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The importance of accurate soil moisture data for the development of modernclosed-loop irrigation systems cannot be overstated. Due to the diversity ofsoil, it is difficult to obtain an accurate model for agro-hydrological system.In this study, soil moisture estimation in 1D agro-hydrological systems withmodel mismatch is the focus. To address the problem of model mismatch, anonlinear state-space model derived from the Richards equation is utilized,along with additive unknown inputs. The determination of the number of sensorsrequired is achieved through sensitivity analysis and the orthogonalizationprojection method. To estimate states and unknown inputs in real-time, arecursive expectation maximization (EM) algorithm derived from the conventionalEM algorithm is employed. During the E-step, the extended Kalman filter (EKF)is used to compute states and covariance in the recursive Q-function, while inthe M-step, unknown inputs are updated by locally maximizing the recursiveQ-function. The estimation performance is evaluated using comprehensivesimulations. Through this method, accurate soil moisture estimation can beobtained, even in the presence of model mismatch.

State estimation for one-dimensional agro-hydrological processes with model mismatch