Impact of sensor placement in soil water estimation: a real-case study.

One important problem in implementing a closed-loop irrigation system is the determination of the optimal locations to install the sensors, both vertically and horizontally, such that improved pressure head estimation can be obtained. In this work, we find the best locations of the sensors in terms of degree of observability in an actual field and investigate whether the optimal sensor placement can significantly improve the pressure head estimation performance in actual applications. We investigate the impact of sensor placement in pressure head estimation for an actual agricultural field in Lethbridge, Alberta, Canada. In an experiment on the studied field, 42 watermark sensors were installed at different depths to collect the soil water tension measurements. A three-dimensional agro-hydrological model with heterogeneous soil parameters of the studied field is set up. The modal degree of observability is applied to the three-dimensional system to determine the optimal sensor locations. The extended Kalman filter (EKF) is chosen as the data assimilation tool to estimate the pressure head of the studied field. Pressure head estimation results for different scenarios are obtained and analyzed to investigate the effects of sensor placement on the performance of pressure head estimation in the actual applications. Estimation results indicate that optimally placed sensors reduce the average root mean square error by about 30% compared to the case the sensor positions are selected randomly. [ABSTRACT FROM AUTHOR]