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 overprediction 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

Item Type:	Refereed Article
Keywords:	cosmic-ray sensor, capacitance probe, supervised machine learning, ANFIS, soil moisture map, soil water monitoring
Research Division:	Environmental Sciences
Research Group:	Soil sciences
Research Field:	Soil sciences not elsewhere classified
Objective Division:	Environmental Management
Objective Group:	Terrestrial systems and management
Objective Field:	Terrestrial systems and management not elsewhere classified
UTAS Author:	Almeida, AC (Dr Auro Almeida)
UTAS Author:	Smethurst, PJ (Dr Philip Smethurst)
ID Code:	117337
Year Published:	2014
Web of Science® Times Cited:	4
Deposited By:	Geography and Spatial Science
Deposited On:	2017-06-07
Last Modified:	2017-10-19
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