Hydrological models are valuable tools for understanding the movement of water and contaminants in agricultural catchments. They are particularly useful for assessing the impact of landscape organization on pesticide transfers and for developing effective mitigation strategies. However, using these models in an operational context requires reducing uncertainties in their outputs, which can be achieved through data assimilation methods. In this study, we aim to integrate surface moisture images into the PESticide and Hydrology Modelling at the catchment scale (PESHMELBA) water and pesticide transfer model using data assimilation techniques. Twin experiments are conducted on a virtual catchment consisting of vineyard plots and vegetative filter strips. We compare the performance of the ensemble Kalman filter (EnKF), the ensemble smoother with multiple data assimilation (ES-MDA), and the iterative ensemble Kalman smoother (iEnKS) in jointly estimating vertical moisture profiles and van Genuchten soil water retention properties of all soil horizons. Results indicate that the ES-MDA performs the best in estimating surface moisture and saturated water contents, while all methods show similar results for subsurface moisture variables and parameters. Furthermore, we examine the sensitivity of the methods to observation error magnitude, observation frequency, and ensemble size to establish an effective assimilation setup. This study paves the way for future operational applications of data assimilation in PESHMELBA