Detecting Shallow Groundwater From Spaceborne Soil Moisture Observations

In order to accurately estimate surface fluxes, groundwater levels need to be specified accurately. However, monitoring Groundwater (GW) is challenging. The main goal of this study is to develop an innovative, yet simple, approach to detect shallow GW based solely on spaceborne soil moisture observations and apply this approach to improve the lower boundary conditions of soil moisture models. We simulated the influence of shallow GW on surface soil moisture under various environmental conditions and the results showed that the mean soil moisture and its Coefficient of Variation (CV) are both sensitive to shallow GW. Using those observations, we developed an algorithm that uses empirical functions calculating thresholds of mean soil moisture and CV to detect occurrence of shallow GW. We tested this algorithm in five different locations representing various climatic regions across the US, Argentina, and Australia. The algorithm performed well by correctly identifying periods of GW influence 92.4% of the time. We confirmed the algorithm by applying it over two regions: The Pampas, Argentina, and Southwestern Australia. The results indicate that our SMAP-based algorithm successfully detects the shallow groundwater influenced time periods and significantly improves one particular soil moisture model output when the lower boundary conditions are adjusted according to the detection of shallow groundwater. The SMAP-based method presented here addresses the growing need for observation-based approaches to detect shallow groundwater at regional to global scales that may be beneficial to improving land surface and Earth system models.