In-season crop-type mapping in Kenya using Sentinel-2 imagery

In-season crop type mapping is essential to agriculture management applications, including yield estimates, crop planting acreage statistics, food market predictions, and land use change analysis that support relevant decision-making, pushing economic development in certain agricultural export nations like Keny. This study employed a supervised machine learning method to produce three Kenya counties’ in-season crop-type maps in September 2023. We used surveyed growing crop ground truth data at the end of August 2023 and European Space Agency (ESA) WorldCover data serving as training labels, including nine crop types (Maize, Coffee, Grassland, Tea, Sugarcane, Exotic tree, Legumes, Vegetable, Native tree). The 15-day composite Sentinel-2 time series data was generated, incorporating training labels to assemble into training samples. They engaged in training a random forest classifier, conducting crop-type classifying in Nandi, Vihiga, and Kisumu Counties of Kenya. Moreover, the majority filter served to refine the classification. The validation results confirmed that grassland, sugarcane, tree, and tea possess high classification accuracy $(0.80-0.91)$, and coffee and maize showcase low accuracy (0.67 0.73) due to the massive mix pixels. This study attempted to produce in-season crop-type maps in an African nation with fragmented crop fields.