  One of the essential elements in implementing a closed-loop irrigation system
is soil moisture estimation based on a limited number of available sensors. One
associated problem is the determination of the optimal locations to install the
sensors such that good soil moisture estimation can be obtained. In our
previous work, the modal degree of observability was employed to address the
problem of optimal sensor placement for soil moisture estimation of
agro-hydrological systems. It was demonstrated that the optimally placed
sensors can improve the soil moisture estimation performance. However, it is
unclear whether the optimal sensor placement can significantly improve the soil
moisture estimation performance in actual applications. In this work, we
investigate the impact of sensor placement in soil moisture estimation for an
actual agricultural field in Lethbridge, Alberta, Canada. In an experiment on
the studied field, 42 soil moisture sensors were installed at different depths
to collect the soil moisture measurements for one growing season. A
three-dimensional agro-hydrological model with heterogeneous soil parameters of
the studied field is developed. 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 soil moisture content of the studied field. Soil moisture
estimation results for different scenarios are obtained and analyzed to
investigate the effects of sensor placement on the performance of soil moisture
estimation in the actual applications.

Applications;Computers and Society;Systems and Control;Systems and Control;Optimization and Control