This application note shows the interest of the time series auto / cross covariance feature to mine periodic land use / land cover (LU/LC) in an agricultural landscape. A Bayesian point of view has been adopted to cope with the variability of the real-world data sets. The agricultural landscape is represented by a mosaic of plots, each of them holding a time series of annual LU/LC surveys. An agricultural district is seen as a sample drawn from a time series random process. For each LU, the autocovariance function of the random process is first calculated and its Fast Fourier Transform (FFT) performed. The peaks of the power spectrum determine the frequencies-or the corresponding periods-of this LU. In addition, the analysis of the crosscovariance between two LU/LC may exhibit the co-occurrences of these LU/LC at different lags and determine a return time. In the data mining of agricultural landscapes represented by a mosaic of agricultural plots, results on time series coming from annual LU/LC surveys are presented. They show that the auto / cross covariance coefficients and their spectral representation give immediate and accurate information to a data mining analyst on complex land use rotations and their trends for understanding the underlying logical process in landscape dynamics.