Accurate crop-type mapping is essential for improving food security, promoting sustainable agricultural practices, and shaping agricultural policies. However, obtaining the requisite landscape scale data for crop type mapping is resource-intensive, particularly in resource-limited countries and expansive geographical areas. This study presents an innovative spatial explicit methodological framework for crop-type mapping in the heterogeneous landscape of the Western Cape Province, South Africa. The approach integrates unsupervised learning and spectral matching techniques. The Learning Vector Quantization (LVQ) algorithm was employed to cluster Sentinel-2 Multispectral Instrument (MSI) imagery acquired between April 2021 and March 2022 into distinct monthly crop clusters. Subsequently, spectral matching techniques were applied to assign accurate crop types to these clusters. The LVQ algorithm revealed the dynamic spatiotemporal variations in cluster counts that mirror the region’s agricultural diversity and climate patterns. The spectral matching exhibited varying effectiveness in associating crops with clusters. May (Overall Accuracy (OA) = 0.84, p = 0.01) had the highest accuracy, due to differing crop growth stages and canopy cover conditions during this period. Despite limitations, including the medium spectral resolution of Sentinel-2 MSI, these results advance cost-effective agricultural monitoring and land management practices. The results establish a robust baseline for comprehensive crop-type mapping contributing to informed agricultural decision-making. In addition, these results underscore the transformative potential of remote sensing and machine learning in enhancing crop management precision and sustainability. The implications of this study extend beyond food security and address temporal gaps in conventional crop surveys.