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

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dc.creatorLi, Hui
dc.creatorGuo, Zhe
dc.creatorDi, Liping
dc.creatorGuo, Liying
dc.creatorZhang, Chen
dc.creatorLin, Li
dc.date2024-09-04
dc.date2024-11-08T19:42:02Z
dc.date2024-11-08T19:42:02Z
dc.date.accessioned2026-06-27T15:26:03Z
dc.descriptionIn-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.
dc.identifierhttps://hdl.handle.net/10568/159465
dc.identifier.urihttp://hdl.handle.net/123456789/104248
dc.languageen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.rightsLimited Access
dc.sourceLi, Hui; Guo, Zhe; Di, Liping; Guo, Liying; Zhang, Chen; and Lin, Li. 2024. In-season crop-type mapping in Kenya using Sentinel-2 imagery. 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/Agro-Geoinformatics262780.2024.10660971
dc.subjectcrops
dc.subjectmodelling
dc.subjectagriculture
dc.subjectmarkets
dc.subjectmachine learning
dc.subjectyields
dc.titleIn-season crop-type mapping in Kenya using Sentinel-2 imagery
dc.typeConference Paper

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)