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.

翻译：准确的土壤湿度信息对于制定精准灌溉控制策略、提高水分利用效率至关重要。基于有限土壤湿度传感器进行土壤湿度估计，是获取大规模农田综合土壤湿度信息的关键。土壤湿度估计的主要挑战在于空间离散化农业水文模型的高维特性。本文提出一种性能触发的自适应模型降维方法以应对该挑战。该方法采用基于轨迹的无监督机器学习技术，并设计了一种基于预测性能的触发机制，以自适应控制模型更新方式，确保在预测时域内降维模型与原模型之间的预测误差维持在预设阈值以下。基于该降维模型设计了自适应扩展卡尔曼滤波器用于土壤湿度估计。通过在大规模模拟农田中的应用，对所提方法的适用性和性能进行了全面评估。