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@ARTICLE{Tazifor:908112,
      author       = {Tazifor, Martial and Zimmermann, Egon and Huisman, Johan
                      Alexander and Dick, Markus and Mester, Achim and van Waasen,
                      Stefan},
      title        = {{M}odel-{B}ased {C}orrection of {T}emperature-{D}ependent
                      {M}easurement {E}rrors in {F}requency {D}omain
                      {E}lectromagnetic {I}nduction ({FDEMI}) {S}ystems},
      journal      = {Sensors},
      volume       = {22},
      number       = {10},
      issn         = {1424-8220},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2022-02382},
      pages        = {3882 -},
      year         = {2022},
      abstract     = {Data measured using electromagnetic induction (EMI) systems
                      are known to be susceptible to measurement influences
                      associated with time-varying external ambient factors.
                      Temperature variation is one of the most prominent factors
                      causing drift in EMI data, leading to non-reproducible
                      measurement results. Typical approaches to mitigate drift
                      effects in EMI instruments rely on a temperature drift
                      calibration, where the instrument is heated up to specific
                      temperatures in a controlled environment and the observed
                      drift is determined to derive a static thermal apparent
                      electrical conductivity (ECa) drift correction. In this
                      study, a novel correction method is presented that models
                      the dynamic characteristics of drift using a low-pass filter
                      (LPF) and uses it for correction. The method is developed
                      and tested using a customized EMI device with an intercoil
                      spacing of 1.2 m, optimized for low drift and equipped with
                      ten temperature sensors that simultaneously measure the
                      internal ambient temperature across the device. The device
                      is used to perform outdoor calibration measurements over a
                      period of 16 days for a wide range of temperatures. The
                      measured temperature-dependent ECa drift of the system
                      without corrections is approximately 2.27 mSm−1K−1, with
                      a standard deviation (std) of only 30 μSm−1K−1 for a
                      temperature variation of around 30 K. The use of the novel
                      correction method reduces the overall root mean square error
                      (RMSE) for all datasets from 15.7 mSm−1 to a value of only
                      0.48 mSm−1. In comparison, a method using a purely static
                      characterization of drift could only reduce the error to an
                      RMSE of 1.97 mSm−1. The results show that modeling the
                      dynamic thermal characteristics of the drift helps to
                      improve the accuracy by a factor of four compared to a
                      purely static characterization. It is concluded that the
                      modeling of the dynamic thermal characteristics of EMI
                      systems is relevant for improved drift correction.},
      cin          = {ZEA-2 / IBG-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)ZEA-2-20090406 / I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35632291},
      UT           = {WOS:000802486100001},
      doi          = {10.3390/s22103882},
      url          = {https://juser.fz-juelich.de/record/908112},
}