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Mapping soil water dynamics and a moving wetting front by spatiotemporal inversion of electromagnetic induction data
Author(s) -
Huang J.,
Monteiro Santos F. A.,
Triantafilis J.
Publication year - 2016
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2016wr019330
Subject(s) - inversion (geology) , transect , soil science , electromagnetic induction , water content , electrical resistivity and conductivity , synthetic data , conductivity , inverse transform sampling , environmental science , remote sensing , geology , algorithm , mathematics , meteorology , geotechnical engineering , geomorphology , chemistry , engineering , geography , oceanography , structural basin , aerosol , electrical engineering , electromagnetic coil
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 (EC a ) measured by electromagnetic induction has been used as a proxy. A number of previous studies have employed inversion algorithms to convert EC a 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 EC a . The algorithm was applied to a case study where time‐lapse EC a 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 cm 3 /cm 3 ) and less biased (ME = −0.0023 cm 3 /cm 3 ) as compared with the nonspatiotemporal inversion approach (0.0483 cm 3 /cm 3 and ME = −0.0030 cm 3 /cm 3 , respectively). In addition, the spatiotemporal inversion algorithm allows for a reduced set of EC a 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.