Combining cosmic-ray neutron and capacitance sensors and fuzzy inference to spatially quantify soil moisture distribution

Auro C. Almeida, Ritaban Dutta, Trenton E. Franz, Andrew Terhorst, Philip J. Smethurst, Craig Baillie, Dale Worledge

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

This paper combines data from soil moisture capacitance probes and a cosmic-ray neutron probe in a fuzzy inference system to estimate spatially variable soil moisture in a (∼ 28) ha circular area at an hourly interval in northeast Tasmania, Australia. The technique uses hourly counts of cosmic-ray neutrons, a network of 25 capacitance probes measuring soil moisture at half hourly intervals and at five depths (0-50 cm), and a multiple adaptive neuro-fuzzy inference system. We quantified soil moisture in the top portion of the soil during wet and dry periods for training and testing periods. After training, the technique provided reliable estimates of temporal pattern of soil moisture at 10- and 20-cm depths during a wet period using input data only from the cosmic-ray neutron probe. There was over-prediction of soil moisture during a dry period, which suggests a longer training period representative of the full range of likely conditions might be required. Spatial maps of soil water content produced from the single cosmic-ray neutron probe were similar to those of the capacitance probe.

Original languageEnglish (US)
Article number6870440
Pages (from-to)3465-3472
Number of pages8
JournalIEEE Sensors Journal
Volume14
Issue number10
DOIs
StatePublished - Oct 2014

Keywords

  • ANFIS
  • Cosmic-ray sensor
  • capacitance probe
  • soil moisture map
  • soil water monitoring
  • supervised machine learning

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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