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| Titel |
3D soil water nowcasting using electromagnetic conductivity imaging and the
ensemble Kalman filter |
| VerfasserIn |
Jingyi Huang, Alex McBratney, Budiman Minasny, John Triantafilis |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250138605
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| Publikation (Nr.) |
 EGU/EGU2017-1673.pdf |
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| Zusammenfassung |
| Mapping and immediate forecasting of soil water content (θ) and its movement can be
challenging. Although apparent electrical conductivity (ECa) measured by electromagnetic
induction has been used, it is difficult to apply it along a transect or across a field. Across
a 3.95-ha field with varying soil texture, an ensemble Kalman filter (EnFK) was
used to monitor and nowcast θ dynamics in 2-d and 3-d over 16 days. The EnKF
combined a physical model fitted with θ measured by soil moisture sensors and an
Artificial Neural Network model comprising estimate of true electrical conductivity
(σ) generated by inversions of DUALEM-421S ECa data. Results showed that
the spatio-temporal variation in θ can be successfully modelled using the EnKF
(Lin’s concordance = 0.89). Soil water dried fast at the beginning of the irrigation
and decreased with time and soil depth, which were consistent with the classical
soil drying theory and experiments. It was also found that the soil dried fast in the
loamy and duplex soils across the field, which was attributable to deep drainage and
preferential flows. It was concluded that the EnKF approach can be used to better
the irrigation practice so that variation in irrigation is minimised and irrigation
efficiency is improved by applying variable rates of irrigation across the field. In
addition, soil water status can be nowcasted using this method with weather forecast
information, which will provide guidance to farmers for real-time irrigation management. |
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