In semi-arid savannas, the availability of surface water constrains movements and space-use of wild animals. To accurately model their movements in relation to water selection at a landscape scale, innovative methods have to be developed to i) better discriminate water bodies in space while characterizing their seasonal occurrences and ii) integrate this information in a spatially-explicit model to simulate animal movements according to surface water availability. In this study, we propose to combine satellite remote sensing (SRS) and spatial modelling in the case of the African buffalo (Syncerus caffer caffer) movements at the periphery of Hwange National Park (Zimbabwe). An existing classification method of satellite Sentinel-2 time-series images has been adapted to produce monthly surface water maps at 10 meters spatial resolution. The resulting water maps have then been integrated into a spatialized mechanistic movement model based on a collective motion of self-propelled individuals to simulate buffalo movements in response to surface water. The use of spectral indices derived from Sentinel-2 in combination with the short-wave infrared (SWIR) band in a Random Forest (RF) classifier provided robust results with a mean Kappa index, over the time series, of 0.87 (max = 0.98, min = 0.65). The results highlighted strong space and time variabilities of water availability in the study area. The mechanistic movement model showed a positive and significant correlation between observations/simulations movements and space-use of buffalo's herds (Spearman r = 0.69, p-value < 10 e-114) despite overestimating the presence of buffalo individuals at proximity of the surface water.