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Observability analysis for soil moisture estimation ? ? Natural Sciences and Engineering Research Council, Canada.

机译：土壤湿度估算的可观察性分析 ？ ？ 加拿大自然科学与工程研究委员会。 < / ce：脚注>

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摘要

The knowledge of soil moisture is important in studying climatology, earth science and most importantly irrigation decision support systems, but is often hard to determine since it is not possible to use critical measurements including moisture sensors all over the entire agricultural grid sector. As a result, soil moisture at unmeasured region needs to be estimated, which can be done using state estimators such as Kalman based estimators. The model that is used to represent water transfer between atmosphere, plant and soil, also known as agro-hydrological model, is highly nonlinear. Since ‘strong’ rather than ‘weak’ observability of the system ensures better performance of Kalman based estimators to develop a reliable soil moisture estimation algorithm, the main objective of this study is to discuss observability analysis of this nonlinear agro-hydrological system. The study was performed using synthetic data. The extended Kalman filter (EKF) was chosen as the state estimator. As would be expected, the results show that the EKF performance is better in cases where the system is ‘strongly’ observable.
机译：土壤湿度的知识在研究气候学，地球科学以及最重要的灌溉决策支持系统中很重要，但由于无法在整个农业电网领域使用包括湿度传感器在内的关键测量方法，因此常常很难确定。结果，需要估计未测量区域的土壤湿度，这可以使用状态估计器（例如基于Kalman的估计器）来完成。用于表示大气，植物和土壤之间的水转移的模型（也称为农业水文模型）是高度非线性的。由于系统的“强”而不是“弱”的可观测性确保了基于卡尔曼估算器的更好性能，从而开发了可靠的土壤水分估算算法，因此，本研究的主要目的是讨论该非线性农业水文系统的可观测性分析。该研究是使用综合数据进行的。选择扩展卡尔曼滤波器（EKF）作为状态估计器。可以预料，结果表明，在“强烈”观察到系统的情况下，EKF性能更好。

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