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Groundwater is the most significant freshwater source and plays a critical role in the earth&#x0027;s water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5&#x0025;. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA&#x0027;s SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19&#x0025; of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015&#x2013;2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2&#x0025; to 6&#x0025; of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies.

Global Shallow Groundwater Patterns From Soil Moisture Satellite Retrievals