Annual meteorological variations and the impact of climate change in recent years impacted on agricultural production and distribution. Since wheat is a main food resource and the most widely grown crop in the world, it is essential to ensure the sustainability of its production. Therefore, accurate wheat mapping is essential for agricultural production forecasts. Multi-spectral satellite image analysis, including supervised machine learning (ML) methods, has been applied to wheat and other crop mapping, but such passive, optical imaging approaches are strongly influenced by weather conditions and cloud cover, whilst the supervised ML algorithms are highly reliant on manual labelling and ground control data. To avoid the limitation of weather and cloud, this research integrates Sentinel-1 Synthetic Aperture Radar (SAR) data with Sentinel-2 multi-spectral image products to achieve more reliable and accurate winter wheat mapping. Normalised Difference Vegetation Index (NDVI) retrieved from multi-spectral imagery and Sentinel-1’s dual-polarisation radar signals, VV, VH, acquired in different time series, are used as key inputs to an unsupervised ML model based on Dynamic Time Warping (DTW) and hierarchical clustering to prevent time-consuming manual labelling. The chosen study area lies in Norfolk, UK. The result of winter wheat classification with NDVI time series data in this study reaches 72% accuracy, but the improved classification integrating NDVI, VH, VV and VH/VV values achieves 86% accuracy. Future research will focus on optimizing the ML model with multi data integration in addition additional research sites will test more complicated scenarios and multiple crop classification.