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Assessing soil water content variability through active heat distributed fiber optic temperature sensing

Author

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  • Zubelzu, Sergio
  • Rodriguez-Sinobas, Leonor
  • Saa-Requejo, Antonio
  • Benitez, Javier
  • Tarquis, Ana M.

Abstract

Soil spatial variability is a key point for the sustainable water management in agriculture. Fractal techniques provide proper tools to analyze soil spatial variability searching for statistical self-similarity patterns among different scales. Although they have been extensively applied to study the soil properties variability, its applicability for the soil water content (SWC) distribution is complicated because requires many data difficult to obtain with the typical point soil water sensors. Recently, a fiber optic distributed temperature sensor has been used to measure soil thermal properties which relate to SWC. These sensors provide large amount of data with high spatial and temporal resolution, thus filling the gap of point soil water sensors. In the present work, soil temperature was measured with a Distributed Temperature Sensing (DTS) and SWC was estimated by different fitting functions which have been studied with focus on spatial variability.

Suggested Citation

  • Zubelzu, Sergio & Rodriguez-Sinobas, Leonor & Saa-Requejo, Antonio & Benitez, Javier & Tarquis, Ana M., 2019. "Assessing soil water content variability through active heat distributed fiber optic temperature sensing," Agricultural Water Management, Elsevier, vol. 212(C), pages 193-202.
    • Handle: RePEc:eee:agiwat:v:212:y:2019:i:c:p:193-202
    DOI: 10.1016/j.agwat.2018.08.008
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    References listed on IDEAS

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    1. Dabach, Sharon & Shani, Uri & Lazarovitch, Naftali, 2015. "Optimal tensiometer placement for high-frequency subsurface drip irrigation management in heterogeneous soils," Agricultural Water Management, Elsevier, vol. 152(C), pages 91-98.
    2. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
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