As global land cover/ land use change (LULCC) threatens the human’s well-being, accurate detection and characterization of LULCC is of paramount importance. The increasing availability of dense satellite image time series (SITS), together with the ever-improving change detection algorithms, has allowed significant progress to be made. However, much remains to be done in its characterization. This study aims to uncover potential relationships between changes in Normalized Difference Vegetation Index (NDVI) SITS patterns and their drivers. It distinguishes itself by representing phenological changes not only as transitions between specific patterns, but also by examining the nature of these changes—whether abrupt, gradual, or seasonal. For seasonal changes, it further refines the analysis to determine their impact on the amplitude, number of seasons (NOS), or length of seasons (LOS) components. Our focus is to provide insights into the land dynamics and drivers of change in Senegal using an RGB (red, green, blue) composite change map. This map is derived from three MODIS NDVI time series change metrics detected by BFASTm-L2 within the MODIS NDVI 2000–2021 SITS: magnitude of change, direction of change, and dissimilarity of time series shape. The 250-meter resolution MODIS data served as an optimal data source for this analysis due to its high temporal resolution (near daily) and extensive coverage over 20 years. The sensitivity of each metric to different types of change was first tested on a simulated dataset before being applied to the MODIS SITS. The RGB change map enabled visualization of different “signatures” of change, which, combined with ground information, rainfall data, NDVI time series analysis, and Google Earth imagery, helped link these signatures to various drivers of change. Climatic and anthropogenic changes, such as those induced by Large Scale Agricultural Investments (LSAI) or mining, were visually inferred from the RGB map.This approach demonstrates the usefulness of integrating the type of change, especially seasonal change, into the characterization of land change. This method has the advantage of being fast, interpretable, robust to noise and easily transferable to different regions.