| Summary: | Accurate crop planting date estimates are required for understanding agricultural seasonality, forecasting yields, planning input distribution, and developing climate resilient interventions. Particularly in smallholder-dominated environments in Africa and Asia, standard field-based methods for recording planting dates are often unreliable, labour-intensive, and spatially confined. This study uses multitemporal satellite data, vegetation index information, and smoothing algorithms to extract planting dates across Kenya and Rwanda utilising the automated remote-sensing workflow of the AgWise platform. Applied over a two-decade period (2002-2023), the approach revealed strong intra- and inter-annual trends in maize planting behaviour, capturing both stable seasonal windows and year-to-year fluctuations across diverse agricultural landscapes. The workflow ensures objective and reproducible efficient monitoring over wide geographic areas, as well as annual trend studies that span decades. This automation not only eliminates human labour and subjectivity, but it also allows for near-real-time insights necessary for policy planning, early warning systems, and adaptive crop management.
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