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The key component of a fuzzy Markov chain (FMC)-based multitemporal cascade classifier is the transition possibility matrix (TPM). Such matrix represents the temporal dynamics of the land use/land cover classes in the target site in a given time period. The choice of the TPM estimation approach is a crucial step in the design of FMC-based classifiers, as it strongly influences the final classification accuracy. Moreover, the task of collecting training data may involve considerable effort, since the number of transitions to be represented grows with the square of the number of classes in the application. In spite of their relevance, the TPM estimation has only been addressed superficially in previous publications about FCM-based classification methods. In this letter, we concern some of those aspects and investigate alternative ways of the TPM estimation. Experimental analysis on a multitemporal data set covering a 20-year period sheds light on the conditions under which those alternative estimation approaches may be used, as well as on their impact over the classification performance.