DTW-kNN and Sentinel-2 NDVI Time Series for Field-Scale Maize Identification and Multi-Crop Classification in an African Smallholder Landscape

Accurate crop mapping provides spatially explicit information for agricultural planning, monitoring and resource allocation, while supporting agribusiness decisions related to supply chains and risk management. However, mapping crops in smallholder landscapes remains difficult because fields are often fragmented, small, and irregularly shaped, with varying planting dates. This study evaluated Sentinel-2 Normalised Difference Vegetation Index (NDVI) time series and Dynamic Time Warping (DTW) for maize identification and multi-crop field-boundary classification in a smallholder farming area. A maize reference trajectory was developed from known maize fields and tested using an independent set of maize fields. The maize reference fields showed low DTW distances (0.106 - 0.281) to the reference trajectory, while independent validation fields produced comparable distances (0.074 - 0.325), indicating that maize followed a recognisable NDVI trajectory despite variable planting dates. DTW distances (0.151 to 0.565) of candidate fields suggested that some fields had maize-like phenological behaviour while others were less similar to the maize reference. Days After Planting (DAP) alignment produced a clear maize curve, with NDVI increasing after planting, peaking between 90 and 120 DAP, and later declining. The approach was then extended to multi-crop classification using maize, soybean, potato and tea reference trajectories. The multi-reference DTW classifier achieved an overall validation accuracy of 80.0%, with maize and tea classified most reliably. When applied to 128 unlabelled fields, most were predicted as maize, although DTW confidence varied. The findings show that Sentinel-2 NDVI time series and DTW provide an interpretable and data-efficient approach for maize detection and field-level crop classification in smallholder systems.