Mapping of peanut crops is essential in supporting peanut production, yield prediction, and commodity forecasting. While ground-based surveys can be used over small areas, the development of remote-sensing technologies could provide rapid and inexpensive crop area estimates with high accuracy over large regions. Some of these recent earth observation satellite systems, such as the Project for On-Board Autonomy Vegetation (PROBA-V), have the advantage of increased spatial and temporal resolution. With a study area located in the South Burnett region, Queensland, Australia, the primary aim of this study was to assess the ability of time-series PROBA-V 100-m normalized difference vegetation index (NDVI) for peanut crop mapping. Two datasets, i.e., PROBA-V NDVI time-series imagery and the corresponding phenological parameters generated from TIMESAT data analysis technique, were classified using maximum likelihood classification, spectral angle mapper, and minimum distance classification algorithms. The results show that among all methods used, the application of MLC in PROBA-V NDVI time series produced very good overall accuracy, i.e., 92.75%, with producer and user accuracy of each class ≥78.79 % . For all algorithms tested, the mapping of peanut cropping areas produced satisfactory classification results, i.e., 75.95% to 100%. Our study confirmed that the use of finer resolution 100 m of PROBA-V imagery (i.e., relative to MODIS 250-m data) has contributed to the success of mapping peanut and other crops in the study area.