From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture

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dc.creatorGuo, Zhe
dc.date2024-12-16
dc.date2025-01-02T15:13:31Z
dc.date2025-01-02T15:13:31Z
dc.date.accessioned2026-06-27T15:26:50Z
dc.descriptionThis 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.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/168470
dc.identifier.urihttp://hdl.handle.net/123456789/104643
dc.languageen
dc.publisherCGIAR System Organization
dc.rightsOpen Access
dc.sourceGuo, Zhe. 2024. From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture. Low-Emission Food Systems Initiative Brief. CGIAR System Organization. https://hdl.handle.net/10568/168470
dc.subjectcrops
dc.subjectcartography
dc.subjectsmallholders
dc.subjectagriculture
dc.subjectsatellite imagery
dc.subjectdata
dc.subjectecosystem management
dc.titleFrom space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture
dc.typeBrief

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)