Increasing demand for water, food and energy has led to dramatic competition for land, resulting in a global land rush in the form of large scale agricultural investments (LSAI). Due to their many potential negative impacts and the opacity of their surroundings, accurate detection and characterisation of LSAIs in space and time is required. The increasing availability of dense satellite imagery time series (SITS), together with ever-improving change detection algorithms, is useful in this task. While SITS change detection algorithms are efficient at detecting abrupt and gradual changes phenological time series, there is still much room for improvement when it comes to detecting seasonal changes. The primary objective of this research was to automatically detect, in an unsupervised manner, the implementation of LSAIs in Senegal based on remote sensing data. This work is structured around three interrelated papers. The first presents a fast and unsupervised approach (BFASTm-L2) developed to detect, in full MODIS NDVI SITS at the pixel level, the breakpoint associated with the largest pattern (i.e. mostly seasonal) change of the time series. Compared to other change detection algorithms (BFAST Lite, EDYN and BFAST monitor), BFASTm-L2 proved to be particularly sensitive to seasonal changes and efficient in highlighting LSAIs in Senegal. This supports the hypothesis that changes induced by land use systems such as LSAIs are very often of a seasonal type. The second paper sought to differentiate the contribution of LSAIs from the main drivers of change (climatic, natural and anthropogenic) at a national-scale, relying mainly on three time series-based change metrics calculated at the pixel level (magnitude of change, direction of change, dissimilarity), which, when combined into a unique composite map, provided insights into land dynamics. LSAIs were shown to have a specific ecoregional signature of change. Finally, the third paper aims to refine the detection of the deal