From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture
This project centers on in-season crop type mapping in Nandi County, Kenya, utilizing time-series Sentinel-2 imagery and supervised machine learning techniques. The objective is to produce accurate crop-type maps to support agricultural management activities such as yield estimation, acreage statist...
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| Formato: | Brief |
| Lenguaje: | Inglés |
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CGIAR System Organization
2024
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| Acceso en línea: | https://hdl.handle.net/10568/168470 |
| Sumario: | This project centers on in-season crop type mapping in Nandi County, Kenya, utilizing time-series Sentinel-2 imagery and supervised machine learning techniques. The objective is to produce accurate crop-type maps to support agricultural management activities such as yield estimation, acreage statistics, disaster damage assessment, and ecosystem evaluation. The approach leverages cloud-based computing, offering a customized and flexible solution that requires no prior knowledge of cloud infrastructure. |
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