  Accurate soil moisture information is crucial for developing precise
irrigation control strategies to enhance water use efficiency. Soil moisture
estimation based on limited soil moisture sensors is crucial for obtaining
comprehensive soil moisture information when dealing with large-scale
agricultural fields. The major challenge in soil moisture estimation lies in
the high dimensionality of the spatially discretized agro-hydrological models.
In this work, we propose a performance-triggered adaptive model reduction
approach to address this challenge. The proposed approach employs a
trajectory-based unsupervised machine learning technique, and a prediction
performance-based triggering scheme is designed to govern model updates
adaptively in a way such that the prediction error between the reduced model
and the original model over a prediction horizon is maintained below a
predetermined threshold. An adaptive extended Kalman filter (EKF) is designed
based on the reduced model for soil moisture estimation. The applicability and
performance of the proposed approach are evaluated extensively through the
application to a simulated large-scale agricultural field.
