Résumé : The monitoring, assessment and prediction of dynamic processes in shallow water constitute an attractive challenge. The availability of targeted observations enable high-resolution ocean forecasting to develop the 4D environmental picture. In particular, range-resolving acoustic tomography data constitute an effective way to reduce the non-uniform distribution and sparsity of standard hydrographic observations. In this paper a Kalman filtering scheme is investigated for tracking the time variations of a range-dependent sound-speed field in a vertical slice of a shallow water environment from full-field acoustic data and a propagation model taking into account the acoustic properties of the seafloor and subseafloor. The basic measurement setup for each radial of a tomography system consists of a broadband, multifrequency sound source and a vertical receiver array spanning most of the water column. The state variables represent the main features of the sound-speed field in a low dimensional parameterization scheme using empirical orthogonal functions. To test the algorithm acoustic data are synthesized from ocean model predictions obtained in support of the MREA/BP07 experiment southeast of the island of Elba, Italy. Bottom geoacoustic parameters obtained from previous acoustic inversion experiments are input to a normal mode propagation model as a background dataset. Additional data such as sea-surface temperature data from satellite or in situ hydrographic observations provide a priori approximate information about the range dependency of the subsurface structure and an estimation of the sea-surface sound speed. The evolution of the entire sound-speed field in the vertical slice is then sequentially estimated by the inversion processor. The results show that the daily space and time variations of the simulated sound-speed field can be effectively tracked with an extended Kalman filter. The depth-integrated sound-speed error (RMS) remains lower than 0.3 m/s (0.09 °C) when the benchmark environment is completely determined in the parameter space and lower than 0.7 m/s (0.22 °C) for an approximate environment parameterization. © 2009 Elsevier B.V.