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 natio...
| Autores principales: | , , , , , |
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| Formato: | Conference Paper |
| Lenguaje: | Inglés |
| Publicado: |
Institute of Electrical and Electronics Engineers
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/159465 |
| _version_ | 1855531151926493184 |
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| author | Li, Hui Guo, Zhe Di, Liping Guo, Liying Zhang, Chen Lin, Li |
| author_browse | Di, Liping Guo, Liying Guo, Zhe Li, Hui Lin, Li Zhang, Chen |
| author_facet | Li, Hui Guo, Zhe Di, Liping Guo, Liying Zhang, Chen Lin, Li |
| author_sort | Li, Hui |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Conference Paper |
| id | CGSpace159465 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Institute of Electrical and Electronics Engineers |
| publisherStr | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | CGSpace1594652025-03-13T16:04:16Z In-season crop-type mapping in Kenya using Sentinel-2 imagery Li, Hui Guo, Zhe Di, Liping Guo, Liying Zhang, Chen Lin, Li crops modelling agriculture markets machine learning yields 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. 2024-09-04 2024-11-08T19:42:02Z 2024-11-08T19:42:02Z Conference Paper https://hdl.handle.net/10568/159465 en Limited Access Institute of Electrical and Electronics Engineers Li, 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 |
| spellingShingle | crops modelling agriculture markets machine learning yields Li, Hui Guo, Zhe Di, Liping Guo, Liying Zhang, Chen Lin, Li In-season crop-type mapping in Kenya using Sentinel-2 imagery |
| title | In-season crop-type mapping in Kenya using Sentinel-2 imagery |
| title_full | In-season crop-type mapping in Kenya using Sentinel-2 imagery |
| title_fullStr | In-season crop-type mapping in Kenya using Sentinel-2 imagery |
| title_full_unstemmed | In-season crop-type mapping in Kenya using Sentinel-2 imagery |
| title_short | In-season crop-type mapping in Kenya using Sentinel-2 imagery |
| title_sort | in season crop type mapping in kenya using sentinel 2 imagery |
| topic | crops modelling agriculture markets machine learning yields |
| url | https://hdl.handle.net/10568/159465 |
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