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The comparison map profile (CMP) method compares two spatially explicit data sets (original images) at each point and through several spatial scales simultaneously. The CMP combines the moving window concept with similarity indices for quantitative or qualitative data to visualize and quantify outputs: Changes in mean similarity value and its variability through scales are reported on a profile, similarities between regions are estimated on monoscale maps, and their persistence through scales assessed on a mean multiscale map. The CMP method is first illustrated using two images with slight difference in the checkered pattern. Second, two sets of comparisons related to African vegetation are conducted using the CMP method. The first set deals with quantitative data of leaf area index (LA!): Remote-sensed LA! images extracted from the AVHRR-NVDI product are compared to simulated LA! output from a dynamic global vegetation model (DGVM) using the distance and the cross-correlational coefficient for quantitative comparison of values and structure patterns, respectively. The second set of images deals with qualitative data: the remote-sensed product of land cover type by 1GBP-MOD!S is compared to the DGVM classified LA! output into land cover types using the Kappa statistics as similarity index. Results show that taking spatial patterns into account using the CMP method decreases the mean correlation by 50%, and increases the distance by 50% as compared to the global pixel-to-pixel indices. Similarly, comparison of land cover maps costs only 35% of the global Kappa value. Equatorial gradients of vegetation from forests to grassland are the most persistent similar regions between both types of data sets. Potential limits and strengths of the CMP method are discussed. [ABSTRACT FROM AUTHOR]