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The deployment of coastal observatories motivates the development of acoustic inversion schemes able to characterize rapidly time-varying range-dependent environments. This paper develops feature models as parameterization schemes for the range-dependent temperature field, when the latter is mainly influenced by an identified oceanic feature, here thermal fronts. The feasibility of feature-oriented acoustic tomography (FOAT) is demonstrated in two cases of coastal thermal front known to occur regularly: the Ushant tidal front, France (48.5° N, 5° E), and the Cabo Frio coastal upwelling, Brazil (23° S, 42° W). Realistic scenarios simulated with regional circulation models provide typical environmental variations for testing the validity of the FOAT approach, with both global optimization and sequential filtering of the (synthetic) full-field acoustic data. Matched-field processing at multiple frequencies is used to reduce ambiguities between parameters and to achieve a good tradeoff between robustness and sensitivity. The proposed feature-model parameterization is shown to provide robust estimates of the 2-D temperature field even when the simulated environment presents smaller scale inhomogeneities. Moreover, the sequential filtering based on a random walk model of the thermal front parameters enables a stable tracking of typical temperature field variations along several days. This sequential approach is particularly convenient for continuous, long-term monitoring operated with bottom-moored ocean observatories.