the role of the climate change at global and regional scales. This paper presents the first global soil moisture dataset (2000–2019) with high spatio-temporal resolution (1 day, 0.05°) named GHSTSM, based on satellite-based optical (i.e. MODIS) and microwave (ECV) products using a machine learning method named general regression neural network (GRNN). The dataset itself reveals significant information on the soil moisture and its changes over the TP, and can aid to understand the potential driven mechanisms for global climate change.