Mapping soil water dynamics and a moving wetting front by spatiotemporal inversion of electromagnetic induction data

Characterization of the spatiotemporal distribution of soil volumetric water content (θ) is fundamental to agriculture, ecology, and earth science. Given the labor intensive and inefficient nature of determining θ, apparent electrical conductivity (ECa) measured by electromagnetic induction has been used as a proxy. A number of previous studies have employed inversion algorithms to convert ECa data to depth‐specific electrical conductivity (σ) which could then be correlated to soil θ and other soil properties. The purpose of this study was to develop a spatiotemporal inversion algorithm which accounts for the temporal continuity of ECa. The algorithm was applied to a case study where time‐lapse ECa was collected on a 350 m transect on seven different days on an alfalfa farm in the USA. Results showed that the approach was able to map the location of moving wetting front along the transect. Results also showed that the spatiotemporal inversion algorithm was more precise (RMSE = 0.0457 cm3/cm3) and less biased (ME = −0.0023 cm3/cm3) as compared with the nonspatiotemporal inversion approach (0.0483 cm3/cm3 and ME = −0.0030 cm3/cm3, respectively). In addition, the spatiotemporal inversion algorithm allows for a reduced set of ECa surveys to be used when non abrupt changes of soil water content occur with time. To apply this spatiotemporal inversion algorithm beyond low induction number condition, full solution of the EM induction phenomena can be studied in the future. Key Points A spatiotemporal electromagnetic inversion algorithm was used to map soil water dynamics and a moving wetting front Spatiotemporal inversion was more robust than nonspatiotemporal inversion Spatiotemporal inversion algorithm allows for a reduction in electromagnetic induction survey density