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

TL;DR

This study evaluates how sensor placement affects soil moisture estimation accuracy in a real agricultural field using a 3D model, observability analysis, and data assimilation with EKF, demonstrating the importance of optimal sensor locations.

Contribution

It applies the modal degree of observability to determine optimal sensor placement in a real field, linking theoretical analysis with practical soil moisture estimation improvements.

Findings

Optimal sensor placement improves soil moisture estimation accuracy.

Sensor placement significantly impacts data assimilation performance.

The approach integrates observability analysis with real-world field data.

Abstract

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.