This title appears in the Scientific Report :
2014
Potential of soil water content mapping using electromagnetic induction in a forested catchment
Potential of soil water content mapping using electromagnetic induction in a forested catchment
Knowledge of spatial and temporal soil water content (SWC) variation is one of the key elements in land and water management and supports the prediction of climate-relevant processes. Nevertheless, reliable field-scale SWC information remains difficult to obtain. A potential way to obtain informatio...
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Personal Name(s): | Altdorff, Daniel (Corresponding Author) |
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von Hebel, Christian / van der Kruk, Jan / Borchard, Nils / Bogena, Heye / Vereecken, Harry / Huisman, Johan Alexander | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Published in: | 2014 |
Imprint: |
2014
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Conference: | DGG 2014, Karlsruhe (Germany), 2014-03-10 - 2014-03-13 |
Document Type: |
Conference Presentation |
Research Program: |
Terrestrial Systems: From Observation to Prediction Modelling and Monitoring Terrestrial Systems: Methods and Technologies |
Publikationsportal JuSER |
Knowledge of spatial and temporal soil water content (SWC) variation is one of the key elements in land and water management and supports the prediction of climate-relevant processes. Nevertheless, reliable field-scale SWC information remains difficult to obtain. A potential way to obtain information about SWC variation is the indirect mapping of easily recordable physical variables, such as bulk electrical conductivity (ECa) measured with electromagnetic induction (EMI). However, ECa depends on a range of soil properties, including porosity, SWC, and pore water conductivity (w). Therefore, it is not straightforward to derive SWC from EMI data, and results often show complex and site-specific relationships. The aim of this study is to evaluate the accuracy of SWC measurements with EMI, and to understand how this accuracy is affected by spatial and temporal variability of other soil properties that also affect the EMI signal. To this end, four time-lapse EMI data sets were recorded within a period of one year with two integral investigation depths (80 cm and 160 cm) in a forested catchment with an area of ~ 27 ha. Independent information on SWC and porosity were provided by a wireless soil moisture sensor network with 110 measuring locations equipped with sensors at three depths (5, 20 and 50 cm). First, we analyzed the relationship between EMI-derived ECa and SWC. Next, we minimized the residuals of predicted vs. measured SWC from the corresponding survey days by using three different models: (i) linear model: SWC = b*ECa, (ii) quadratic model: SWC = b*ECa^1/2, and (iii) Archie based model that considers spatial variation in soil porosity and saturation. Linear regression between ECa and SWC yields an R2 of approximately 0.4 whereas the Archie-based models provided R² > 0.6 with an RMSE of ~4 vol %. However, all models required daily calibration, which limits the applicability of EMI for mapping and monitoring SWC in this particular catchment. Finally, we derived maps of w by assuming that the residuals between measured and estimated SWC using the Archie model are solely due to spatial variability of w. Interestingly, these maps were highly structured and showed similar repeating patterns in w for all survey days, although the mean w varied. This supports our notion that the accuracy of SWC mapping is limited by spatial and temporal variation in w. This study indicates that the accuracy of EMI to derive SWC may be limited at some test fields due to spatial and temporal variations in other soil properties besides SWC considerably affect ECa. |