The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat over a study site located in southwestern France. The methodology is based on the use of Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the NDVI (Normalized Difference Vegetation Index) is combined with the previous yield maps, the agricultural season 2014 showing the lower level of performances with a R² of 0.44 and a RMSE (Root Mean Square Error) of 8.13 q.h⁻¹ (corresponding to a relative error of 12.9%), the three other years being associated with values of R² close or upper of 0.60 and RMSE lower than 7 q.h⁻¹ (corresponding to a relative error inferior to 11.3%).